The Future of AI-Powered Personalisation in Embedded Finance
Businesses expect a more personalised experience when looking for the products and services they need. Our latest blog discusses the role of AI in delivering personalised experiences in embedded finance.Return to blog posts
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.
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