Accessibility in Banking services is a significant part of any economy in the world. If money is not lent, it doesn’t move and an economy stagnates. The basis of every economy is in borrowing or lending in order to create more wealth. In India and other major parts of the world, many people can’t access a bank for loans due to several reasons. Let’s look at the problems here and how Data Science can solve some of them.
A big problem when getting banks to approve loans is credit invisibility, you have never borrowed anything before and so banks don’t have any data to form a credible credit rating. Your Credit Rating determines how reliable you are in repaying liabilities and determines the number of loans that you can be approved off. If there is not enough data, your credit rating will be inaccurate or poor and your finances will be affected. Banks in most cases are formal offices that require people to make good impressions in order to get services, this disadvantages the poor who are unable to do so.
In the midst of the Corona Virus, all industries are changing rapidly and fintech is one of the major advancements. There are now many solutions to these problems, let’s look at 3.
Consumer-Permissioned Data
Banks in India operate under KYC law that is connected to the customer’s Adhar card and Pan card. Their credit history is always available to any bank, once a request for a loan is processed. But options become severely limited when there is not much history as previously stated. The solution here is the use of customer permissioned data like bill payments and a history of any bank account including saving history. This gives bankers a more robust view of the financial health of a customer.
Leveraging Mobile Data
Many countries in the world use mobile payment apps like Google pay and Venmo. These apps should also have a lending feature that is easily accessible called micro-lending. It has already been implemented in many 3rd world countries to great results. With it, you don’t need to walk into a bank to borrow small amounts.
Machine Learning
In the past A credit rating was applied through countless hours of manual analysis and calculations, this was prone to many biases like human error or fatigue. These days all reputed institutes use an algorithm that time and again proven to be far more reliable and accurate than manual reviews.Fintech has been making use of AI tools for years now, over $160 billion of digital loans were successfully extended due to these advances and this number will only grow at an increasing pace in the coming years
