In order to make loan decisions, financial institutions and other lenders need an accurate picture of the likelihood that consumers will default on their debt. This requires calculating and assessing multiple factors affecting a borrower such as current level of indebtedness and past borrowing history as well as economic trends or any unexpected events that could arise.
An effective credit risk model can prevent losses and defaults, allowing lenders to provide loans only to people who can pay back their debt in an appropriate time frame. An inaccurate or biased model, however, can lead to discriminatory lending practices and regulatory fines; to prevent this happening lenders must ensure their models are transparent and fair.
Lenders can enhance their credit risk models by incorporating new data and analytics, and taking advantage of technological innovations. Machine Learning (ML) can identify patterns within large datasets to determine whether a consumer’s creditworthiness is low or high; additionally, this process automates credit decisioning processes to increase approval rates while saving cost while freeing resources up for more crucial tasks.
Machine learning (ML) has quickly become one of the go-to approaches in finance because it enables companies to develop and deploy credit scoring models more quickly than with traditional approaches, with more accurate assessments of consumers’ likelihood to default than existing models. But ML should only be seen as one method used to predict credit risk; regression and clustering techniques should also be combined for optimal results in providing a complete picture of each customer’s creditworthiness.
Financial institutions should make use of credit risk models that are robust and consistent across all markets, giving them the confidence to lend to more consumers during periods of economic instability. It’s also crucial that credit risk models be capable of rapidly adapting to changes in global events or consumer behavior so that real-time analytical insights can be delivered through real-time credit risk models.
An understanding of credit risks within a portfolio is paramount for lenders who wish to successfully manage capital reserves and allocate loan loss reserves appropriately. Without it, they risk significant losses which could damage both their organization and shareholders financially.
Financial institutions utilizing an analytics platform can quickly develop and deploy new credit risk models while optimizing decision making strategies, keeping their lending rates competitive while meeting customer needs while remaining profitable. To learn how lenders can utilize machine learning (ML) for predictive accuracy in financial services, download our white paper: “Get AI Decisioning Right in Financial Services.”
