Using AI to Improve Consumer Lending Outcomes and Drive Financial Inclusion

As a data scientist and advanced analytics consultant to the banking industry, I’m fascinated by modern AI tools. The increased adoption of AI and machine learning has completely revolutionized consumer lending: specifically, the way consumer loans can now be priced and offered. AI is a complete game-changer – now giving pricing teams a much deeper, more complete view of their lending portfolios and empowering them to influence more consumer-centric, strategic outcomes.

There are several distinct ways that AI-powered pricing analytic tools can help pricing teams meet their growth, profitability, regulatory compliance, customer financial inclusion, and other strategic objectives. Let’s explore some of these in more detail.  

Price optimization: Delivering the Best Outcomes for Consumers

Customer expectations in terms of credit provisioning have evolved significantly, leading to considerable disruption and innovation in the consumer lending sector. Yet, consumers are dealing with increased living costs and interest rates that have remained at a 15-year-high since the start of 2024 in a continued effort to curb inflation.

When it comes to seeking credit – be that unsecured loans, credit cards, or car finance – consumers now have more choices, but they also have more nuanced needs. 

Today, AI-driven strategies can factor real market, competitive, and customer data and many other sources of information, making it possible to test models and adjust the results accordingly. This lets you develop AI-powered risk-based pricing aligned with a particular customer profile or current market conditions.

Better price optimization can deliver a real win-win: The increased likelihood of loan acceptance and approval and a new foundation for a long-term, profitable relationship with individual consumers.

Increased Loan Personalization: Provide Better Service for Overlooked Customers

In consumer lending, loan personalization is about delivering the right amount with the right terms to the right customer at the right moment. AI has already had a transformative impact on this process by considering these variables and setting the parameters of the loan.  

Analytical modeling is also a powerful instrument in identifying and servicing marginalized customers whose needs may be overlooked by traditional underwriting processes. For instance, in consumer lending, banks can now offer personal unsecured loans for small amounts to customers with a thin credit profile or lower credit score.

When determining the interest rates (and fees) for such loans, several operational obstacles are evident in traditional decision-making processes.

• First, an accurate evaluation of the risk of default for these customers might not be available to the bank or finance provider if they’ve been bundled together with other customers requesting slightly larger loans and/or with better credit scores.

• Second, loan quoting engines for different channels might not support the level of granularity needed to enable differential pricing and underwriting for such specific segments. This is especially true if the criteria by which segments are defined will vary over time (for example, the level of average disposable income over the last three months).

• Finally, understanding the customer’s true repayment ability might be difficult if similar types of customers were excluded from loan models in the past.

Many banks have already reported impressive results using such an approach. At one UK bank, real-time personalization contributed to a 300% increase in loan sales among mobile users, and a leading European bank reported a 9% volume increase in unsecured loans in the first year alone. 

Improved Compliance with Consumer Lending Laws

The use of AI tools can also support compliance commitments. Credit providers in the U.S., UK, and many other countries are dealing with intense regulatory pressures.

For example, last year’s rollout of the FCA's Consumer Duty standard – described as possibly the most extensive piece of regulation in terms of defining the relationship between customers and financial services providers – has led to a renewed focus on how pricing is calculated.

In the U.S., Fair Lending laws that focus on financial inclusion, such as the Equal Credit Opportunity Act (ECOA) prohibit discrimination in lending based on race, color, religion, national origin, sex, marital status, age, or other factors. These laws are designed to ensure that all individuals have equal access to credit and financial services, promoting fairness and equality in banking/lending markets. .

To comply with these regulations, lenders need to be able to demonstrate that customers are paying a reasonable price (fair value) for the product they’re taking and also that the product is aligned with the market and fits the customer’s specific financial profile. This highlights the need for greater transparency in terms of how prices are set so that customers can understand the products and rates offered.

AI will play an increasingly important role in compliance. For example, AI-driven pricing tools can help credit providers meet individual consumer needs, consistently and systematically, while leading to overall greater customer satisfaction, building trust, and supporting long-term relationships. 

AI-driven technology has matured to a point where pricing managers are able to implement quick adjustments and better personalization strategies for products and services such as interest rates and fees. These modern AI tools and models directly impact the product catalogs, and their pricing, and are integrated with the main systems within the bank.

AI Pricing Tools: Early Benefits with More Results to Come

In my role as a data scientist, I keep witnessing how modern AI technology can offer better visibility and data-driven insights to decision-makers within banks. 

AI-powered pricing analytics and other tools can lead to increased transparency in customer communications, increased personalization and compliance, and greater perceived value in the interaction between the bank and the customer. Yet like any technology, I’m confident we’ll continue to see even more impressive benefits as the industry continues to develop more innovative approaches in the future.

To find out more about the real-life applications of AI-based analytics in consumer lending, visit https://earnix.com.

About the Author

Giovanni Oppenheim is a Director, Banking Solutions at Earnix – a global provider of AI-driven dynamic pricing, product personalization, and digital decisioning solutions. For almost a decade, Oppenheim has headed up the delivery of analytics and implementations for global financial institutions at Earnix, including consumer lending, car finance, cards, mortgages, and more.