Artificial Neural Network (ANN) based Fraud Detection & Prevention in FinTech Industry
FinTech has disrupted the financial services industry in many ways such as democratization of lending via peer to peer (P2P) lending, accessibility of capital to underserved or unserved segments, digitization of entire loan processing. In addition, decision making has been brought down from days to within few mins!
However, with faster/agile decision making and digitization of the loan process, the FinTech players are more exposed to fraud as compared to a bank. FinTech players are also hit some of the unique fraudulent activities such as “stacking” or “Ponzi scheme”.
Although statistical techniques such as Elastic Net, Gradient Boosting Tree have traditionally done a good job in identifying potential fraud cases, but with the ever evolving modus operandi of fraudsters we need to deploy self-learning/ deep learning algorithms such as ANN to stay one step ahead of fraudsters!
Ratnakar Pandey has 15+ years of experience in data science and deep learning fields. Currently he is heading the India Analytics and Machine Learning teams for Kabbage Inc where he is leading machine and deep learning models development activity across customer life cycle, from acquisition to customer engagement to fraud prevention to risk based underwriting policy development.
Before joining Kabbage, Ratnakar was part of the data science leadership team in Citigroup, Target, Texas Instruments, and few startups in India. Ratnakar holds an MBA from ISB Hyderabad, MS from University of Arkansas, and BTech from HBTI Kanpur.