A vast majority of the populations in the emerging markets of Southeast Asia, Latin America, and India are at the cusp of financial inclusion, thanks to the growing availability and adoption of digital lending services. The fintech-as-a-service market is predicted to grow to around US$ 949 Billion by 2028 due to the popularity of the alternative payment solution Buy Now Pay Later in these markets. With increased acceptability for digital lending in segments that had never been a part of the financial mainstream, organizations must enhance risk decisioning while ensuring faster turnaround on credit applications. Maintaining a high rate of credit approvals and managing risk while lending to people with little credit information is a challenge that more and more financial institutions are looking to solve by leveraging machine learning and artificial intelligence. Fintech companies are automating these processes by enriching their machine learning techniques with data and scores that improve predictive risk modeling.

Machine learning plays a pivotal role in effective fraud prevention systems. In this article, learn how machine learning can significantly improve your business' ability to stop fraud attacks. With the recent surge in e-commerce usage globally, the possibility of an online fraud attack is at an all-time high. This would represent an 18% increase in fraud losses compared to the US$17.5 billion recorded in 2020. Customers want to have the assurance that they can purchase products without experiencing a false decline, and merchants want to feel that they can trust the legitimacy of new incoming transactions.