Predicting Loan Eligibility in Seconds Using Machine Learning

The Problem

Lending is a huge business area for banks. With a big demand for loans and the number of loan applications increasing greatly, the time it takes for banks to process loan applications is increasing substantially given the limited manual processing abilities. Also, many banks solely rely on credit scores, legacy processes, and tedious paperwork still for processing of loan applications and do not look at other aspects.

It is also a known fact that in many countries, a large percentage of the population does not have credit score. This makes the process of evaluating the loan eligibility even more difficult. Both, established banks and start-ups are looking for innovative solutions to solve this problem.

Solution

Machine Learning models built on Kranium can help banks determine the creditworthiness of a customer. If a customer does not have a credit score, then machine learning models can create one for a customer.

Also, machine learning models can predict the eligibility of a loan application by also considering data from several other sources like the customer’s social media accounts, digital presence, customer’s shopping habits, utility payments and more. AI models are exceptionally fast and can make such decisions in a matter of milliseconds, thereby reducing processing times for loan applications.

Such AI models can also help reduce the time and cost required for processing loan applications. They also contribute greatly towards improving data and risk management.