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Tanat Tonguthaisri, CISSP
 Money laundering is hard to detect.
 Spread throughout government & local
municipalities like cancer.
 Lead to Terrorism Financing.
 Commercial Solutions are costly.
 Off-the-shelf AML products need
customization with local know-hows.
 Machine Intelligence to detect Money
Laundering and counter Terrorism
Financing with human friendly reporting.
 Use proven concepts from other regulators
(outside of Thailand).
 Applying to domestic scenario of
separatism in the Deep South and local
corruption.
 https://fb.com/Watchdog.ACT/
 https://fb.com/groups/OpenDataASEAN/
 DataKind experts
 Use training data from fintechsandbox.org and use
DataRobot to create a prototype
 Obtain test data from regulators
 Store in Hadoop / Apache Spark using Cloudera
Enterprise
 Prep data using Alteryx
 Feed them to DataRobot to find rankings of more
complex ML models
 Visualize findings and generate reports with Tableau
 Add narration with Quill and Wordsmith
 Use Splunk for ad hoc queries
 Financial Institutions
 Commercial banks with presence in Thailand.
 Regulators
 BoT: Bank of Thailand
 MAS: Monetary Authority of Singapore
 FCA: Financial Conduct Authority (UK)
 ASIC: Australian Securities & Investments Commissions
 FINRA: Financial Industry Regulatory Authority
 ESMA: European Securities and Markets Authority
 IOSCO: International Organization of Securities Commissions
 FINMA: Swiss Financial Market Supervisory Authority
 BaFin: Federal Financial Supervisory Authority (Germany)
 Subscription model for financial institutions
 Long-term Enterprise Licensing for
Regulators
 Tanat Tonguthaisri
https://LinkedIn.com/in/epicure
 Tools
 Cloud services (AWS, Azure, or Google Cloud)
 Cloudera (Big Data via Apache Spark / Hadoop)
 Alteryx (Data Preparation)
 DataRobot (ML automation)
 Tableau (Visualization)
 Splunk (ad hoc querying & Hunk for Hadoop)
 Narrative Science (Quill for narration)
 Automated Insights (Wordsmith for narration)
Siam RegTech

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Siam RegTech

  • 2.  Money laundering is hard to detect.  Spread throughout government & local municipalities like cancer.  Lead to Terrorism Financing.  Commercial Solutions are costly.  Off-the-shelf AML products need customization with local know-hows.
  • 3.  Machine Intelligence to detect Money Laundering and counter Terrorism Financing with human friendly reporting.
  • 4.  Use proven concepts from other regulators (outside of Thailand).  Applying to domestic scenario of separatism in the Deep South and local corruption.  https://fb.com/Watchdog.ACT/  https://fb.com/groups/OpenDataASEAN/  DataKind experts
  • 5.  Use training data from fintechsandbox.org and use DataRobot to create a prototype  Obtain test data from regulators  Store in Hadoop / Apache Spark using Cloudera Enterprise  Prep data using Alteryx  Feed them to DataRobot to find rankings of more complex ML models  Visualize findings and generate reports with Tableau  Add narration with Quill and Wordsmith  Use Splunk for ad hoc queries
  • 6.  Financial Institutions  Commercial banks with presence in Thailand.  Regulators  BoT: Bank of Thailand  MAS: Monetary Authority of Singapore  FCA: Financial Conduct Authority (UK)  ASIC: Australian Securities & Investments Commissions  FINRA: Financial Industry Regulatory Authority  ESMA: European Securities and Markets Authority  IOSCO: International Organization of Securities Commissions  FINMA: Swiss Financial Market Supervisory Authority  BaFin: Federal Financial Supervisory Authority (Germany)
  • 7.  Subscription model for financial institutions  Long-term Enterprise Licensing for Regulators
  • 8.  Tanat Tonguthaisri https://LinkedIn.com/in/epicure  Tools  Cloud services (AWS, Azure, or Google Cloud)  Cloudera (Big Data via Apache Spark / Hadoop)  Alteryx (Data Preparation)  DataRobot (ML automation)  Tableau (Visualization)  Splunk (ad hoc querying & Hunk for Hadoop)  Narrative Science (Quill for narration)  Automated Insights (Wordsmith for narration)