The financial services industry is a part of the revolution that has Artificial Intelligence (AI) and Big Data Analytics at the forefront. Advanced technologies are opening the door for a transformation within the financial service space, wherein the credit and risk allocation can be made fairer, efficient, inclusive and accurate. When we talk about biases it refers to the discrimination in credit allotment on the grounds of gender, social-economic status, or irregularities in scores as a consequence of varying outputs provided by different Credit Bureaus.

Especially in the context of India's financial sector scenario, there is much that is left to be explored in terms of banking on more robust and reliable credit rating systems that enable financial institutions and borrowers to avoid facing the brunt of irregular, nonstandard and non-reliable credit scoring. AI provides an opportunity to change the status quo wherein existing biases within the credit system can be eliminated while also remaining a possible factor in contributing to

Influence of the algorithm bias in credit ratings on banks

When biases in the credit score ratings sneak in, it causes the decision making of banks regarding the creditworthiness of borrowers to dwindle. Now, the bank's decisions are powered by factors that have no place in their initiatives rather than considering what actually matters. When we look at things within the restraints of the Indian context, the factors that impact the credit score include 'payment history', 'credit card utilisation', 'total accounts' and more, discrepancies in the values of which have been observed from different Credit Bureaus.

• These irregularities and biases from inauthentic source data result in high opportunity costs for banks as they might miss out on prospective customers that would have been a good fit otherwise.
• This bias also hampers the ability of banks to make optimal predictions of borrower's behaviours due to skewed credit score ratings.
• It also impacts the positioning of the financial institutions within the market due to offering a weaker proposition. Now their decisions have been altered by factors that can be deemed inauthentic and frustrating by the prospective customers.

Influence of the algorithm bias in credit ratings on borrowers

Undoubtedly, the most direct impact of algorithm biases in credit score ratings due to irregularities in source data arising on the ground of gender, status, class, race, education or anything else is faced by borrowers. Moreover, this only works to feed a never-ending loop of being stuck in a position of low creditworthiness for specific strata of customers as in the absence of any relevant data regarding them, enabling the algorithm to be sensitive to these biases can be challenging. For Indian customers, the irregular credit scores provided by the different Credit Bureaus can hamper their ability to get the financial help they might need to access.

Conclusion

Facts ' n' Data turns your business data into easy-to-digest and ready-to-use insights to solve your problems related to marketing, sales, customer service, inventory management and more by utilising our advanced analytics solutions and algorithms. Our proprietary Credit Rating Tool empowers banks to better their decision making regarding credit-worthiness of borrowers through reliable, uniform and accurate credit scores.