The world of subprime lending is at a crossroads. On one hand, it provides essential credit access to a large and often underserved segment of the population. On the other hand, it's an industry with elevated risk, where the line between profitability and loss can be razor-thin. For lenders, the challenge is clear: how do you grow your loan portfolio performance and serve your target customers effectively while mitigating the inherent risks of this complex segment? In other words, how do you make risk proof lending decisions? For too long, the lending analytics has relied on a limited set of tools and metrics for credit risk assessment, with the FICO score being the most prominent. But in today's economic climate, with increasingly diverse borrower profiles, this one-dimensional view is no longer enough to assess credit risk. It's time to evaluate the lending analytics approaches. The lenders who will thrive in…
In industries like Financial Services, Housing, and Insurance, the automated scoring of client risk can provide critical benefits. By quantifying the potential hazards for an organization, risk profiling helps organizations build sustainable long-term growth and minimizes losses in economic downturns. However, traditional client risk scoring methodologies are often not sufficient but can be augmented with machine learning-based models. Client Risk Profile Examples Popular use cases across different domains include: Which of my customers might default on loan payments? (Finance) Can a supplier deliver goods on time? (Manufacturing) Will my new tenant pay the rent on time? (Housing) What are the risks associated with a specific property or driver? (Insurance) Challenges with Traditional Risk Profiling Methods Traditionally, businesses solved these use-cases with rule-based approaches. A banker might have used a credit score and household income to determine loan eligibility. Simple rule-based logic is easy to implement but does not provide enough…
