These figures show that, despite a rising demand for credit, lenders, including banks, haven’t been able to keep a steady portfolio of personal loans.
It’s also a wake-up call to lenders to review their credit scoring and underwriting strategies and make them more effective by using modern and innovative techniques like machine learning (ML or artificial intelligence (AI) and bigger data sets.
This article explores how credit scoring is changing and the ways predictive analysis and various technologies are likely to transform it in the coming years.
Table of Contents
What exactly is credit Underwriting?
Credit underwriting is an end-to-end process employed by lenders, such as banks, financial institutions and NBFCs to assess the ability of a borrower to pay back the loan.
Typically, lenders assess the creditworthiness of a person’s borrower on various aspects, including the status of their employment as well as financial stability, debt-to income percentage, the credit score and income.
What is a credit score?
Credit scores are a crucial indicator of the consistency of a borrower’s repayment loans. Credit scores are calculated by licensed credit information organizations – The Credit Information Bureau (India) Limited (CIBIL), Equifax, Highmark, and Experian.
The CIBIL score happens to be the most sought-after credit score of the mentioned credit info agencies.
Credit Scoring and Underwriting: Future Outlook
The growing amount of NPAs, especially within the mortgage sector as well as the substantial amount of people who are not banked, could be a wake-up call for lenders looking to ensure their presence within the financial market.
Alternative Data
Despite achieving the highest financial performance over the last year, Indian banks continue to come up with solutions to an issue that has been around for a long time – financial inclusion.
The absence of information from traditional credit sources has forced financial institutions to study other sources of data to increase their capacity to make decisions about credit.
Experian reports that approximately 62% of banks are using alternative data sources including transactionsal data, social network data, rent payments and digital behavioral data.
It allows them to offer credit options to a larger population and conduct customized risk assessment.
In the simplest terms alternative data could assist lenders in reaching potential customers who have been excluded due to a absence of credit history or restricted access to traditional credit services.
Credit Assessments using Machine Learning
About 67% of the population of India of 938 million have benefited from personal loans during their lives for a variety of reasons, such as buying an apartment, traveling or remodeling.
Traditional scoring and credit assessment techniques hinder the ability of lenders to gather, analyze and make informed credit decisions. In this case machine learning algorithms are revolutionizing credit scoring, providing precise assessments of creditworthiness.
They also manage large quantities of non-traditional information like social media, internet-based behavior, utility bills and rent as well as utility payments to identify important patterns and trends, opening the way for more informed loan decisions.
Another major benefit of making use of machine learning algorithms for credit score is the unparalleled efficiency and ability to process massive amounts of data in a matter of minutes, allowing for immediate credit scoring decisions.
A lot of digital lenders are advocating for loan underwriting that is paperless and speeding up the process of data collection by using open banking sources, such as APIs.
Accuracy, Fairness and Predictive Accuracy Improved
In addition to providing quicker credit decisions algorithms for credit scoring based on AI use a mix of real-time as well as historical data to boost the predictive abilities of a lender.
A combination of predictive AI as well as traditional credit assessment techniques allow lenders to identify relationships between various variables like spending patterns, geographical location and repayment history to gain more insight into the financial behavior of a borrower that can lead to more precise and accurate credit forecasts.
This method assists lenders in making credit available to those who the traditional scoring system for credit has have ruled out and do not have official lending establishments in the remote regions of India.
Additionally, because AI is able to access a greater variety of data sources that it can play a significant part in reducing biases which are common within credit score systems which mainly depend on traditional data sources like income, collateral and credit history which don’t include the under-served or unbanked population.
Parting Notes
It is evident it is evident that AI as well as machine-learning are revolutionizing the credit scoring ecosystem throughout lending institutions in India. They are laying the foundation for accurate, fair and efficient credit scores through the use of alternative data and real-time evaluation.
Financial institutions that make investments in AI and use technologies to increase credit score will be better placed to increase the financial inclusion of their customers and reduce risk.
Finesse’s loan origination software is specifically designed for lenders who are looking to simplify their lending procedures and offer better customer experience.
With features such as Automated Document Identification as well as 360-degree customer profile Assessment and a plethora of credit Assessment tools The loan origination system is a complete solution that aims at speeding up and creating a smooth loan process.
