Datacultr leverages this data to chalk out changes in customer behaviour which might hint at a potential fraud. With ML capabilities, Datacultr has been able to significantly reduce the magnitude of damage when it comes to default in payments. Every day, users generate tremendous digital information with identifiable patterns on their digital devices. Actionable Insights - providing actionable insights and setting up triggers that can alert them against potential frauds and asset resale. Defaults in consumer lending space have been a matter of concern for years.
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.
Datacultr has helped consumer lending companies in multiple ways:User Behaviour - In today’s world, data holds the ability to define an individual, community and even a country.Educating the borrowers - Datacultrs technology acts as an added incentive for users to repay their loans. The rest are hesitant to lend to first-time borrowers because of the increased risk of default. Datacultr allows China plastic injection machine Manufacturers 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. It allows the lender to block some features of the device user-interface, in case there is a continuous delay in payment by the customer. However, the rise of machine learning can prove to be a game-changer for these banks, Non-banking financial companies (NBFCs), and fintechs.
Currently, there are only a few players in India who are willing to disburse loans to people without any credit rating references. As per a recent report by RBI, the total amount in bank frauds rose 74% to Rs 71,543 cr in FY19. 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.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