Credit risk modeling is a major requirement for banks and businesses in the financial sector. With the emergence of technologies like artificial intelligence and machine learning in lending, the aftermath is mostly automated with reduced chances of defaults. Now, businesses can make use of technology to analyze a customer’s financial portfolio, check creditworthiness, and make credit-based decisions.
However, there’s one major requirement for these technologies to provide accurate decisions and work efficiently — user d
Credit risk modeling
There are numerous factors such as the financial health of the borrower and historical trends in default rates, etc. Lending businesses need to evaluate these factors to estimate the credit risk of the borrower. That is where credit risk modeling comes into the picture.
Credit risk modeling refers to the process of utilizing data to analyze – (1) the probability of the borrower defaulting on the loan, (2) impact on lender’s financials if this default happens.
Effective modeling with the data-oriented approach
Traditionally, the credit risk models of banks and financial institutions were dependent on the historical data, that too from limited resources. However, advanced analytical solutions like big data have emerged to automate the whole process and impact how credit risk modeling is done.
Open banking will lead customers and banks to share data through secure APIs to develop an entirely new financial ecosystem. The potential benefits of sharing data in credit risk modeling are substantial.
Deeper insights and informed decisions
Diving into the user financial data is the most reliable way to determine whether to make a loan or not. Data provides deeper insights into the borrower’s financial health and a reasonable basis for extending credit. As in-depth data is available for the businesses now, they can make informed decisions by looking further into the salary history, current debt loan, and past credit performance.
A better understanding of associated risk
Newer technologies have helped to convert data into actionable insights allowing businesses to have a better understanding of associated risk. Open banking might open broader ways for businesses as a vast amount of data will be available. Analyzing data can provide an enhanced view of an individual’s income and expenditure to make accurate forecasts in terms of credit risks. It should also help businesses by minimizing the risk associated with offering loans.
Open banking and data sharing can facilitate a series of services of value to both borrowers and providers. Although the models have been evolved several times in recent years to be more accurate, data sharing could help businesses to get an edge over the past systems. A huge amount of insightful data being available to businesses could enable them to create superior credit risk models.
Wider business opportunities
This point is not directly related to credit risk modeling.
Data sharing could accelerate innovation by letting banks and businesses to build better products and services. These will be more customer-centric and offer more personalized financial assistance. It will help businesses to understand the needs and requirements of the customers and what financial services they might expect in the future. Maybe through data analysis, enterprises could evaluate consumer demands and build advanced lending systems.