The document discusses the challenges of detecting money laundering and its links to terrorism financing, highlighting the need for customized commercial solutions and machine intelligence. It outlines a strategy for utilizing proven concepts, training data, and advanced analytics tools to create a prototype for detecting money laundering in Thailand's specific context. The approach involves collaboration with regulators and financial institutions using a subscription-based model for service delivery.

1. by
Tanat Tonguthaisri, CISSP

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)