Banks, Payment Providers and capital markets firms are under intense regulatory mandate to process huge amounts of transaction-related data from both traditional and non-traditional sources. Compliance teams need to constantly analyze data-in-motion (wires, fund transfers, banking transactions) and data-at-rest (years worth of historical data) for actionable intelligence required for Suspicious Activity Reports—to discover illegal activity and provide detailed reporting to authorities. Annual estimates of global money laundering flows ranging anywhere from $ 1 trillion to 2 trillion – almost 5% of global GDP. Almost all of this is laundered via Retail & Merchant Banks, Payment Networks, Securities & Futures firms, Casino Services & Clubs etc – which explains why annual AML related fines on Banking organizations run into the billions and are increasing every year. However, the number of SARs (Suspicious Activity Reports) filed by banking institutions are much higher as a category as compared to the numbers filed by these other businesses. In this presentation we will discuss the business imperatives, value drivers and the woeful inadequacy of current technology architectures and approaches in tackling AML. We will then pivot to a deepdive around Big Data and Predictive Analytics in how they can ease and solve these vexing challenges that Banking executives are grappling with globally.
Sanjay Kumar, GM Industry Solutions - Telecom and FS, Hortonworks