The impact of neobanks on the traditional banking model should not be underestimated: they are what Amazon is to the high street and what Uber is to local taxi ranks – a disruptive force that challenges the status quo. At the last count, there were 276 of these challenger banks globally – exponential growth considering the first players only emerged in the wake of the 2008 financial crisis.
This shift from bricks-and-mortar branches housing narrow financial services to customer-centric digital offerings represents an inflexion point for small and medium-sized enterprises (SMEs). By embedding lending into their ecosystem, neobanks are providing SMEs with a frictionless and opportune alternative to traditional banks. No longer reliant on these legacy providers’ rigid lending frameworks, they are being empowered by an innovative approach to lending that delivers finance at the point of need – vital funds that can mean the difference between survival and ceasing to exist.
So, why is embedded lending important to neobanks? How do traditional lending models restrict them? And what risks do face if they fail to move into embedded lending?
It’s easy to say with the power of hindsight that it was only a matter of time before a new breed of provider challenged the traditional banking model by embracing digital channels. But the early trailblazers in the neobanking space faced a huge battle to alter a deep-rooted mindset: banking was the preserve of high street institutions, which consumers – until then – had entrusted with their finances.
Thankfully for them, as the world began to recover from the 2007–2009 financial crisis, a symptom of this global event played straight into their hands: a growing distrust of big banks. Not least by SMEs, who were ready for an alternative to the clunky and restrictive financial services peddled by traditional banks for so long – and neobanks had the foresight to give them what they wanted:
For neobanks to offer SMEs a truly comprehensive digital banking service, they must break down entrenched barriers that have restricted their access to finance when they need it most. To achieve this, they must set themselves apart from legacy lenders by addressing short-sighted assumptions that SMEs are too risky to engage with.
For too long, this myopic view has deprived SMEs of the funding needed to enter the market, achieve financial security, and scale the business. If they can’t get off the ground in the first place due to financial exclusion, neobanks’ other SME-focused services will be futile.
By providing frictionless digital lending services that allow SMEs to access finance at the point of need, neobanks can attract and retain customers that recognise legacy lenders’ limitations. And by working in partnership with them throughout their lifecycle – from starting up to embarking on growth – neobanks can generate sustained revenue.
According to the independent financial comparison website NerdWallet, Tide is the leading neobank when it comes to attractive small business loans. SMEs can apply for a loan that meets their needs in a matter of minutes, with the funds potentially available in around 24 hours. With this service powered by their embedded lending partner Liberis, Tide currently boasts £1bn worth of demand for business finance from SMEs each month.
To successfully augment the lending application and assessment processes with impactful automation requires investment, regulatory knowledge, and technical acumen. Otherwise, neobanks are left to rely on outdated legacy infrastructure that hinders their lending service – including:
Thankfully, neobanks don’t have to settle for outmoded models amid the emergence of a distributed approach to providing access to timely capital: embedded lending. This process of integrating credit or financing products into a neobanks environment has gained traction in response to a growing demand for a frictionless, digital-first lending experience – requirements that align with neobanks core principles.
For neobanks, the benefits of integrating embedded lending options into their technology ecosystem are hard to ignore:
Fintech lending is expected to reach a global value of $27.1bn by 2028, growing at an annual rate of 18.13% – and it’s up to proactive neobanks to tap into demand from SMEs for customer-centric, frictionless lending services. If these digital-first businesses drag their heels when it comes to embracing the benefits of embedded lending, they will expose themselves to some potentially crippling risks:
Traditional lending models act as a roadblock to innovation and agility for neobanks. From cumbersome client support infrastructures to lengthy and tedious application and assessment processes, these outdated systems simply can’t keep pace with changing consumer behaviour, nor can they adapt to changing market conditions.
Revolut Business is a prime example of a leading neobank that doesn’t currently provide business loans. This gaping hole in their offering has caused them to fall behind comprehensive providers like Tide in the neobank popularity stakes.
Neobanks simply can’t afford to overlook embedded lending – not just in terms of generating extra revenue but staying true to their principles as well. These digital-first businesses are built on a foundation of seamless technology, frictionless service, and low costs – a triumvirate of factors that allow them to disrupt traditional banking services. If they fail to embrace embedded lending, they will fall short of this mission statement when it comes to servicing SMEs’ funding requirements – a vital segment of the market that has been underserved by traditional banks.
If they are going to be banking’s answer to Amazon or Uber, neobanks should partner with an expert embedded lending provider like Liberis, rather than attempting to build a platform in-house – saving money, accelerating the time to market, and providing a truly frictionless digital lending experience that empowers SMEs to thrive.
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?
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:
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.
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.
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.
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:
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.
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:
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.
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.
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 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:
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 integration of AI-powered fraud detection and prevention functionality into embedded finance platforms is not without its challenges – including:
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:
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.
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.
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.
If you want to learn more about partnering with Liberis, feel free to get in touch.
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.
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 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.
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:
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:
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:
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.
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.
If you want to learn more about partnering with Liberis, feel free to get in touch.
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.
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.
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.
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:
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:
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.
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.
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.
If you want to learn more about partnering with Liberis, feel free to get in touch.
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.
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 – 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.
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.
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.
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.
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.
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:
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.
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.
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?
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.
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:
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.
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.
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.
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.
If you want to learn more about partnering with Liberis, feel free to get in touch.
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.
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.
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:
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 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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