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Dr. Wanpracha Chaovalitwongse
Head of Data Management and Analytics
Bank of Thailand
Dr. Wanpracha Chaovalitwongse is the Senior Director and Head of Data Management and Analytics at the Bank of Thailand, where he contributes to the advancement of the institution’s Data and Artificial Intelligence (AI) capabilities. He oversees the Bank’s strategic direction for Data & AI, including governance, innovation, and official statistical reporting.
Speaking At
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1:00–2:30pmWed 17 SepPublic-Private RoundtablePublic-Private Roundtable, (2F), BOT Learning CenterOpenModerator
Others
The Future of Fraud Detection: AI, Data Architecture, and Collaboration
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Dr. Amanah RamadiahHead of Analytics and Client SuccessFNA
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Audrey WongVice President of Financial Crime Solutions and Crypto Services, Services, Asia PacificMastercard
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Nor Halimaton HalimDeputy DirectorBank Negara Malaysia
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Jessica ChuahVP, GrowthCrystal Intelligence
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Kah-Wee LimDirector, Payment Fraud DisruptionVISA
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Dr. Natawat SaigosoomAssistant Managing Director, Innovation BUNITMX
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Dr. Tiravat AssavapokeeExecutive Vice PresidentKasikorn Bank PLC.
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Dr. Wanpracha ChaovalitwongseHead of Data Management and AnalyticsBank of ThailandModerator
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Dr. Supot TiarawutAdvisor on Technology and Digital ManagementMahamakut Buddhist UniversityChair
About this session
Digital financial crimes have caused widespread damage to the public and affected the whole economy. Scammers employ diverse methods and techniques, using bank accounts or electronic money accounts as tools to receive and transfer illicit funds.
In response, the committee under the Royal Decree on Measures to Prevent and Suppress Technological Crime, B.E. 2566 (2023), has approved the Central Fraud Registry (CFR) system for exchanging information about related customer accounts and transactions. The data are utilized to support investigations and legal proceedings against criminals, as well as to monitor the effectiveness of the digital fraud management process.
However, enhancing the end-to-end management approach—from prevention, monitoring, and detection to management and resolution of digital fraud incidents, including assisting affected customers—requires regular review and improvement of detection processes to be more proactive. It also involves implementing data analytics and AI/ML models to better track and detect anomalies. The effectiveness of digital fraud management and the design of countermeasures must be based on empirical evidence, utilizing data to develop in-depth analytical models. -
