How Financial Institutions are Leveraging AI for determining Credit Worthiness

Sep 2018 | Team Rubique

There are several macro and microeconomic elements that govern many financial institutions, while several are the factors, which are responsible for bringing these companies or institutions closer to risks. Lending of money is one of the crucial functions of banks and the other financial institutions, which generate ample amount of revenues for them; however, the loans cannot be given to anyone and everyone. While lending a loan to any individual the banks and financial institutions check the creditworthiness of the persona approaching for the same with the CIBIL score, but in most of the cases, a large number of potential borrowers face loan request rejections due to their bad credit score. However, this does not prove that they now fall into the list of defaulters. Ignoring all such clients is not often offer any long-term benefits, as the financial companies or banks would lose their business.

On the contrary, we have industries like healthcare, auto industry, and real estate also face similar issues as the money was expected to be invested in these domains. Hence seeing at the overall picture, the strict CIBL Score is not just the only method to decide upon the creditworthiness of the loan seekers, while to overcome these disadvantages, AI or Artificial Intelligence comes out into the picture. This gives a very precise, quick and practical option to check the payback capabilities of the borrowers. Thus it would be fair to say that the digit credit evaluation system is undoubtedly the real turning point of any industry.

Understanding the way it works

Previously, the credit providing options often considered the credit score in order to carry out any lending decision for any client or firm. The different loan providers often relied on different types of credit models, but at the very core, all such forms of models are seen laid over the foundation of the transaction of the burrower history and details of the payments coming from the banks or financial institutions. Apart from this, these models are seen calculating the credit score from several limited amounts of structured data that offer to a number of tools including decision trees, statistical analysis, and regression.

However, of late, the financial institutions and banks are now relying upon a more holistic kind of approach in credit providing procedure with the help of having a data from unstructured, semi-structured and additional sources including the mobile phone usage, social media, and text message activities, and thus they boost up the accuracy ratings of loans. These can be called as the credit scoring tools in the current market, which apply even the machine learning to ensure the evaluation of all the factors including even the qualitative factors like the willingness to pay and the consumer behavior.  This capability has merely paved the way for quicker, greater and affordable segmentation of borrower quality, which ends up giving the instant and much more precise credit decision.

Greater and Much Better Access to Credit

The use of machine learning algorithms are simply not confined to just developing a precise and segmented assessment of creditworthiness but at the same time has even enabled better and greater access to credit.   In a majority of conventional scoring models, the people coming to borrow the money were supposed to have required amount of historical credit details available in order to be considered scorable. Hence in its absence, the potential clients with excellent credit worthiness failed to get the good credit as they were not able to generate a good credit score. However, thanks to the help of the optional data sources and the newly introduced Artificial Intelligence systems, the banks and financial institutions are not able to reach out to make the right credit decision by judging the capability and interest to repay, which was earlier a big deal to crack.

A Wind of Change 

Since past few years, we have witnessed the growth of FinTech start-up groups, which have embarked in the market serving the customers as per their requirements which failed to get any financial help from banks and similar groups that followed the same old traditional credit systems. Leveraging the AI has given both the merits and demerits in the credit scoring models like the initial phase of the alternative technology would do. However, thanks to the number of benefits attached to the Artificial Intelligence systems, the pros are more than the cons in credit checking and carrying out other similar things. With the AI working for their side, the banks and financial institutions are now competent to study huge amounts of data in no time thereafter adding up the credit risk evaluation for individuals and boosting up several individuals for whim the credit risk can be easily measured.  The classic example of the application of big data and credit scoring could even include the evaluation of the non-credit bill payments like the time to time payments of the utility bills and the mobile phone bills along with the combination of several other data.

Risk minimization for banks and financial institutions

Machine learning can do things that human beings at times can fail to do so. The right example is the precise identifying of the tough investors that work with multiple accounts using the machine learning that seek the help of a predictive study of the huge amount of real-time data.  Adopting the AI can help the bank to digitize the process of credit risk identification and add a feature to offer the overall transformation. The machine learning can be easily executed in the traditional systems to get a more profound understanding of credit health by analyzing and studying the complex and significant amount of data without much worrying about fixing the limits of the standardized statistical analysis.

 Conclusion

In this way, the Artificial Intelligence plays an important role in banks and financial institutions for deciding the creditworthiness of a potential borrower.