Applications of Machine Learning in the Identification and Mitigation of Money Launderingby Nikhil Aggarwal
FinTech Entrepreneur in Residence
iValley Innovation Center
Financial Crime Risk is the adverse financial, reputational or regulatory impact from the breach of anti-money laundering, economic sanctions, anti-bribery and corruption laws and regulations. Continued regulatory scrutiny and enforcement has resulted in financial institutions bolstering their controls framework and capabilities. Data mining and machine learning play a core role in delineating and quantifying risk; and making informed policy and control decisions to operate within the financial instruction’s risk appetite. This session outlines the application of machine learning models to transaction monitoring. Analytical insights generated will result in improved risk classification, a more calibrated risk appetite statement, and mitigation of criminal behavior.