Datacultr, a leading Platform-as-a-Service(PaaS) player has helped reduce the risk of fraud for some of India’s leading consumer lending companies by a significant percentage. Its first-of-its-kind platform has been a key enabler for these companies by significantly reducing the default rates with the help of Machine Learning.
Currently, there are only a few players in India who are willing to disburse loans to people without any credit rating references. The rest are hesitant to lend to first-time borrowers because of the increased risk of default. Defaults in consumer lending space have been a matter of concern for years. As per a recent report by RBI, the total amount in bank frauds rose 74% to Rs 71,543 cr in FY19. However, the rise of machine learning can prove to be a game-changer for these banks, Non-banking financial companies (NBFCs), and fintechs. With ML capabilities, Datacultr has been able to significantly reduce the magnitude of damage when it comes to default in payments.
Datacultr allows vast amounts of data to be handled in a short time, helps to manage both structured and unstructured data, a task that would take too much time for a human to do. Datacultr has helped consumer lending companies in multiple ways:
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User Behavior – In today’s world, data holds the ability to define an individual, community and even a country. Every day, users generate tremendous digital information with identifiable patterns on their digital devices. Datacultr leverages this data to chalk out changes in customer behaviour which might hint at a potential fraud.
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Actionable Insights – providing actionable insights and setting up triggers that can alert them against potential frauds and asset resale. It allows the lender to block some features of the device user-interface, in case there is continuous delay in payment by the customer.
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Educating the borrowers – Datacultr’s technology acts as an added incentive for users to repay their loans. It makes borrowers aware that timely repayment helps build their credit score which in turn can get them access to big-ticket size in the future.