Date : 11-Oct-2021
Location: Kuala Lumpur
Organization:
- Bangkok Bank Limited (BBL),the 6th largest bank in Southeast Asia and the largest bank in Thailand by total assets.
- SAS,a leader in analytics.
Offering:
- SAS Anti-Money Laundering solution offers a single platform to replace the bank’s legacy systems with more advanced analytics tools.
- The platform caters deployment of global best practices - based on a global standardized investigation workflow - whilst permitting localisation capability, per each location’s regulatory needs and banking activities.
- The solution enables BBL to apply a more advanced, score-based approach to risk-rate its customers. The bank can apply scenarios and risk factors to detect potential suspicious activity against threshold values specific to each segment based on customer type, risk level, and product.
Spokepersons:
- Suteera Sripaibulya, Senior Executive Vice President of the IT Division at BBL,said,money laundering is a global problem, requiring a global solution.
- Andy Zook, Senior Vice President of Asia Pacific at SAS,said,SAS not only helped to simplify and consolidate the bank’s AML infrastructure and process,they have also deployed an agile implementation process which covers everything from the start of the project, through the monitoring and maintenance of the solution.
Insights:
- AML compliance is a continuous process, and BBL will continue to fine tune and strengthen the bank’s AML operations and capabilities, in close collaboration with SAS.
- SAS Anti-Money Laundering is the third SAS solution deployed at Bangkok Bank, following SAS Customer Intelligence and SAS Fraud Management.
- In recognition of innovation in global AML compliant operations, IDC named Bangkok Bank “Best in Future of Trust” at its inaugural IDC Future Enterprise Awards 2021 in October.
- To learn more about how financial institutions throughout Asia/Pacific and around the globe are leveraging technology to fight money laundering, download the global AML study by SAS, ACAMS and KPMG, Acceleration Through Adversity: The State of AI and Machine Learning Adoption in Anti-Money Laundering Compliance.
Best Practices:
The initiative also demanded other bank-wide changes, including new resources and data sources, as well as new ways of working among compliance, business, and IT. The bank instituted a strong data team to ensure the model functions effectively. Further, a skilled technical team operates data ETL (extract, transport, and load) tools to give enough focus for data-mapping exercises. Seamless collaboration between the IT ETL, data and compliance analytics teams proved essential.
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| AML Solutions apply scenarios and risk factors to detect potential suspicious activity |
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