Here’s How Technology is Improving The Efficiency Of Credit Underwriting Process

In the digital world, can credit underwriting be conducted to provide a more accurate profile and fill in urgent gaps?

A credit rating system that considered the income of an individual or business as well as the current level of debt and prior loans as the sole basis for lending loans about 10 years earlier. In this scenario, an excellent credit score could increase the probability of receiving a loan in the near future and at a fair interest rate.

But this model ended up ignoring a large part of the population and businesses all over the world by excluding these people and companies. Companies are now steadily getting access to massive information sets that haven’t been previously considered, and reducing risks that could open the way for more clarity and, consequently, more accurate, immediate and effective credit underwriting process through making use of new technology like Big Data and predictive analytics.

What are Big Data and Machine Learning?

Big data and machine learning are the two most vital technologies of the contemporary IT sector. In contrast, machine learning has the capability to acquire knowledge and grow without being explicitly automated.

Big Data” It is a collection of data sets so large and complex that it becomes difficult to process using on-hand database management tools or traditional data processing applications. Storage, curation of data, sharing and transmitting, analyzing and displaying the data all pose challenges.

What was the impact of Machine Learning and Big Data transforming the Credit Underwriting Process?

Traditional credit underwriting relies on the manual process of reviewing financial information and making a subjective judgment about the borrower’s capability to pay back the loan. But, thanks to machine learning and massive data, lenders are able to quickly and objectively evaluate the creditworthiness of a potential borrower.

Big data refers to the vast datasets that are now accessible to lenders. Machine learning is an artificial intelligence that lets computers study data to improve prediction over time. By utilizing these techniques lenders are now able to more precisely assess the risk of a borrower of default.

Machine learning is specifically suited for credit underwriting since it automatically detects patterns in the data that we are unable to detect. For instance, the machine learning algorithm could find that a borrower who has an unproven history of payment in arrears is much more likely to be in default on loans than a borrower who has consistently made timely payments.

The lenders can make better-informed choices about the potential borrowers they will approve of loans and are in a position to provide more tailored loan terms to the individual borrower. This is only the beginning. As the number of data sets grows while machine learning technologies continue to develop and improve, the possibilities for further innovations regarding credit risk underwriting can be enormous.