How embedded finance and AI impact the lending sector (2024)

Several trends are currently gaining momentum in lending, transforming the industry and bringing a series of opportunities.

The Belgian lending sector is currently experiencing the influence of several significant trends, driven by the evolving needs and preferences of customers, along with the regulatory landscape. These factors present both opportunities and challenges for the sector, with two specific trends currently gaining momentum.

1. The growth of embedded finance

Embedded finance allows customers to access financial products and services in a seamless and personalized way, without having to leave their preferred digital interface. It is enabled by the collaboration of banks, technology providers, and distributors of financial products via non-financial platforms.This is gaining traction, as more customers demand faster, easier, and more tailored financial solutions. A few notable first-tier banks have integrated their mobile banking app with various third-party services, offering for example mobility and energy solutions.

Embedded lending (EL), a subset of embedded finance, extends loans or credit through non-financial platforms such as retail, e-commerce, or travel services. This approach allows customers to access financing precisely when needed, bypassing traditional financial institutions. The benefits extend to both consumers and providers. For customers, it translates to enhanced satisfaction, and swift access to funds. Simultaneously, providers can identify new revenue streams, obtain insightful data, and fortify customer relationships and increase loyalty. For example, users of one of Belgium’s largest real estate search sites can simulate a loan for their dream home and immediately take one out with a Belgian financial institution.

In 2028, the market is expected to reach approximately €185 billion in Europe alone, with B2B accounting for around 40% of the volume, twice the share observed in 2022. Currently, B2B financing methods in both online and offline businesses are quite similar. However, EL solutions are expected to gain popularity due to increasing digitalization in the market, leading to a compound annual growth rate (CAGR) of about 34%. Although the nominal market volume in 2028 remains higher in B2C, the B2B market is much less pervasive and early entrants can capture significant market share.

The EL industry is currently navigating a challenging market environment, a situation that may persist for quite a while due to higher interest rates and inflation, as well as an uncertain macroeconomic outlook. Additionally, it faces stricter rules and regulations prompted by criticism from consumer advocates regarding insufficient measures to protect against over-indebtedness. This will hamper distribution and increase costs.

On the other hand, there is a growing awareness among customers and an increased demand for flexible payment and financing solutions. Renowned non-FS players, such as Apple, have recently entered the EL market.

2. The role of artificial intelligence

In recent years, and even more rapidly since the launch of ChatGPT in 2022, AI and GenAI have emerged as a game-changers in various industries, and lending is no exception. Key lending activities involve assessing borrowers’ creditworthiness, loan origination, and managing repayment and default risks. AI can help lenders in several ways.

In the area of risk assessment, AI can help analyze large data volumes to predict the probability of repayment. This contributes to more informed lending decision-making, a reduction in the risk of default and an increased efficiency of lending processes.

In credit scoring, AI can play an important role by analyzing credit data to quickly assess creditworthiness, determine appropriate credit limits, and set lending rates based on clients’ risk profiles. This can reduce the time and resources required for manual underwriting, allowing lenders to process more applications within shorter time frames. AI enhances borrower assessment by including multiple sources such as transaction history, alternative financial data, and social media (through large language models). Business plans can even be fed into these systems to allow for more informed decision making in small business loans, as well as provide transparent argumentation when denying a loan application.

AI can help improve customer experience by evaluating a borrower's past spending behavior and credit history, to provide customized offers that are best suited to the client’s personal needs via for example digital assistants. Customers demand a seamless, end-to-end, consistent lending experience that delivers fast decisions and immediate availability of funds. AI can increase customer satisfaction and retention, as well as attract new customers and segments by for example proactively identifying cross- or up-sell opportunities in the client portfolio.

AI systems play a crucial role in supporting innovation and fostering inclusion by introducing new and alternative lending products and channels. Examples include peer-to-peer lending, crowdfunding, and instant lending where AI can improve identification of counterparty risks. This can expand credit access and affordability, especially for underserved and unbanked populations. Additionally, such use of AI can foster financial literacy and education.

Finally, AI systems can be used to monitor and detect fraud, as well as to comply with regulatory and ethical requirements, such as the AI Act. This can enhance the security and trustworthiness of lending, while minimizing the legal and reputational risks.

For banks to fully leverage the benefits of AI in lending, they need flexible, open, real-time, and easily integrated solutions that facilitate the use of external data sources to streamline front, middle and back-office activities. Banks should explore different setups such as a multicloud infrastructure and allow scaling for maximum experimentation possibilities, while also improving their data assets.

It is imperative to employ AI systems that are not only accurate but also explainable to the end user, and able to prevent biases and discrimination in credit decision-making. This approach ensures accountability and responsibility on the part of AI providers and users. To protect the rights and interests of customers, employees, and society, it is crucial to uphold fair and ethical AI systems that respect EU and country-specific values and norms. Lastly, maintaining agility is essential to navigate the rapidly changing environment and capitalize on the opportunities while addressing the threats presented by AI technology.

Reaping the benefits of Embedded Lending and AI

As we navigate through the dynamic landscape of Belgium's lending sector, it appears clearly that change is not just inevitable: it comes with unbelievable potential. The industry is witnessing a transformative wave driven by evolving customer dynamics and the regulatory landscape, offering both challenges and unparalleled opportunities.

Embedded Lending and AI stand out as the vanguards of this transformation, propelling the sector into a new era of efficiency and customer-centricity.

The doors to innovation and strategic growth are wide open. Integrating embedded lending seamlessly into your services, harnessing the power of AI for more informed lending decisions… EY, with extensive experience in both Lending and AI, has the necessary expertise to become your trusted partner in navigating the changing landscape to reap the opportunities ahead.

How embedded finance and AI impact the lending sector (2024)

FAQs

How embedded finance and AI impact the lending sector? ›

Embedded Lending allows timely access to financing, enhancing customer satisfaction, and offers more insightful data to identify new revenue streams. AI has an incredible potential to support lenders with risk assessment, credit scoring and customer experience and it makes lending processes more trustworthy.

How does AI affect lending? ›

Role of AI in Lending

AI, along with machine learning (ML) and Gen-AI, helps financial institutions identify borrowing patterns to reduce the risk of default. By utilizing machine learning algorithms banks can efficiently analyse large amounts of data to evaluate creditworthiness and make real-time lending decisions.

How does artificial intelligence affect the banking sector? ›

AI for corporate banking automates tasks, boosts customer services through chatbots, detects fraud, optimizes investment, and predicts market trends. This increases productivity, lowers costs, and provides more individualized services.

How big is the AI in lending market? ›

AI in Lending Statistics

In 2023, the market was valued at USD 7.0 billion. In 2023, the software segment led the AI lending market, holding a significant 65% share. Similarly, the cloud-based segment dominated with a commanding 70% share of the market.

How AI is used in finance and how it has impacted financial inclusion? ›

AI-driven financial platforms are enabling micro-entrepreneurs and small business owners to access banking services and credit, which were previously out of reach due to strict requirements and limited physical bank branches.

What are the problems with AI lending? ›

AI may face legal and ethical issues, such as transparency, accountability, and explainability, especially when dealing with complex and black-box models, that could affect the fairness and trustworthiness of the lending decisions.

How will AI change the finance industry? ›

AI's Impact on Financial Analysis and Risk Management

Artificial intelligence is also transforming risk management and compliance in the finance industry. By processing vast amounts of data faster than humans, AI systems can detect risks and fraudulent activities that might otherwise go unnoticed.

How big is the embedded lending market? ›

The global market for embedded finance was valued at USD 82.48 billion in 2023. The market is expected to grow at a 32.4% compound annual growth rate (CAGR) during the forecast period of 2024-2032. The embedded finance market size is anticipated to grow to 1,029.02 billion by 2032.

How big is the Generative AI in finance market? ›

Marketresearch.biz reports that the Generative AI in Finance Market size is expected to be worth around USD 27,430.7 Mn by 2033 from USD 1,397.9 Mn in 2023, growing at a CAGR of 35.7% during the forecast period from 2024 to 2033.

What percentage of banks use AI? ›

"32% of financial service providers are already using AI."

What are the positive effects of AI in finance? ›

The benefits of implementing AI in finance—for task automation, fraud detection, and delivering personalized recommendations—are monumental. AI use cases in the front and middle office can transform the finance industry by: Enabling frictionless, 24/7 customer interactions.

How can generative AI be used in financial institutions? ›

Financial institutions are using the tech to generate credit risk reports and extract customer insights from credit memos. Gen AI can generate code to source and analyze credit data to gain a view into customers' risk profiles and generate default and loss probability estimates through models.

What impact will AI have on accounting and finance? ›

Financial Budgeting: AI algorithms can assist in budget planning by considering historical data, market trends, and other relevant factors. Predictive Analytics: By using AI, accountants can perform financial forecasting with higher accuracy, taking various market variables and historical data into account.

What is the drawback of AI in banking? ›

Data Privacy and Security

Banks collect large amounts of data from customers, and AI algorithms require access to this data to function effectively. If sensitive data exist, as is often the case with financial data, any security breach could have serious consequences.

What are the negative effects of AI in finance? ›

2. Data Security Risks. AI systems heavily rely on data, and any vulnerabilities in data storage or processing can expose sensitive financial information to potential breaches. As a CFO, you must prioritize and implement robust security measures and regularly update your AI systems to prevent potential data breaches.

Will AI affect investment banking? ›

AI will change how businesses operate and can transform investment banking, but it won't replace bankers soon. AI may simplify tasks and improve decision-making, but investment banking relies on human perception and connections. AI may eliminate some jobs but generate others. Thus, a complete replacement is impossible.

Can AI make bank loans more fair? ›

AI and empathetic technology can help overcome this. Legacy processes that took weeks can now be completed in minutes to hours to help people make informed decisions. We've been able to eliminate human bias and increase mortgage application acceptance rates by up to 50% for some groups using augmented AI technology.

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