Automated Feature Selection Methods to Assess Client Risk
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 scalability and robustness…
