How AI is helping the Fintech industry by reducing loan dropout through automation?

How AI is helping the Fintech industry by reducing loan dropout through automation?

The fintech industry is going through a massive revolution right now. This disruption is helping customers get easy access to credit, which has made payments and transactions hassle-free, like never before. All this is possible, thanks to innovations in technology like open banking, along with the boom of AI and Machine Learning. Young India is credit hungry and their spending per capita has been consistently growing. Until a few years ago, customers used to visit the bank branch, provide the required documents physically and wait for a minimum of 15 days to get credit or loan. Banks used to take a long route to process the document, get the KYC done through personal visits, perform a credit risk assessment and then approve loans. In stark contrast, banks and lenders are now facilitated to lend money within a day rather than days. This has made the whole lending cycle shorter and more within the reach of the common man.

The Fintech space has got a complete makeover by digitalization and open API and Machine Learning integration. Lenders can process loan applications, perform e-KYCs and credit appraisals, assess creditworthiness, and process the loan amount in just a few minutes. This has opened a plethora of options for those seeking credit. Millions of customers are applying for the loan every month but only 10 to 15 % are able to complete the loan application journey and finally, only 2 to 5 % of them can secure the loan. 

The loan dropout happens at both pre-processing and post-processing stages. Pre-processing stages are namely – filling out the application, getting an offer, providing KYC documents, providing account statements, income tax returns, etc. Post-processing stages consist of credit appraisal, credit decision, and finally loan disbursement. Various factors lead to loan dropout at different stages: Either the customer is not completing the application or he/she is not able to provide the necessary documents, is not up to the risk score criteria, is price sensitive, etc. 

With a significant loan drop at every stage, the leak of customers during the loan application journey has become costly for digital lending companies with the rise in customer acquisition costs. This is where AI-driven intelligent automation systems are helping financial institutions not just automate the whole process, but also significantly reduce the cost burden on financial institutions and even assist the customer to make an informed decision during their loan application journey. 

Moreover, it is a very tedious process for lending companies to do all the homework using the proficiency of credit risk managers, credit policy makers, legal resources, and a whole team to review the documents provided by customers and still falter. Given the bulk of applicants in this digital age, it’s humanly impossible to review the documents, assess the risk, measure the credit worthiness, and eventually make the right decisions, with a minimal amount of risk. 

To turn around this challenging situation, AI and ML-based intelligent automation systems have been developed and deployed to process these humungous number of documents, classify anomalies, evaluate payment behavior and pattern, assess credit worthiness, and automate risk decisions. AI is empowering the credit risk manager to understand the persona of each customer and risk behavior in a rather scientific approach and provide causation of credit risk. 

AI is helping lenders to predict customer loan drop-out probability which can help in filtering out good applicants to optimize the funnel and eventually lead to targeting quality customers as well as improving the entire application process. 

The overarching AI model helps in predicting customers who are highly likely to get their loan approved after completing loan applications and get a pattern for quality applications. This is facilitating lenders to identify quality customers upfront and hence put all their focus on assisting these customers to improve conversion rates and reduce loan drop out.  This also brings down customer acquisition costs to a great extent. 

AI-backed intelligent automation also helps the lender to predict customers who are likely to drop out in the digital loan application journey at different critical stages of the loan application journey like avail offer, KYC, document upload, etc. The powerful combination of AI and Automation gives a unique customer treatment strategy and assistance to prevent this leak of loan dropout. With this knowledge, the lender can now optimize targeted customer campaigns and call center efforts.

Adopting digital technologies like artificial intelligence in loan application management can help banks restructure the customer journey, deliver efficiencies and free up employees for value-added services.

Images Powered by Shutterstock