Sponsor-led Interactive Roundtable Discussions

Sponsor-led Interactive Roundtable Discussions

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Sponsor-led Interactive Roundtable Discussions

02 Jul 2026
Main Stage

 

Table 1: Eward Driehuis , TheatFabric

Your Customers: Weakest Link or Fraud‑Fighting Super Sensors?  

  • Fraud and APP prevention can be strengthened by treating customers as active partners in scam detection rather than viewing them as the weakest link.
  • New tools such as link-checking bots, in-app call verification, and contextual warnings enable customers to provide valuable real-time intelligence during high-risk interactions.
  • Customer participation can improve fraud detection, reduce the burden on fraud teams, and transform security friction into a source of trust, engagement, and competitive differentiation.

 

Table 2: Manish Kumar Bisht, Concentrix 

How Financial Institutions Are Operationalising Scam and Fraud Prevention in Southeast Asia leveraging Human + AI capabilities 

  • Addressing the rise of scams and authorised fraud across Southeast Asia through AI-powered prevention and stronger regulatory alignment
  • Leveraging AI to manage real-time payments, high alert volumes, and cross-border risks while enhancing customer experience.
  • Transforming financial crime operations with AI, automation, and data-driven models to strengthen detection and response.

Table 3: Jordan Lo, IMTF 

Holistic Anti-Financial Crime - From Functional Excellence to Systemic Effectiveness 

  • Why increasingly interconnected financial crime requires a more holistic and unified compliance approach.
  • How aligning data, workflows, intelligence, and governance across AML, fraud, sanctions, and KYC improves effectiveness.
  • The practical challenges and transformation required to move from siloed compliance functions to a single compliance vision.

 

Table 4: Sanat Paralkar, NICE Actimize

Fraud Risk Futures – New Threats in an Uncertain Market 

  • Evolving scam typologies across SEA 

  • The role of analytics and AI in next-generation fraud defense 

 

 

 

Table 5: AI Deployment in Financial Crime – Real Use Cases & Lessons Learned 

  • Practical AI implementation 

  • Ethical challenges and regulatory expectations 

Table 6: Deploying behavioral intelligence at scale 

  • Tactics, opportunities 

  • Best practice and procedures