Abhishek Mehta

Director Field Engineering at TigerGraph

Abhi runs the Sales Engineering team at TigerGraph and has worked with the majority of our customers in the Financial Services, Healthcare and eCommerce domain. He has built his career around Enterprise Software, Graph Databases, Search Engines and hold Patents in Natural Language Processing spanning Conceptunary, Ontology Design, Language Pattern Recognition, and Conversion. Prior to TigerGraph, Abhi has worked at McKinsey, Bloomberg, Cisco & Dabizmo (NLP Startup) as Founder.

Gaurav Deshpande

VP Marketing at TigerGraph

Gaurav Deshpande is the VP of Marketing at TigerGraph. Gaurav brings TigerGraph a proven track record in leading teams in creating new products, establishing new markets, and dominating industries. At TigerGraph, Gaurav’s team has grown the lead base, pipeline, and revenue 10x in three years and racked up over 50 awards including Most Disruptive Startup (Strata 2018), Gartner Cool Vendor 2020, and Leader in Forrester Wave Report for Graph Data Platforms Q4 2020. Previously, Gaurav spent 15 years overseeing marketing for IBM’s Artificial Intelligence, Blockchain, and Cloud portfolios for the Banking and Financial markets, Telecommunications, and Retail. He also built out and positioned IBM’s Big Data and Analytics portfolio, driving 45 percent year-over-year growth. Before IBM Gaurav led two startups through explosive growth, i2 Technologies (IPO), and Trigo Technologies (acquisition by IBM).

WATCH LIVE: December 8 @ 3:00PM – 3:30PM ET

Double the performance of your fraud detection system with graph DB and machine learning

Financial transaction fraud costs hundreds of billions in losses worldwide and affects all industries as online sales became the dominant channel during the COVID-19 pandemic. Current fraud detection solutions are struggling to keep up, especially in detecting online fraud.

Join us to learn how seven out of the top 10 banks in the world are using TigerGraph Graph DB and machine learning to double the performance of their fraud detection system. We will cover how to:

  • Generate machine learning features with every financial transaction by using graph analytics
  • Improve fraud scores, find missed fraudulent activity and, reduce false-positives, in a standard machine-learning pipeline
  • Identify potential fraud and money-laundering rings by digging deeper into connected datasets (payments, accounts, users, devices)