Fraud as we know it has existed in some or the other form since the concept of monetary transactions came into existence. As part of this session, we would discuss about classifying frauds into different categories and mechanisms to tackle them using behavioral science, honeypots & various other methods in a man-machine ecosystem.

Outline

1. Introduction to Fraud
   a. A brief history on fraud

   b. Differentiating Fraud from the noise

   c. Various examples of fraud that we see

2. Detecting Fraud
   a. Detecting Identity Fraud

   b. Detecting Behavioural Fraud

   c. Detecting Opportunism

3. Designing products to add better signals for classifications

4. Actions post detecting a suspicious behavior

Speaker bio

Raj Vardhan has about 7 years of experience working with Fortune 100 companies as well startups across the globe consulting on data, tech and product. He is currently the lead data scientist and head of analytics & Inferences at Simpl. In addition, he also looks into the consumer facing part of the product along-with consumer centric risk. In his last 3 + years at Simpl, he has been involved in building the underwriting models, fraud detection mechanisms and the in-house ML platform for at-scale training and deployment of models.

His major interest lies in working with businesses on Data Science Adoption, behavioral sciences and designing intelligent product flows.