Embedded finance is a big change to the fintech world. It has completely changed the way we think about financial services. It’s so popular that you’ve probably used it before, even if you don’t know it. 

Embedded finance is a new way to provide financial services. It uses technology to connect financial products, like payments, loans, and deposits, to non-financial platforms, like company websites and mobile apps. This makes it easier and cheaper for merchants and brands to offer financial services to their customers. 

The driving forces behind embedded finance 

The rise of embedded finance has been made possible by three big changes in the business world: 

• Ecommerce: The rise of online shopping has created new opportunities for merchants to offer financial services to their customers. For example, about one-third of all global card spending now happens online, and half of all card spending in the US happens online. 
• Consumer behaviour: Modern consumers, especially digital natives, want financial products and services that are built into their daily activities. They want a smooth and seamless experience that doesn’t involve having to go to a bank or other financial institution. For example, Which estimates that one in three (33%) people in the UK have used an embedded Buy Now Pay Later product, which equates to approximately 17.4 million people. 
• Technology: Cloud computing and open APIs have made it easier and faster for businesses to adopt embedded finance. 

These three forces are working together to create embedded finance services that can strengthen brand loyalty, increase customer retention, boost conversion rates, and generate repeat business. And the growth is amazing: the global embedded finance market is expected to grow from $264 billion in 2021 to $606 billion in 2025. 

Another technological development that’s fuelling this unstoppable growth is artificial intelligence (AI). 

The transformative power of AI 

The rise of AI has turbocharged the intersection of finance and technology. AI is constantly changing, which makes it possible to create embedded user experiences that are more and more intuitive, natural, and engaging. And the benefits are clear: 

Tailored experiences 

AI algorithms allow embedded finance platforms to personalise user experiences by analysing huge amounts of data in real time. This helps them to understand customer preferences, behaviours, and patterns, and to tailor financial services, such as loans, accordingly. 

For example, AI-powered credit scoring models use machine learning to analyse non-traditional factors, such as reviews, to offer terms and conditions that are tailored to an applicant’s unique financial situation. 

Streamlined customer experience 

Embedded finance platforms can also use real-time data analysis to offer convenient experiences from start to finish. For example, embedded lending makes the loan application and assessment process easier and faster by delivering a frictionless four-click journey that unlocks funds quickly and cost-effectively once AI has expedited the merchant pre-approval process: 

1. See the offer: The lending feature is seamlessly integrated into the brand’s existing customer journey. 
2. Customise the offer: Real-time user experience optimisation customises the lending offer based on the brand’s offerings and your needs.  
3. Confirm details: Your details are processed instantly, and you get an automatic approval decision and offer.  
4. Sign the contract: You accept the offer immediately and get access to the funds almost instantly. 

This convenience removes barriers to access to capital, such as old-fashioned lenders, and gives small and medium-sized businesses the point-of-need access to capital they need to manage their cash flow efficiently. Consumers can also access flexible payment structures that improve their online shopping experience. 

Create test data to train models and assess systems 

By simulating user activity patterns and generating scenarios that mimic the characteristics of real-world situations, generative AI can be used to create synthetic test data for training models and evaluating systems. This diverse and representative dataset enables robust testing and development of embedded finance systems and informs decisions.   

For example, generative AI is a powerful tool for creating synthetic data that mirrors fraudulent patterns. By training generative AI models on vast datasets containing known instances of fraud, it’s possible to generate data that simulates the characteristics and behaviours of fraudulent activities. This can be harnessed to create realistic scenarios for testing and fine-tuning embedded fraud detection capabilities.  

Transparency 

As AI is used more and more in embedded finance, businesses want access to more real-time data to make better decisions. But how accurate is this data? Explainable AI (XAI) is helping to bridge the gap between AI models and user trust in their reliability. XAI is a set of processes and methods that makes AI more explainable, intuitive, and understandable to human users without sacrificing performance or prediction accuracy. 

For example, non-financial businesses that offer lending as part of their customer experience are using XAI to make informed creditworthiness decisions directly within their core applications or products. Liberis offers a revenue-based lending model powered by an intelligent data engine that accurately predicts business transaction revenues and makes personalized and pre-approved offers instantly. 78% of businesses receive their funding in less than 48 hours. 

What does the future hold for AI and embedded finance? 

AI can constantly learn and adjust, so embedded finance will keep evolving in terms of user experience, transparency, fraud prevention, and accessibility of financial services. The progress that AI has already made will continue to advance as AI learns from experience and applies what it learns. 

Generative AI, the technology behind ChatGPT, is taking this to the next level. Generative AI goes beyond traditional AI, which is typically used to analyse data and make predictions, by creating new data. For example, generative AI can be used to create chatbots that can understand the context of a situation and respond accordingly to provide personalised financial advice and support. This is just one example of how AI is making embedded finance more seamless, tailored, and accessible.