A modern approach to customer risk assessment with LLMs

At a global bank, the risk assessment process is a critical component of determining whether a customer is eligible for a particular financial product. This process involves evaluating multiple factors, including the customer's creditworthiness, financial behavior and the potential risks associated with offering the product.

Traditionally, this evaluation relies on structured data, such as credit scores, including FICO scores—the dominant credit scoring system—income verification, transaction history and existing debt obligations.

However, the landscape of risk assessment is evolving rapidly, driven by the need for more comprehensive and accurate assessments in a highly competitive market.

When a customer applies for a financial product, such as a mortgage or a personal loan, the risk assessment process begins with data collection. This data includes the applicant's personal information, credit history, income, employment status and transaction records.

The data is then processed by using statistical models and machine learning algorithms to calculate a risk score. This score reflects the likelihood of the customer defaulting on their obligation.

The score is complemented by a thorough analysis of the customer's financial behavior, which considers factors such as recent large transactions, unusual spending patterns and the stability of their income.

In addition to the credit score and behavioral analysis, the risk assessment process also includes evaluating external factors, such as economic conditions, market trends and the regulatory environment. This holistic approach helps ensure that the bank can make informed decisions that not only protect its assets but also align with regulatory requirements.

Generative artificial intelligence (gen AI) represents a significant leap forward in the risk assessment process. IBM highlights that gen AI can analyze market trends, financial indicators and credit histories to provide more accurate risk assessments, aiding banks in making better-informed decisions. This enhanced efficiency, accuracy and transparency in risk assessment leads to improved customer satisfaction and increased revenue.

A primary application of gen AI is creating synthetic data for risk assessment. As explained by the Financial Conduct Authority (FCA), this synthetic data can simulate various customer scenarios, including those relevant to fraud detection, credit scoring and anti-money laundering, enabling banks to test and refine their risk models under different conditions.

For instance, gen AI can produce datasets that mimic the customers' behavior in a recessionary environment, enabling banks to anticipate potential risks and adjust their lending strategies accordingly. This ability to generate diverse and representative datasets helps ensure that the banks’ risk models are robust and capable of handling a wide range of real-world situations.

As a result, banks can make more accurate predictions about customer behavior, reducing the likelihood of default and improving the overall quality of their loan portfolios.

Gen AI's ability to process and analyze vast amounts of data is another key advantage. By integrating gen AI into the risk assessment pipeline, banks can access comprehensive information about their products, risk scores and customer data, including transaction histories and FICO scores. Moreover, gen AI can provide detailed insights into the rationale behind approving or denying customers for specific products.

For example, when evaluating a mortgage application, gen AI can quickly summarize the applicant's credit history, assess their financial stability and compare their profile against similar customers who have successfully secured mortgages.

This level of analysis not only speeds up the decision-making process but also provides a clear rationale that can be communicated to the customer, enhancing transparency and trust.

Gen AI's capabilities extend beyond risk assessment to support product managers and marketing teams in targeting customers more effectively. By setting up multimodal large language model (LLM) pipelines, banks can classify, assess, summarize and explain the rationale behind product recommendations and customer approvals.

For instance, an LLM can analyze customer data to identify patterns that suggest a high likelihood of interest in a particular product, such as a credit card or personal loan. The LLM can then generate targeted marketing messages that highlight the product’s benefits, tailored to the specific needs and preferences of the customer.

LLMs can also provide product managers with insights into which customer segments are most likely to respond positively to certain offers, allowing for more precise and effective marketing strategies.

1.        Risk score interpretation: An LLM generates a summary for a risk analyst explaining the factors contributing to a high-risk score for a mortgage applicant. The summary includes an analysis of the applicant's recent financial behavior, potential red flags and a comparison to similar applicants. This enables the analyst to make an informed decision quickly.

2.        Product recommendations: An LLM analyzes customer data across multiple channels—including transaction history, social media activity and previous bank interactions—to help a product manager. The LLM identifies a customer segment likely interested in a new premium credit card and generates personalized offers highlighting the card's unique features.

3.        Approval rationalization: When a loan is denied, an LLM generates a detailed explanation for the customer, outlining the specific risk factors that led to the denial and offering guidance on how to improve their chances in the future.

Gen AI is poised to revolutionize risk assessment in banking by providing more accurate, transparent and efficient evaluations. By using gen AI's capabilities in synthetic data generation, comprehensive data analysis and multimodal LLM pipelines, banks can enhance their risk management strategies, improve customer satisfaction and drive revenue growth.

As the financial industry evolves, gen AI will undoubtedly play a central role in shaping the future of risk assessment and customer engagement.