Digitization in Credit Risk Management

Financial sector is becoming increasingly competitive and facing several threats from various areas. Threats like, those coming from regulators, the expectations of investors, emergence of new competitors, expectation of customers who expect to access funds and make loan requests through a multitude of digital channels, all put the lenders in weak position.

Smart lenders minimize their risk by knowing exactly what they are getting into, and how predictable they can forecast activity on the loans. To boost the quality of the overall loan portfolio, lending firms need to reset their value focus and digitize their credit risk process.

Digitization offers huge potential to improve credit risk management. It is likely to result in more transparency of risk assessment. It allows lenders to make rapid assessments of the credit worthiness of applicants quickly.

Digital trends in financial industry

Changing customer expectations
Tighter regulatory control requiring greater risk function effectiveness
Growing importance of strong data management and advanced analytics in staying competitive
New attackers driving business-model disruptions
Increasing pressure, especially from financial-technological companies, on costs and returns

Digitizing credit risk management allows lending firms to withstand new pressures and create value. It can bring value in areas like sales and planning, mortgage process and insight and analysis. Digital credit risk management uses automation, connectivity and digital delivery and decision making to create values in protecting revenue, reduce cost of risk mitigation and reduce operational cost.

Lenders are beginning to respond to these trends, albeit slowly.

Analytics in enabling “digital credit risk”

To stay competitive, banks need to use data and analytics effectively to gain insights. Analytical techniques enable the implementation of credit strategies and workflows for decisions and risk monitoring. While analytics provide deep insights about customers’ behavior, it also apprises financial institutions with factors that have influenced their changing buying patterns and habits.

Predictive analytics can enable credit managers to reduce the lending risk by making data driven decisions. Using statistics and machine learning techniques, they can analyze the data available from various sources to create credit scoring models on their own, specific to their business. These models incorporate financial and non-financial data such as demographic and profile information to do forward-looking analysis of the probability of default for a borrower over various time frames, and calculate the potential expected loss in case of default.

In fact, Predictive analytics can be utilized to improve the customer experience throughout the loan life cycle. Marketing departments can benefit through improved targeting in their campaigns, and credit risk departments can create scorecards to make more informed decisions on whether or not to accept an application. Opportunities for cross-sell and up sell can also be identified by analyzing the behavior of existing customers and by assessing the risk of default, proactive actions can be taken to mitigate the risk early. Collection analytics can predict the likelihood of delinquent customers paying back the debt and the right channel to reach out to these customers. This would not only help in increasing the interest revenue, but also in reducing the collection costs.