by

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 Tonguthaisrihttps://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)