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Embedded Value-Added Service

The final blog in the embedded ecosystem series is about embedded value-added services and how they can build and maintain strong customer loyalty.

Read more May 15th, 2023

Embedded Business Management

This blog dives into embedded business management and its role in helping small businesses run their businesses when they don't have dedicated resources to help.

Read more May 15th, 2023

Embedded Lending

This blog looks at the evolution of embedded lending and how it has gained traction for both businesses and consumers needing access to more flexible finance options.

Read more May 15th, 2023

Embedded Deposits

This blog looks at embedded deposits and how non-bank financial services providers are meeting the demand of their customer base.

Read more May 15th, 2023

Embedded Payments

Liberis are experts in embedded finance but there is an entire embedded ecosystem out there! Over the next 5 weeks, we will be releasing a new blog each week that will dive into the full embedded ecosystem and the many different facets of it that businesses can avail of. First up is Embedded Payments where we will discuss its evolution, from traditional payment methods to online payments to embedded payments.

Read more May 15th, 2023

Embracing Diversity: A Deeper Dive into Pride Month

Unveiling the significance of Pride Month, this blog explores the roots of LGBTQIA+ rights, the issue of Rainbow washing, and the importance of fostering inclusivity through understanding and proper language use.

Read more May 15th, 2023

A Day in the Life of an Engineering Manager with Kirsty Luke

Kirsty Luke, Engineering Manager at Liberis shares what a 'day in the life' looks like.

Read more May 15th, 2023

4-Click Funding: How Acquirers Are Offering Instant Funding Without Putting Their Balance Sheet at Risk

Acquirers in the payments industry are partnering with embedded lending platforms to offer instant funding to SMEs, enabling them to access tailored financing solutions and improve cash flow management, while the acquirers differentiate themselves and mitigate risk through a streamlined 4-click process.

Read more May 15th, 2023

Why PSPs Should Partner with Embedded Finance Platforms for Tailored Financing Solutions

PSPs should partner with embedded finance platforms to offer tailored financing solutions that benefit both PSPs and merchants by providing frictionless access to finance at the point of need.

Read more May 15th, 2023

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Embedded Finance

Embedded Lending: Neobanks can Offer 4-Click Instant Funding Without Putting their Balance Sheet at Risk

There’s a portmanteau that perfectly describes the challenger banks that emerged from the ashes of the 2008 financial crisis: the fusion of neo and banks to create neobanks. The use of neo – another word for new – underscores their determination to disrupt the traditional banking model using the latest technology. These innovative providers of financial services constantly strive to make their offering more streamlined, frictionless, and accessible – and the emergence of embedded lending aligns perfectly with this forward-thinking philosophy.  

Embedded lending – a subset of embedded finance – is empowering small and medium-sized enterprises (SMEs) to break down entrenched funding barriers. By leveraging alternative funding options that untether them from traditional lenders, SMEs can achieve financial independence – from making payroll on time, to buying stock for seasonal periods, to settling VAT bills. And it’s neobanks – working in partnership with third-party embedded lending vendors – that are providing them with a platform to access this vital finance at the point of need. 

So, what is embedded lending? How can it help neobanks offer sustainable instant funding? And what are the benefits of 4-click instant funding for SMEs? 

What is embedded lending?

A thirst among SMEs for frictionless, digital-first lending experiences that break the monopoly held by traditional lenders has been quenched by embedded lending. This process of integrating credit or financing products into non-financial businesses – or in this case neobanks – allows customers to access finance when they need it from a brand they trust. Crucially, the funding gap faced by businesses currently stands at a staggering $5 trillion. With the average bank approval rate for loans being only 30%, it makes it difficult for many small businesses to secure the necessary funding they need. To add to this challenge, the average time it takes for a bank to approve a loan application is between 3 to 8 weeks. Furthermore, only a few banks offer unsecured loans exceeding £25,000, which further narrows the financing options for businesses. These factors make it increasingly difficult for businesses to obtain the funds they need to grow and thrive, especially in today’s uncertain economic climate. 

By harnessing a customisable API or white-label solution, neobanks can seamlessly integrate frictionless embedded lending options into their technology ecosystem; before tailoring them to meet SMEs’ needs – and the benefits are compelling: 

  • The entire lending process becomes faster, simpler, and frictionless. 
  • SMEs can focus on using the funds, rather than applying for them. 
  • It facilitates the point of need access to capital that SMEs crave, improving their cash flow management.  

By working in partnership with alternative lending providers like Liberis, neobanks benefit from offering SMEs instant funding without putting their balance sheet at risk – while remaining cash rich.  

Neobanks: lending challenges

Most neobanks claim they exist to ‘rip up’ the banking rule book, but there’s one sphere of financial services they have struggled to disrupt until now: lending. Rather than reoiling the lending model to make it a frictionless experience, they have been encumbered by regulatory challenges. For example, neobanks that have failed to obtain a full banking licence have been forced to run on an e-money licence model that does not permit them to lend out customer deposits, one of the key money makers for a bank.  

Neobanks have also struggled with challenges surrounding credit risk. Their failure to augment the lending application and assessment processes with impactful automation leaves them reliant on outdated infrastructures that fail to flag high-risk customers that might default on their repayment obligations. Not to mention narrow credit scores, which typically focus on a borrower’s past financial information, leaving lending decisions at the mercy of dated factors like payment history and outstanding debt – rather than their suitability to repay the loan in the future. 

Embedded lending: instant funding without the risk

By inserting alternative lending experiences into the moment the customer identifies a funding requirement, neobanks can shield themselves from the associated risk to their balance sheet. For example, revenue-based finance is an example of B2B embedded lending that allows SMEs to access funding based on their overall business revenue – not just their credit history.  

By hinging creditworthiness decisions on real-time assessment of the current and – crucially – the future business situation – including sales, inventory, and reputation – the likelihood of repayment is based on the borrower’s suitability to repay it thereafter. Using this flexible approach, repayments can be made in line with the businesses cashflow – with more repaid during good months from a performance perspective.  

This foresight removes the risk associated with traditional loan applications and assessment models, which rely on the number of transactions and payments made in the past – historic data that fails to horizon scan an SMEs future creditworthiness. 

With the risk to their balance sheet mitigated, neobanks can tap into the other compelling benefits that an embedded lending platform offers, including expert underwriting, real-time analysis of vast data sets, and finance monitoring. 

Partner with an expert

Success is not guaranteed when entering the lending space. That’s why most neobanks choose to outsource their embedded lending platform to the experts, rather than attempting to build it themselves – a trend that is being perpetuated by four key factors: 

  • Cost: A neobank is saddled with all the costs when conducting the project in-house. By partnering with a specialist third-party provider, extra revenue can be generated rapidly without the additional costs of building the lending platform from the ground up or the ongoing cost of developing software updates. 
  • Time to market: The development of embedded lending platforms requires significant investment in terms of time and resources – from building a technology platform to supporting customer onboarding. Specialist third-party vendors possess the skills and experience needed to deliver the project expeditiously. This ensures a quicker, more streamlined launch to market than building from scratch, enhancing customer satisfaction and retention. 
  • Underwriting expertise: Many third-party providers have an in-house team of underwriters with decades of experience, a resource that many neobanks lack. Instead of relying solely on digital approvals and declines based on credit scores, and because each individual business’ circumstances are different, they speak to the customer to get a full picture of their performance which helps to reduce the SME’s fears of rejection.  
  • Alignment of goals and objectives: By working with a vendor that aligns with its goals and objectives a neobank can harness their expertise to tailor the lending service they offer to SMEs. This will ensure it’s relevant to their customers’ needs, providing them with the holy grail: certainty of funding at the right time. 

This brings the vendor selection process into sharp focus for neobanks seeking to harness the power of embedded lending to mitigate the credit risk associated with instant funding. They must trust the vendor from a security, technical, reputational, and strategic perspective. Take the Liberis/Tide partnership for example: the neobank has partnered with Liberis to provide unparalleled access to debt and equity for SMEs. 

Benefits of 4-click instant funding for SMEs

Artificial intelligence can be leveraged to augment the embedded lending process, enabling a seamless and streamlined 4-click journey that has convenience, transparency, and personalisation at its core: 

  1. See the offer: The lending functionality is seamlessly embedded into the brand’s existing customer journey, enabling an automated pre-approved offer to be made. 
  2. Customise the offer: Real-time user experience optimisation customises the lending proposition depending on the brand’s offering and the customers’ requirements.  
  3. Confirm details: The applicant’s details are processed instantly, and an auto-approval decision is made followed by an auto-approved offer. 
  4. Sign the contract: The applicant accepts the offer immediately, gaining access to the funds almost instantly.

This white-label solution places the customer experience at the forefront of the lending journey via a neobank brand they already know and trust.  

This smooth application process democratises instant funding for SMEs, which benefit from point-of-need access to capital and improved cashflow management; while the neobanks that embed it into their ecosystem improve brand loyalty and increase revenue through an improved user experience. 

Impact of embedded lending on the lending industry

SMEs face an all-too-common existential threat: limited access to responsible and sustainable finance. This unwanted scenario is largely a symptom of legacy providers’ rigid lending frameworks that perpetuate erroneous assumptions that SMEs are too risky to engage with – depriving them of the funds they need to survive and thrive. 

According to our 2022 survey commissioned with YouGov, 15% of SMEs say rejection is one of their biggest funding concerns. Embedded lending has the power to address this anxiety by facilitating financing without friction. To say it’s well-placed to go from strength to strength is an understatement: research by Bain Capital estimated that by 2021 around $12 billion in B2B loan transactions were made via embedded finance, which it expects to increase exponentially to between $50 billion and $75 billion by 2026. 


Once obstructed by legacy providers’ antiquated systems and processes, SME funding applications have been given the 21st-century treatment thanks to the emergence of embedded lending – and its widespread adoption by neobanks. With rejections replaced by rubber stamps, SMEs can gain instant access to the funding that’s their lifeblood.  

Core to this financial inclusion are the frictionless experiences that embedded lending facilitates. Reinforced by these alternative lending models, neobanks can offer 4-click instant funding safe in the knowledge that each applicant’s future financial posture has been considered – shielding their balance sheet from credit risk.  

Posted on Notice: Undefined variable: date_format in /code/wp-content/themes/liberis/archive.php on line 374 April 3, 2023 By Kieran Darmody
Embedded Finance

The Role of AI in Detecting and Preventing Financial Fraud in Embedded Finance

Cybercriminals haven’t created the world’s biggest criminal growth industry by resting on their laurels – it’s estimated that global cybercrime cost will reach $10.5tr annually by 2025, up from $3tr in 2015. These persistent individuals understand that their nefarious online activities must evolve to achieve their goal: to deceive intended targets for financial gain while evading the clutches of the authorities. 

Their determination to develop innovative attacks to fit new trends has brought one of the fastest-growing areas of fintech into their line of sight: embedded finance. This seamless integration of banking-like services into a non-financial customer journey ticks all the boxes for cybercriminals: it’s a new financial service that lacks regulatory control; it’s growing at an exponential rate; and its inherent convenience makes it easy to move money around. 

Embedded finance platforms that treat cybersecurity as an afterthought leave gaps in their defences for cybercriminals to exploit – exposing businesses to reputational damage and financial loss. Rather than giving them a head start, fintech providers must embed robust fraud detection and prevention capabilities into the customer journey – not just convenience. 

The role of AI in detecting and preventing financial fraud

The cyber-attack surface is vast – and continuing to grow and evolve rapidly. Traditional fraud detection techniques barely dent the mindboggling amount of data that needs analysing to prevent cyber-attacks – allowing them to go undetected. These manual and time-intensive processes heighten vulnerability and delay remediation activities – making it almost impossible to manage cybercrime proactively. 

How do financial service providers overcome the mountains of data that stand between them and the perpetrators? Two words: artificial intelligence (AI). AI-powered fraud detection and prevention tools conduct real-time analysis of broad and diverse customer data sets and present their findings expeditiously. 

This ability to monitor vast swaths of transactional data allows providers to identify nuanced trends that can be used to detect fraud in real-time. When fraud is detected, AI models can automatically reject transactions or flag and rate them for further investigation – and the benefits are compelling: 

  • Speed: AI continuously processes and analyses new data and can assess countless transactions in real-time.  
  • Scale: The efficiency of fraud-based AI algorithms improves as data sets grow. Data helps AI to distinguish between different transactional behaviours and determine which are legitimate and which are fraudulent. 
  • Efficiency: Unlike people, AI algorithms love repetitive tasks. They are built to handle the data analysis heavy lifting, and only involve people in decision-making when they have spotted subtle or counterintuitive patterns in transactions.  

The use of AI in real-world fraud detection and prevention cases

Danske Bank

Almost all Danske Bank’s customer interactions take place digitally, providing a large attack surface for cybercriminals to target. The bank had a low 40% fraud detection rate and was managing up to 1,200 false positives per day. Consequently, 99.5% of all cases it was investigating were not fraud related – false alarms that required a substantial investment of people, time, and money to investigate.  

The bank made the strategic decision to apply AI-powered analytics to improve its fraud detection process while reducing false positives – and it paid off: they experienced a 60% reduction in false positives, with an expectation to reach as high as 80%, and a 50% increase in true positives. 


Mastercard’s AI-powered Decision Intelligence technology uses patterns obtained from cardholders’ historical shopping and spending habits to establish a behavioural baseline against which it compares new transactions. Unlike traditional prevention technologies, which rely on a one-size-fits-all approach to transaction evaluation, the Decision Intelligence tool circumvents the common causes of false declines by analysing every transaction in context. 

The challenges of using AI for fraud detection and prevention

The integration of AI-powered fraud detection and prevention functionality into embedded finance platforms is not without its challenges – including:   

  • Lack of data: AI algorithms work by learning from data. SMEs may not have the IT infrastructure needed to harvest enough data to create a baseline understanding of what fraud looks like. 
  • Ethics: The developers who build AI decisioning models can’t always explain how it arrives at the outcomes or which factors had the biggest influence. Known as a “black box”, this opaque symptom of AI creates ethical challenges, such as the fairness and accountability of outputs. 
  • Data privacy and security: AI’s reliance on vast datasets exposes it to privacy concerns around the handling of personal information and potential data breaches. 
  • Regulatory compliance: The General Data Protection Regulation (GDPR) requires everyone responsible for using personal data to follow strict rules called ‘data protection principles’. They must ensure the information is: used fairly, lawfully, and transparently – or face penalties. 

Best practices for implementing AI for fraud detection and prevention in embedded finance

Successfully applying AI to fraud detection and prevention models involves training algorithms to identify trends and characteristics indicative of high-risk transactions within a dataset. There’s more to this than simply pouring data into the funnel; the foundations must be laid to build an informed solution that can automate risk management: 

  • Input data: By feeding segregated data into the AI solution in different forms – such as good data and fraudulent data – it’s empowered to learn and perform better.     
  • Extract features: Things like past transactions, identity details, locations, preferred choices during payment, and network details play a crucial role in determining the signals for identifying fraud. 
  • Algorithm training: To ensure the solution can differentiate between legitimate and fraudulent transactions – and predict fraudulent activities accurately – algorithms are trained using learning datasets. 
  • Model development: Once the training phase is complete the solution can be improved by adding new data or features. 

In the context of embedded finance, AI-powered fraud detection and prevention must be integrated into the entire customer journey – or fraudulent activity may go undetected. Cybercriminals test for vulnerabilities in systems to pinpoint any weak spots they can exploit. Take embedded lending for example: from the moment the customer is onboarded to the moment they receive their funds, they are exposed to cybercrime. When AI is leveraged to protect them at every stage of this journey, they benefit from a truly frictionless embedded experience.  

The future of AI in detecting and preventing financial fraud in embedded finance

According to Juniper Research’s AI in Financial Fraud Detection report, global business spend on AI-enabled financial fraud detection and prevention platforms will exceed $10bn globally in 2027 –  rising from just over $6.5bn in 2022. The report also forecasts that cost savings will reach $10.4bn globally in 2027, from $2.7bn in 2022. 

This rapid growth will be perpetuated by a new trend in AI-enabled fraud management: a focus on accessing fraud information from beyond a business’s transactions. To achieve this, fintech providers are forging partnerships with third parties, such as credit bureaus and payment networks, to improve data coverage – and enhance algorithm learning. 

AI: wave goodbye to manual fraud detection and prevention

We hear a lot of talk about “keeping up with cyber criminals”. To put these words into action, fintech providers of embedded finance platforms are leveraging AI-enabled fraud detection and prevention solutions to do what manual processes can’t: conduct real-time analysis of vast datasets, identify trends indicating suspicious behaviour without delay, and provide automated alerts for timely remediation.  

This proactive approach to fraud management augments embedded finance by providing a vital layer of protection from cybercrime throughout the entire customer journey – one that is scalable, expeditious, accurate, and always improving. 

Get in Touch

If you want to learn more about partnering with Liberis, feel free to get in touch. 

Posted on Notice: Undefined variable: date_format in /code/wp-content/themes/liberis/archive.php on line 374 March 6, 2023 By Kieran Darmody
Embedded Finance

Understanding the Role of Explainable AI in Embedded Finance Compliance 

What do you get when you integrate third-party banking-like services into a non-financial customer journey at the point of need? This might sound like the opening line to a corny joke but the answer is no laughing matter: embedded finance. By removing friction from the consumer experience, and creating growth opportunities, competitive advantage, and operational efficiencies, it presents a significant opportunity to fintech providers and non-financial companies – but a challenge inherent to financial services remains: regulatory compliance. 

Businesses that embrace embedded finance must be empowered to strike a balance between delivering a seamless customer journey and achieving regulatory compliance – fail and they will be exposed to financial penalties and reputational damage.  

Rather than allowing regulatory compliance to slip through the cracks during integrations, embedded finance providers are leveraging explainable artificial intelligence (XAI) to manage this vital requirement – innovation that enhances the delivery of frictionless financial services. 

What is XAI?

AI empowers financial service providers to harness the large volumes of data generated in financial transactions – and the benefits are compelling: identify patterns, make predictions, create rules, automate processes, communicate more efficiently, obtain a holistic view of the customer, and provide timely support. But there’s an inherent problem that presents ethical and regulatory challenges: the developers who build AI decisioning models can’t always explain how it arrives at the outcomes or which factors had the biggest influence.  

The emerging field of XAI is providing embedded finance providers with a platform to overcome issues of transparency and trust by lifting the veil on opaque AI models. This set of processes and methods makes AI models more explainable, intuitive, and understandable to human users without sacrificing performance or prediction accuracy. 

XAI in the context of embedded finance compliance

XAI augments AI’s innate ability to process large volumes of data expeditiously with transparency. This allows it to cut through the regulatory noise and provide information that can be trusted to maintain compliance. 

A single embedded finance regulatory framework does not currently exist, amplifying the need for clarity from a compliance perspective. While the regulatory policymaking process is moving at a glacial pace compared to the exponential growth of embedded finance technology, specific rules and regulations are emerging and more will follow. For example, the recent updates to Buy-Now-Pay-Later (BNPL) regulations in the UK now require providers to perform credit checks. 

XAI automatically horizon scans the regulatory landscape for new rules and regulations – or tweaks to existing ones – updates stakeholders and produces performance data that underpins preventive action if processes shift towards non-compliance. Crucially, there is no ambiguity around the steps taken to reach its conclusions. 

For example, businesses that embed responsible lending services into digital journeys can leverage XAI to reassure customers that they are acting in their best interests – from ensuring affordability and providing transparency of terms and conditions to supporting them if they experience repayment difficulties. 

Challenges of implementing XAI in embedded finance

Yes, XAI is a robust, descriptive tool that offers in-depth insights in comparison to opaque AI models – but it has its own sets of challenges: 

  • Bias: AI systems are only as good as the data they are trained on. Biased data typically leads to prejudice in automated outcomes that can lead to discrimination and unfair treatment. 
  • Fairness: Its perception of fairness in terms of the decisions it takes is contextual and depends on the information fed to the machine learning algorithms. 
  • Cost: Implementing XAI-based systems can be expensive, particularly for small and medium-sized businesses that may not have the resources to invest in such technology. 
  • Data privacy and security: Its ability to collect and process large amounts of data exposes it to privacy concerns. The data may contain personal information which, if not handled properly, can lead to breaches, identity theft, or fraud. 

Use cases of XAI in embedded finance compliance

XAI is helping to power the exponential growth of embedded finance: valued at $54.3 billion in 2022, it is forecast to reach $248.4 billion by 2032. A determination among embedded finance providers to take responsibility for the management of regulatory compliance using XAI – notably fraud, money laundering, and risk – is adding a layer of transparency and trust that’s enhancing its appeal: 

  • Fraud: Explanations of how or why the AI algorithm arrived at a particular conclusion in the fraud detection process can help investigators pinpoint the source and type of fraud. 
  • Money laundering: XAI is being used to replace legacy rule-based anti-money laundering models with intelligent algorithms and self-learning solutions that uncover suspicious transactions and patterns. 
  • Risk: Explainability scores provide clarity during the risk assessment process based on characteristics like the complexity of the dataset on which the model was trained. 

Best practices for XAI in embedded finance

The process of implementing XAI functionality into an embedded finance solution in an integrated and synchronised manner is sometimes mismanaged, leading to crippling pain points – from misaligned objectives and requirements to a lack of scalability. 

Embedded finance providers that adopt a logical step-by-step approach to the implementation process harness the power of XAI to streamline the management of regulatory obligations: 

  • Business and data understanding: Define the business objectives and translate them to XAI-related goals, collect and verify the data quality, and assess the project feasibility. 
  • Data preparation: Produce a dataset for the subsequent modelling phase. 
  • Modelling: Craft one or multiple models that satisfy the constraints and requirements. 
  • Model evaluation: The performance of the trained model should be validated against a test set. 
  • Model deployment: This phase is characterised by its practical use in the designated field of application. 
  • Model monitoring and maintenance: XAI models are typically used over a long period and their lifecycle must be managed. Failure to maintain the model can cause the degradation of performance over time, leading to false outcomes. 

The future of XAI in embedded finance

Predicated on the need to build trust in AI models, the global XAI market size is estimated to grow from $3.5 billion in 2020 to $21 billion by 2030 – acting as a strategic differentiator for those that embrace it.  

According to the 2022 IBM Institute for Business Value study on AI Ethics in Action, 79% of CEOs are prepared to embed AI ethics into their AI practices, up from 20% in 2018. More than 67% of respondents who value AI ethics indicated that their organisations outperform competitors in sustainability, social responsibility, and diversity and inclusion. 

Viewed through the lens of embedded finance compliance, XAI will not only continue to build trust in AI-based solutions; its ability to flag errors will drive improvements in compliance processes.  

Against a backdrop of regulatory development in the embedded finance space, there is also scope for fintech providers to establish industry best practices – with XAI at their core – that can help define future data and privacy policies – levelling the playing field for nonbank companies to provide financial services.  

XAI: building trust in embedded finance compliance

There’s no questioning the benefits of AI when it comes to conducting real-time analysis of vast datasets – but there’s a stigma attached that it’s found hard to shake: its proliferation as a tool to increase efficiency, save money, and inform decision-making has raised questions about the trustworthiness of the outcomes it produces.  

While XAI is not a silver bullet, it is ensuring AI can be better understood by making algorithms and their application less enigmatic. Consequently, humans are inclined to trust the AI model because the characteristics and rationale of the AI output have been explained. 

Forward-thinking businesses that embrace embedded finance are benefitting from XAI functionality that’s building trust in compliance management – elevating the integrity of these organisations. 

Get in Touch

If you want to learn more about partnering with Liberis, feel free to get in touch.

Posted on Notice: Undefined variable: date_format in /code/wp-content/themes/liberis/archive.php on line 374 February 28, 2023 By Kieran Darmody
Embedded Finance

The Future of AI-Powered Personalisation in Embedded Finance

Embedded finance – the seamless integration of banking-like services into non-financial ecosystems and environments – is empowering businesses to take ownership of their access to financial services. No longer fettered by the monopoly banks once held, they are demanding bespoke experiences beyond physical branches and points of sale – a trend that has been stoked by the emergence of FinTechs.  

These subversive providers are disrupting the way businesses view financial services: they don’t want to be treated like a commodity by legacy providers that offer generic products and services; they want their interactions with loans, accounts, and payments to mirror the highly personal and frictionless experiences they enjoy elsewhere online. 

This need to develop a deep understanding of each customer’s requirements and orchestrate a set of tailored digital experiences is being powered by AI. 

The role of AI in delivering personalised experiences in embedded finance

Businesses are more likely to buy from a brand when they are acknowledged, remembered, or get relevant recommendations – which requires data to be successful. The more information that can be aggregated and analysed about a business, the easier it is to deliver personalised services that are relevant to their specific needs and preferences.  

Embedded finance looks beyond conventional data sources based on demographics and age by leveraging AI-powered personalisation. This is changing the ways brands interact with customers by conducting real-time analysis of vast datasets – such as transaction history, page clicks, social interactions, past purchases, and location – and recommending products and services based on their browsing/buying preferences.  

This insight allows brands that embrace embedded finance to enhance the customer journey by anticipating their expectations and giving them what they want at the right time, through the right channels – from tailored financial advice to contextualised loans. 

Impact of AI-powered personalisation on embedded finance

AI is a driving force behind the exponential growth of embedded finance: valued at $54.3 billion in 2022, it is forecast to reach $248.4 billion by 2032. This growth is being fuelled by an expectation among businesses for embedded finance to enhance customer engagement, satisfaction, and loyalty through AI-powered personalisation. 

By augmenting embedded finance with AI, this conduit for frictionless financial services becomes a truly customer-centric offering. Its ability to segment product offerings by market audience and distribute them as part of an integrated, personalised omnichannel experience taps into the modern consumer’s requirements: 81% of consumers want brands to get to know them and understand when to approach them and when not to.  

Challenges of implementing AI-driven personalisation

Embedded finance providers know a thing or two about integrating technology – which is handy because the process of implementing AI-powered personalisation into their systems presents challenges: 

  • Data privacy and security: The need to create a more personalised experience for customers requires access to large amounts of personal data – exposing businesses to increasingly stringent regulations. Failure to balance the need for customer data with the need to protect user privacy can lead to hefty fines and reputational damage. 
  • The Data Protection Act 2018: The UK’s implementation of the GDPR requires everyone responsible for using personal data to follow strict rules called ‘data protection principles’. They must ensure the information is: used fairly, lawfully, and transparently.  
  • Ethics: Due to the opaque nature of the algorithms that power AI, it can unintentionally operate in unethical ways to meet its targets – potentially damaging brand reputation and leading to legal issues. 

Best practices for implementing AI-driven personalisation into embedded finance

Before AI can do the heavy lifting on the data and personalise the customer journey, it needs to be seamlessly integrated into the embedded finance experience.  

Every AI implementation is unique: diverse data sets with different variables, challenges with existing software or hardware and unique expectations and goals. To get this right, several key factors should be considered, including: 

  • Realistic goals: Stakeholders should outline the basic steps required to create a viable personalisation offering, rather than redesigning the entire workflow. This incremental approach allows teams to start small and build, and for early successes to fuel future projects. 
  • Secure: AI-powered personalisation is underpinned by data, so it must comply with relevant security and privacy regulations.  
  • Scalable: Data models, infrastructures, and algorithms must be agile enough to increase or decrease their complexity, speed, or size at scale depending on the business’s dynamic requirements. 
  • Automated updates: The built models and applications must be kept up to date – which can be time-consuming and expensive if conducted manually. Automated updates ensure high performance is maintained automatically. 

The need for automated updates underscores the importance of continuous improvement and innovation in AI-powered personalisation. AI is inherently adaptable, allowing it to create user experiences that are increasingly natural and engaging by constantly learning and adjusting. 

The future of AI-powered personalisation in embedded finance

A current AI trend that will enhance the personalisation process is the development of ethical and explainable models. AI requires data to learn – and often this means sensitive personal data. If businesses don’t trust AI or understand how it makes decisions, they won’t feel safe handing over their personal information – making it rudderless.  

Transparent AI systems are being developed that can explain how decisions are made and what information was used to arrive at them. Advancements in AI ethics are allowing organisations to eliminate bias from their automated decision-making processes. Biased data typically leads to prejudice in automated outcomes that can lead to discrimination and unfair treatment – which is unacceptable in a world where AI is ubiquitous in digital decision-making processes. 

Amid this appetite for innovation, the global personalisation software market is expected to grow from $620 million in 2020 to $2.2 billion by the end of 2026. 

Embedded finance and AI: a perfect marriage

Once a binary process that was limited to product quality and price, competitive differentiation has evolved into a customer-centric pursuit thanks to the augmentation of embedded finance with AI.  

AI-powered personalisation compliments the core aim of embedded finance – to enhance the customer journey by providing instant experiences at the right time and place – by adding a vital layer to this modus operandi through the expedition of data aggregation and analysis: it provides the insight needed to ensure these experiences align with the businesses needs and preferences. 

By enhancing embedded finance’s ability to attract, convert, and retain digital consumers, AI-powered personalisation has reinforced the sustainability of this forward-thinking approach to accessing financial services.  

Get in Touch

If you want to learn more about partnering with Liberis, feel free to get in touch.

Posted on Notice: Undefined variable: date_format in /code/wp-content/themes/liberis/archive.php on line 374 February 16, 2023 By Kieran Darmody
Embedded Finance

The 4-click Journey: Streamlining Embedded Finance with AI

Small businesses have every right to expect lending to be instant and transparent – just like every other service that has been disrupted by the digitalisation. Unfortunately, traditional lending products are failing to meet their expectations – and legacy providers only have themselves to blame. Their reluctance to respond to the demand for frictionless lending is no longer acceptable in today’s internet-enabled world.  

Traditional lending: embedded finance to the rescue

According to our 2022 survey commissioned with YouGov, 59% of SMEs would consider using their main bank when seeking funding. What they don’t realise is these legacy lenders provide clunky services that rely on outdated infrastructure and laborious manual processes. Amid a blizzard of paperwork, disjointed systems and frustrating phone calls, the lending application and assessment processes remain infuriatingly ponderous. Even if a loan is eventually authorised, it might be too late for an SME that has a time-critical need for finance. 

The tide isn’t just turning in the lending space; it’s being flooded by a new breed of alternative lending platforms that are riding the embedded finance wave – and challenging traditional lending models in the process. There’s virtually no part of the modern finance ecosystem that hasn’t been enriched by this seamless integration of financial services into non-financial ecosystems and environments. 

Embedded finance

Embedded finance – a subset of this new distributed approach to delivering financial services – has gained traction amid an avalanche of demand for a frictionless, digital-first borrowing experience. What began as helpful assistance for partners when managing the technology element of the lending process has evolved into a lucrative market: revenue generated by the embedded lending market totalled $4.7 billion in 2021 and is expected to reach $32.5 billion by 2032.  

Its ability to add value to the customer journey by providing seamless access to finance isn’t a linear attribute. It possesses the agility to create bespoke lending experiences for different customer groups that match their unique requirements. Take companies like Sezzle and Klarna, for example, who have partnered with Liberis to embed our platform into their offering as a value-added service for their business customers – enhancing loyalty and increasing revenue.  

Augmenting embedded finance with AI

This ability to mould the lending experience around the merchant’s business model is being elevated by artificial intelligence (AI). Embedded lending providers are leveraging AI to conduct real-time analysis of broad and diverse customer data sets and present their findings transparently and expeditiously. Empowered by the results, they can personalise the lending journey by learning from previous experiences. 

AI’s innate ability to foster a detailed understanding of the customer’s needs and preferences has changed the way we think about user experiences. Algorithms process data rapidly before applying changes to optimise the findings. By continually learning and adjusting, they improve the user experience to offer a more engaging, customised experience that matches current trends and behaviours. 

AI also has the power to facilitate instant and transparent decision-making processes, enhancing the user experience by addressing the applicant’s fear of rejection early in the process or right at the start in the case of pre-approval – according to our survey, 15% of SMEs say rejection is one of their biggest funding concerns. This peace of mind allows SMEs – for which restricted cash flow can be an existential threat – to start planning immediately. 

The 4-click journey

Embedded lending – augmented by AI – creates a symbiotic relationship between the innovative lenders who deliver it and the brands that embrace it: innovative lenders are ‘inserted’ into the moment the customer identifies a funding requirement or even before they identify their requirement, and brands benefit from a competitive advantage by elevating their offering. 

For this to play out, SMEs must be provided with a seamless lending experience that unlocks access to funds quickly and cost-effectively. Aware of our impatience online, through its machine learning capabilities, AI can expedite the lending process by enabling a 4-click journey that has convenience, transparency, and personalisation at its core. 

This frictionless process stimulates the point of need access to capital that SMEs demand, improving their cashflow management; while the partners that embed lending into their ecosystem improve brand loyalty and increase revenue through an improved user experience. 

The future of AI in the user experience

No longer shrouded in mystery AI is now part of our everyday lives, even if we don’t realise it – but we’ve only scratched the surface of its potential to improve the lending journey by expediting decisions and addressing rejection anxiety. The dynamic nature of AI – which is constantly learning and adjusting – instils it with the adaptability needed to create user experiences that are increasingly intuitive, natural, and engaging.  

Posted on Notice: Undefined variable: date_format in /code/wp-content/themes/liberis/archive.php on line 374 February 10, 2023 By Kieran Darmody
Embedded Finance

Embedded Finance and XAI: Credit Decisioning with Clarity

Consumers are obsessed with their credit scores. Some who have achieved the holy grail of a score above 800 have even been known to boast about it on their dating profiles. For businesses, which are scored in a range from 0 to 100, anything above 80 indicates good financial health and creditworthiness. But should they obsess about them – and boast about them on their LinkedIn profile?

The ‘credit bureau blind spot’ suggests they’re not as reliable as everyone thinks. This phenomenon refers to the inability of loan providers to accurately assess creditworthiness using legacy credit decisioning models that underpin their lending processes. Encumbered by narrow credit reports that overlook the applicant’s future financial position, instead relying on payment history and outstanding debt, these providers are unable to gain a holistic view of their financial position.

A lack of competition among credit bureaus also means that lenders typically make decisions based on the same information, restricting differentiation. This can make it difficult for businesses with a poor credit score to receive funding approval or do anything to improve their score.

Against this opaque backdrop, businesses are demanding instant access to real-time data that facilitates informed decision-making. Advances in artificial intelligence (AI) are generating opportunities for lenders to develop transparent credit decisioning models that mitigate the risk of rejecting creditworthy applicants and approving those whose finances might deteriorate.

Explainable AI to the rescue

The use of AI in finance is growing rapidly, with applications in areas such as risk management, fraud detection, and algorithmic trading – prompting the adoption of explainable AI (XAI) tools: a set of processes and methods that make AI models more explainable, intuitive, and understandable to human users without sacrificing performance or prediction accuracy.

Credit decisioning is also being elevated by this advanced technology, which is having a proven impact on credit-approval times and percentages. XAI-driven decisioning streamlines lending journeys by conducting real-time analysis of customer data to expedite credit decisions for retailers, small and medium-sized enterprises (SMEs), and corporate clients. This is achieved by aggregating structured and unstructured data from traditional sources (such as bank transaction history, credit reports, and tax returns) and overlooked sources (such as location data, telecom usage data, and utility bills).

By leveraging XAI to analyse these broad and diverse data sets, businesses can qualify new customers for credit services and determine loan limits and pricing expeditiously – and the benefits are compelling:

  • Increase in revenue through higher acceptance rates, lower cost of acquisition, and better customer experience.
  • Reduction in credit-loss rates by more precisely determining customers’ likelihood to default.
  • Efficiency gains through highly automated data extraction and case prioritisation.
  • Enhanced fraud management by replacing manual processes with automated detection and prevention tools.
  • Improved customer experience by addressing applicants’ fear of rejection early in the funding journey and by providing instant and transparent decision-making processes.
  • Reduces compliance risk by powering faster and more secure transactions, expediting informed decisions, and automating regulatory change management.

XAI and embedded finance

XAI is underpinning one of the hottest trends in the world of finance today: embedded finance. This seamless integration of financial services into non-financial ecosystems and environments is expected to grow at breakneck speed over the next decade: valued at $54.3 billion in 2022, it is forecast to reach $248.4 billion by 2032. This growth is being driven by an expectation among businesses for embedded finance providers to leverage XAI risk models and continuously invest in new ways to learn from traditionally overlooked sources.

Credit decisioning is a fulcrum of embedded lending: a subset of this new distributed approach to providing financial services that eliminates the need to rely on high-cost third parties – typically a financial institution – within the lending process. By integrating XAI-driven decisioning tools into this digital-first lending experience, it becomes a truly frictionless value-added service.

This is empowering nonfinancial businesses that embed lending functionality into their customer journey to make informed financial decisions directly within the context of their core non-financial applications or products. For example, Liberis offers a revenue-based lending model driven by an intelligent data engine that automatically forecasts business transaction revenues and makes a personalised and preapproved offer instantaneously – with 70% of businesses receiving their funding in less than 48 hours.

Credit bureaus typically focus almost exclusively on negatives – such as missed payments and prior defaults – meaning businesses don’t get rewarded for good financial behaviour. By leveraging XAI to factor in other data sources – such as revenues – and developing a more holistic view of a business, Liberis can apply a more positive mindset to our decisioning with a bias towards approving where possible.

Research by Bain Capital estimates that by 2021 around $12 billion in B2B loan transactions were made via embedded finance, which it expects to increase to between $50 billion and $75 billion by 2026. XAI is fuelling this exponential growth by allowing lenders to augment historic credit data with forward-looking insights into an applicant’s suitability to repay a debt obligation in the future – and present their findings transparently and expeditiously.

The future of XAI in credit decisioning

XAI is playing a vital role in helping more SMEs gain access to the credit they deserve so they can live their best financial lives – and it’s only just getting started. By its very nature, XAI is constantly developing. This dynamism will continue to enrich credit decisioning processes through advancements in open bank connectivity for access to richer data, and by harnessing counterintuitive data that challenges the status quo.

Posted on Notice: Undefined variable: date_format in /code/wp-content/themes/liberis/archive.php on line 374 January 23, 2023 By Kieran Darmody
Embedded Finance

Building Trust Between Partners in Embedded Finance 

Bill Gates knows a thing or two about technology. The co-founder of Microsoft once said: “The advance of technology is based on making it fit in so that you don’t really even notice it, so it’s part of everyday life.” This seamless digital experience is a cornerstone of embedded finance: the integration of traditional financial services or tools within a non-financial business’s infrastructure – from payments to lending to accounts.  

Bill’s experience has also taught him the value of another fundamental element of this finance revolution: partnerships. According to the seventh richest person in the world: “Our success has really been based on partnerships from the very beginning.” In the context of embedded finance, this refers to the level of collaboration between the non-financial business and the third-party technology provider. 

The foundation of a successful embedded finance partnership is trust. This will allow both parties to forge a symbiotic relationship that benefits everyone involved. So, what’s important when it comes to building trust between partners in embedded finance? 

Information Security

Embedded finance has the power to democratise financial services – but its reliance on sensitive customer data means it comes at a risk. Cybercriminals are opportunists. Their determination to exploit new financial services by developing innovative attacks designed to compromise data exposes the burgeoning embedded finance industry to cyberattacks. For example, the attack surface within the embedded payments space is rapidly expanding: market research firm IDC forecasts that 74% of online consumer payments globally will be conducted via platforms owned by nonfinancial institutions by 2030. 

Embedded finance technology providers must, therefore, instil trust by safeguarding data through the application of appropriate security layers relative to consumer activity in that ecosystem – from implementing two-factor authentication to complying with GDPR to developing internal policies that protect data to achieving ISO accreditations. 

Take ISO 27001 for example: the international standard for information security, which sets out the specification for an information security management system (ISMS). This framework helps organisations protect their information systematically and cost-effectively, through the adoption of an ISMS.  

Best in Class

The power of embedded finance to make financial services ubiquitous in today’s market is driven by established third-party technology providers. While it’s possible to build a solution in-house, the challenges soon escalate – including: 

  • Costs: when building in-house, you bear all the costs – from legal and resource build to ongoing software updates. 
  • Time to market: building an embedded finance solution in-house typically takes significantly longer – from developing risk models to building a technology platform to support customer onboarding. 
  • Regulations: financial services are traditionally highly regulated and often require specific licenses and registration from regulators and authorities. 
  • Opportunity cost: time and experience are scarce business resources – particularly for SMEs. Choosing to build in-house can deprive other projects of vital resources.  

While it can be daunting for non-financial organisations to rely on a third-party technology provider, their knowledge and experience of integrating creative financial services into their partners’ end experiences offers compelling benefits – from expediting the time to market to absorbing regulatory requirements to reduced costs to ongoing development and support.  

Quality of integrations

Let’s take the buy or build debate a step further by exploring the technical element of partnering with a technology provider to create an embedded finance ecosystem. This brings application programming interfaces (API) into sharp focus.  

Once developed, API integrations enable faster, cost-effective, and more secure service enhancements. However, any new capability requires substantial programming resources, takes months to develop and deliver, and demands intense governance, risk, and compliance (GRC) analysis. 

Working with a tech-led embedded finance partner that specialises in developing sophisticated APIs that allow rapid deployment won’t just save you time and money; you can also easily add new features when necessary. The best partners will provide technical resources to assist you during the integration process and troubleshoot any issues once live.  

Shared Vision

This intersection of technology and financial services will drift off course unless both parties are on the same page. Any potential technology partner must be aligned with your business’s objectives, committed to delivering the product accordingly, and flexible enough to allow you to change your view during the partnership. For example, if your main objective is to gain a new revenue stream, you must communicate this and work together to achieve it – otherwise, the integration could end up being a costly mistake. 

Failure to cooperate and achieve strategic synergy could result in your business wasting time and money by implementing embedded finance technology for the sake of it – leaving it rudderless and unable to fulfil your objectives. Moreover, the new offering will not match the quality of your core product and the customer journey will experience more, not less, friction. 


An established technology provider with a strong track record can demonstrate how their seamless embedded finance integrations have delivered two key benefits for existing partners: value creation and increased revenue. 

These case studies should form part of the vendor selection process. They will show how businesses like yours that have embedded financial services into their nonfinancial offering are improving the customer experience and opening new revenue streams to complement their current model. You can even contact these companies directly to confirm that the technology provider has provided a better value proposition for the end user that encourages them to spend more money. 

Vendor Selection Process

Embedded finance is typically a leap into the unknown for a non-financial organisation. The benefits of augmenting their offering with financial products are well documented and enticing, but success is not guaranteed in this alien environment. This brings the vendor selection process into sharp focus for businesses seeking to harness the power of embedded finance to enhance the user experience. If you’re planning on joining the embedded finance revolution, you must trust the provider you partner with from a security, technical, reputational, and strategic perspective.  

To ensure they align with your objectives and have the necessary credentials to help you achieve them, carefully consider the options available before making an informed decision. After all, this is a long-term investment in a meaningful partnership, not an off-the-shelf product.  

Get in Touch

If you want to learn more about partnering with Liberis, feel free to get in touch.

Posted on Notice: Undefined variable: date_format in /code/wp-content/themes/liberis/archive.php on line 374 November 21, 2022 By Kieran Darmody
Embedded Finance

Embedded Value-Added Service

Embedded finance is often defined as financial services on the consumers’ terms. This amalgamation of traditional financial services with non-financial companies creates seamless experiences for customers by streamlining their access to services like payments, loans, and accounts – but it isn’t the only type of embedded system making a wave out there.

The battle for customer loyalty and retention online has also prompted a demand for value-added services to be embedded into their experiences – such as fraud management, know-your-customer, compliance, insurance, and loyalty rewards. By plugging non-traditional offerings into their platforms using APIs and harnessing the “better together” proposition, businesses can enhance the customer journey by creating new value for them.

The rise of value-added services

Not so long ago the high street was king when it came to shopping. Businesses could peddle their wares in physical stores without worrying much about creating new value for their customers – for whom choice was limited compared to today. The exponential growth of the internet this century has not only sounded the death knell for the high street following a shift online; it has reshaped the battlefield for customer loyalty and retention amid an explosion in competition and changing demands.

For a business to appeal to new and existing customers and remain competitive in a crowded online marketplace, it must think beyond its core offering and add value to the relationship. One way to achieve this is to offer auxiliary services that customers will find valuable and will complement their core services.

Take banks for example, which have traditionally had a myopic view of service provision. Having spent decades building their products and services and offering one-stop banking for small businesses and personal customers, they have become hampered by a siloed approach that’s no longer viable. The modern consumer wants a relationship with their bank that extends beyond standard transactions and balance checking to the integration of complementary services – and the fintech disruptor banks are showing the legacy players the way.

Embedded value-added service

As businesses are required to work increasingly hard to compete online, they must think outside the box and consider value-added services – or risk losing customers. A value-added service is a feature that can be embedded into a core product to enhance the user experience or a service that can function as a standalone product or feature – and they’ve become fundamental to customer loyalty and retention in the highly competitive online business world.

The fintechs that are driving the growth of embedded value-added services excel at understanding the customer and creating offerings specific to their requirements. This valuable insight is reimagining the scope of the businesses they support. With their blinkers removed, these businesses don’t have to rely on generic one-size-fits-all bolt-ons; they can embrace additional services that are complementary and add value to their customer’s experience.

Examples of embedded value-added services that are helping to complement businesses’ core offering include:

  • Square: The payments company has expanded its appeal by adding a range of add-ons like email marketing and payroll support.
  • Starbucks: The world’s largest coffeehouse chain adds value to its customer experience through its customer loyalty points programme. To earn loyalty points – or, in Starbucks’ case, loyalty stars – customers must order or pay using the Starbucks app. They can redeem those stars to get free drinks, food, and even merchandise.
  • STET: This automated clearing house offers fraud scoring as a value-added service for all payment types it processes.

By focusing on their customer needs and embedding value-added services, businesses can strengthen existing relationships and build new ones – and the benefits are compelling: customer loyalty, customer retention, competitive advantage, stimulates demand for core products and services and can generate additional revenue.

The future of embedded value-added services

The value-added proposition is not a rigid selection of services that businesses are forced to choose from; it’s a dynamic process that can be tailored to meet their customer’s unique requirements. This fluid landscape means existing value-added services are constantly being enhanced and new services are being developed and embedded into the native customer journey.

Embedded loyalty programmes that offer consumers rewards and incentives such as discounts, vouchers, cashback, and reward points are a prime example of how this constant evolution is driving growth: the number of loyalty programme memberships is forecast to grow by 33% from 24 billion worldwide in 2022 to more than 32 billion in 2026. It’s a similar story in the mobile embedded value-added services market – services offered by telecom providers to customers beyond core services like SMS, voice, and data – which was valued at $655 billion in 2021 and is expected to reach a value of $1133.85 billion by 2029.

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Embedded Finance

Embedded Business Management

The benefits of embedded finance – the seamless integration of financial services by non-financial companies into their digital experience to deliver new, innovative, and streamlined customer experiences – are typically viewed through a B2C lens. But the scope of this finance revolution extends beyond reducing the friction that impedes online financial transactions to breathing new life into ambitious businesses admin processes.

Embedded business management uses the banking-like services offered by nonbanks model to embed a different kind of convenient service into a business’s infrastructure: accounting tools.

Traditional accounting

According to research by salary benchmarking site Emolument, the accounting profession ranks fifth in a roundup of the most boring jobs. It might be yawn-inducing, but this crunching of numbers is vital to the successful operation of a business – and must move with the times to be effective. Traditional manual accounting processes have evolved during the digital revolution from physical books using a written ledger of transactions to spreadsheets. But even these electric documents can become confusing, time-consuming, and error-strewn – making them outdated amid the emergence of embedded business management.

As a business grows, its financial data evolves with it, becoming more complex and increasing in volume. Small and medium enterprises (SMEs) typically don’t have the resources to create a dedicated function to conduct laborious – but vital – accounting processes. This leaves them with three options: conduct them in-house when there’s time, exposing the business to errors and delays; outsource them to an expensive third-party provider, placing strain on tight budgets; or think beyond antiquated accounting methods by embedding them into the business infrastructure.

Embedded business management

Embedded business management empowers SMEs to focus on what they care about most without worrying about the admin, which is automated and consistent. This means less effort, less time and lower costs when running a business compared to using clunky manual processes. Unshackled from repetitive admin and time-consuming processes, business owners can use their resources more proactively.

This subset of embedded finance has many beneficial branches of its own: from embedded payroll that allows business owners to set a single pay rate, to embedded bank feeds that automatically appear in accounting software, to embedded accounts payable that automate purchase orders when stock levels hit certain limits.

These core functions are often delivered using an enterprise resource planning (ERP) cloud solution: a suite of integrated applications that collect, store, manage and interpret data to gain resilience and real-time agility – and position for growth.

Examples of popular embedded business management experiences that are helping to streamline SMEs’ accounting processes include:

  • Clearbooks: intuitive online accounting software designed for UK-based small businesses, contractors, freelancers, and sole traders.
  • Bench: APIs let financial institutions and SaaS partners embed their platforms with an integrated financial solution like bookkeeping and tax services, providing SMEs with actionable insights to grow their business.
  • Tide: Limited companies save time with accounting integration, centralised invoicing and expense cards for easy expense management.

Embedded business management functionality is brimming with benefits:

  • Saves time: manages time-consuming manual bookkeeping and accounting processes automatically.
  • Convenient accounting: cloud-based software allows you to connect from an internet-enabled device from anywhere, at any time.
  • Syncs financial data: siloed business data that’s stored across multiple platforms is synced via an API, so you can compile financial records quickly and easily.
  • Improved accounting security: data is secured in the cloud under layers of high-end encryption algorithms, making your records easy to retrieve when you need them.
  • Integrate with other business apps: integrates accounting application software with other apps like CRM solutions, reporting applications, and information management systems.
  • Generates financial reports: built-in reports – such as your income statement, balance sheet, and cash flow statement – are automatically updated. This provides key insights and facilitates informed decision-making.
  • Data accuracy: the software automatically refreshes your business’s financial statements and reports to reflect any changes you make, keeping your data error-free.
  • Provides detailed insights: provides you with a transparent view of your business’s financial posture, helping you to produce focused reports and make informed strategic decisions.
  • Streamlines tax filing: standardised financial statements and accurate data simplify the tax filing process and your ability to calculate available tax credits.

The future of embedded business management

Research by Bain Capital suggests that payments and lending will continue to be the largest embedded financial services but will be bolstered by the growth of adjacent value-added services, including tax and accounting. As the pace at which organisations transition to digital-first admin processes continues to accelerate, embedded business management functionality will become ubiquitous across the business landscape.

What’s next

Check out our final instalment of the embedded ecosystem blog series – Embedded value-added services – which will look at how nonbank financial services companies can embed things like insurance into their service offerings and how it’s become instrumental in increasing customer loyalty.

Get in Touch

If you want to learn more about partnering with Liberis, feel free to get in touch.

Posted on Notice: Undefined variable: date_format in /code/wp-content/themes/liberis/archive.php on line 374 November 7, 2022 By Kieran Darmody
Embedded Finance

Embedded Lending

Born out of a desire to disrupt the traditional banking model in the wake of the 2008 financial crisis, challenger banks – or neobanks – kicked off the finance revolution. This disruptive rebellion has inspired another new breed of provider to take to the banking battlefield and challenge the old guard across a broad spectrum of services: non-financial organisations.

Empowered by the rise of embedded finance – the integration of traditional financial services or tools within a non-financial organisation’s infrastructure – businesses and their customers are benefitting from streamlined financial processes that reduce friction when accessing products and services online. Today, embedded finance pervades online transactions – but you might not even realise you are benefitting from it because of its power to smooth the customer experience.

A subset of this new distributed approach to providing financial services eliminates the need to rely on high-cost third parties – typically a financial institution – within the lending process: embedded lending.

Traditional lending

There was a time – not so long ago – when you had to arrange a meeting with your bank manager and physically go into your local branch to apply for a loan. The lack of lending options meant banks – which held the monopoly over the lending space – were judge and jury of who was creditworthy. While the internet has given rise to a new wave of digital lending options – and diluted the role of the bank manager – traditional financial institutions continue to rely on outdated, labour-intensive legacy processes and narrow credit-decision criteria.

Take small and medium-sized enterprises (SMEs), for which restricted cash flow can be an existential threat. According to the World Trade Organisation, they represent over 90% of businesses and 60-70% of employment worldwide. Despite the vital role they play in economies across the globe, many struggle to access the funding they need to keep operating and growing.

Traditional institutions’ rigid lending framework prevents SMEs from accessing capital because they are considered too risky. This myopic view stems from a range of factors that are typical among them, including:

  • They lack formalised governance processes
  • They lack publicly available information
  • They often operate in emerging sectors
  • They often have insufficient assets to be used as collateral
  • They produce low credit scores

These lending hurdles are exacerbated by the banks’ clunky and costly client support infrastructure and convoluted application and assessment processes. Even if a loan is eventually authorised amid these time-consuming constraints, it might be too late for an SME that has a time-critical need for capital.

Embedded lending

Embedded lending gained traction in the face of pandemic-induced lockdowns that shuttered businesses and strangled household incomes; a trend that has been perpetuated by a general demand for a frictionless, digital-first lending experience. This process of integrating credit or financing products into non-financial businesses, such as online retailers or marketplaces, allows customers to access finance at the point of need from a non-financial brand they trust – removing any interaction with a bank or other lender.

Using a customisable API (Application Programming Interface) or white label solution, digital brands can integrate embedded lending options into their technology ecosystem or e-commerce platform. This dynamic offering can be tailored to meet their specific customer needs, ensuring brand integrity remains intact. The entire lending process subsequently becomes faster, simpler, and frictionless, allowing applicants to focus on using the funds, rather than applying for them.

This convenience fosters the point of need access to capital that SMEs crave, improving their cashflow management; while consumers can access flexible payment structures that enhance their online transaction experience. Embedded lending’s innate ability to provide businesses and individuals with access to useful, affordable and responsible lending products and services underscores the role it plays in driving financial inclusion.

Buy Now Pay Later (BNPL) is an example of B2C embedded lending: a type of short-term financing that allows consumers to make purchases and pay for them at a future date. For example, Clearpay is a payment service that lends customers a fixed amount of credit to make purchases instantly before paying for them in four interest-free automatic instalments, made every two weeks.

Revenue-based finance is an example of B2B embedded lending: an alternative funding option that allows SMEs to access funding based on their overall business revenue – not just their credit history. For example, Liberis offers a revenue-based lending model driven by an intelligent data engine that automatically forecasts business transaction revenues and makes a personalised and preapproved offer – with 70% of businesses receiving their funding in less than 48 hours.

The benefits of embedded lending are being felt throughout the modern lending ecosystem: businesses and retail customers benefit from a seamless lending experience that unlocks access to funds quickly and cost-effectively; brands that embrace it benefit from a competitive advantage by augmenting and enhancing their offering; and innovative lenders are ‘inserted’ into the moment the customer identifies a funding requirement.

The future of embedded lending

According to a World Bank report, the world’s SMEs have unmet finance needs of approximately $5.2 trillion a year, around 1.5 times the current lending market for businesses of this size. Against this backdrop of escalating demand for finance without friction, embedded lending is well-placed to go from strength to strength. For example, the proliferation of BNPL within the B2C space has inspired e-commerce platforms to offer lending solutions to their business customers in the UK – a trend that is expected to gather pace.

Research by Bain Capital estimates that by 2021 around $12 billion in B2B loan transactions were made via embedded finance, which it expects to increase exponentially to between $50 billion and $75 billion by 2026.

Driven by increased customer loyalty and brand value, embedded lending in the B2C space has rapidly evolved from a burgeoning value-added service into a ubiquitous facilitator of streamlined lending experiences. According to Bain Capital, around 10% of point-of-sale transactions are made via embedded finance, resulting in a transaction value of around $43 billion. By 2026, this market is expected to grow to between $80 billion and $90 billion – and there won’t be a bank manager in sight.

What’s next

Check out our blog on embedded business management. Many small businesses don’t have the resources to create separate functions for important tasks like accounting. Embedded business management is the part of the ecosystem that can accommodate this allowing small business owners to focus on what they do best, growing their businesses.

Get in Touch

If you want to learn more about partnering with Liberis, feel free to get in touch.

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Embedded Finance

Embedded Deposits

Astonishingly, customer-centricity – an approach to doing business that focuses on providing a positive customer experience at the point of sale to drive profit and gain competitive advantage – has traditionally been an afterthought when delivering financial interactions. This short-sighted outlook left consumers resigned to their fate: to accept the disconnect between themselves and the company they’re doing business with; a gap that’s bridged by a third-party bank they are redirected to, creating an extra layer of friction.

This clunky process is increasingly unacceptable in the modern financial services landscape, where tradition has been replaced by innovation – and customer-centricity is a prerequisite. Embedded finance has disrupted the involvement of third parties through the disintermediation of financial interactions. This seamless integration of financial services by non-financial companies into their infrastructure is not limited to streamlining the payments process; other core interactions have been enhanced by embedding them behind these organisations’ apps and websites, including bank accounts – known as embedded deposits or embedded banking.

Traditional banking

Customer retention has never been a huge concern for traditional high-street banks – until now. Their general model is simple and effective: get someone to create an account when they’re young and assume they will bank with them for life because the hassle of changing to another similar high-street institution isn’t worth it.

With their choice restricted to a few brick-and-mortar providers that held a monopoly over the banking space – and offered the same services under different brands – consumers’ access to financial services was limited. Since the global financial crisis of 2008, however, the tide has turned amid the emergence of a broad set of tech-driven financial companies (fintechs). This new breed of provider aims to fundamentally address outdated financial services by offering access to innovation that supersedes the traditional methods used by incumbent banks – a trend that has been accelerated by the pandemic after consumers’ reliance on online functionality increased profoundly.

The conventional banking infrastructures flaws have been amplified in the face of this fintech revolution: slow in undertaking digital transformation, legacy infrastructure that lacks agility, strict regulatory standards, poor customer service, and the emergence of disruptive banking models.

Throw in the odd scandal – notably Payment Protection Insurance (PPI) mis-selling – that’s dented the public’s faith in them, and the banks’ grip on the industry has been prized loose. Take TSB for example, which in November 2021 announced that it was shutting 70 bank branches across the UK the following year as more customers switch to online.

Embedded deposits

The emergence of disruptive banking models has paved the way for embedded deposits to reshape the banking landscape: the process of incorporating specific banking tools – such as debit cards and checking accounts – into non-financial companies’ products or software, forming part of a larger bundle of services. When banking is embedded into a non-bank environment, it streamlines the customer journey while building more secure, fluid experiences into the tools they already use, increasing retention. By bringing banking to the customer, it creates simple, linear journeys that can be completed without opening a banking app or website.

Examples of popular embedded deposit experiences that are helping to drive a new era of flexible banking include:

  • Shopify: The Canadian multinational e-commerce company’s banking feature aims to encourage small business owners to create a separate bank account for their company, rather than use their personal checking and savings accounts or set up a business account with their bank.
  • Worldpay & FIS: The British/American payment processing company and technology provider makes it easy for vendors’ customers to pay for products online using their digital or mobile wallet of choice – essentially a new breed of agile bank accounts – helping drive conversions, accelerate checkout, and boost revenue.
  • Starbucks: The Starbucks app offers deposit and credit products provided by investment bank and financial services holding company JPMorgan Chase, allowing customers to store cash and earn rewards for in-store purchases. In the US, a quarter of store transactions now occur via the app, with the retailer holding as much cash in its app and on its cards as some banks hold in deposits.

While fintechs still dominate the conversation, banks are starting to engage in the embedded banking space. Once considered upstarts in this previously rigid sector, the banks are viewing fintechs as potential co-collaborators to help establish their own digital footprints – commonly known as banking-as-a-service (BaaS). For banks, this can open the door to new revenue streams and expansion into unbanked customer segments. But there is still a long way to go for these traditional players: according to the 2021 Economist Impact report, a little over a quarter (27%) of banks and credit unions surveyed believe their organisation has the necessary technology tools – “to a great or large extent” – to create new digital products and services internally or externally.

Embedded deposits have empowered small businesses to take control of their banking. The monopoly once held by a handful of institutions in the banking space has been broken by the choice and convenience that’s inherent to fintechs. No longer an afterthought, customer-centricity is now a cornerstone of this streamlined approach to banking.

The future of embedded deposits

According to recent research by Finastra, embedded bank accounts and payment cards are poised for 30% growth by 2024. This trend is echoed by Bain Capital research which estimates that by 2021, US consumers and businesses spent $3.60 trillion on their debit cards and $3.55 trillion

on their credit cards – with 3% and 4% of these transactions for debit cards and less than 1% for

credit cards, conducted using embedded banking services. By 2026, Bain Capital predicts that the nonfinancial services market penetration for debit cards could increase fivefold to around 15%.

What’s next

Check out our blog on embedded lending (our speciality) and how it has changed how businesses and consumers can obtain more flexible finance options that traditional banks and lenders have been unable to provide.

Get in Touch

If you want to learn more about partnering with Liberis, feel free to get in touch.

Posted on Notice: Undefined variable: date_format in /code/wp-content/themes/liberis/archive.php on line 374 October 25, 2022 By Kieran Darmody
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