AI & ML in Fraud Risk Management

Anurag Jain presented his views on building fraud risk management program using advanced data analytics to detect blind spots and high-risk areas. He highlighted how sophisticated AI/ML algorithms can be used for mitigating fraud to detect synthetic identity, identity theft, account takeover, ACH. and others. He also presented implementation approach for these models.

Mr. Jain also highlighted the need for comprehensive fraud risk assessment program considering both current and emerging risks, which would help identify gaps. This requires assessing variety of fraud scenarios with library of triggers to zone in on high-risk areas. The fraud risk assessment process must also help with

  • Assessing against fraud risk maturity model
  • Strengthening fraud scenario library
  • Improving fraud risk scoring methodology
  • Developing roadmap to plug identified gaps

AI/ML in fraud risk management panel also included Bharat Panchal, Arpit Ratan and Sameer Singh Jaini

Registration link to join my panel –

Some of the areas that Mr. Jain and his team can help with

  • Fraud analytics per business areas / products / services
  • Management Insights using high-tech visualizations
  • Hypothesis testing and scenario simulation
  • Model development for specific fraud scenarios
  • Conduct tuning and optimization exercise to address drifts and data updates
  • Dashboarding and reporting through standard BI tools

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