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Banks and financial institutions are increasingly under scrutiny for financial crime from governing authorities and regulatory bodies. Money laundering is one of the most common financial crimes committed globally. The Basel Institute on governance, a leading organization on global governance, assesses countries and the world's average risk level of money laundering and gives them scores from 0 to 10. Their latest report estimated the average global money laundering risk score at 5.25 from 5.30 in 2021. This drop, however, could be attributed to a slight improvement in Anti Money Laundering (AML) and Combating the Financing of Terrorism (CFT) systems and regulations by governments and financial institutions. Another reason is that criminals are looking at innovative methods such as cryptocurrency to avoid detection altogether.
The emergence of newer automated AML IT systems could help combat this globally, helping institutions stay AML compliant. Financial institutions and banks have continued to invest in automated AML technology to monitor transactions and conduct background checks. However, it has not yielded the expected results- the Basel AML report also suggests that the effectiveness of AML/CFT measures across all countries fell further in 2022, from its already low level of 30% last year to 29%. The average score required for technical compliance stands at 66% as of 2022, and the global average has fallen well short of it. These existing AML systems are additionally complex and inflexible, further affecting compliance and customer experience and increasing costs. Manual risk assessments, transaction monitoring systems' efficacy, and data quality are the most significant issues plaguing existing AML systems in the banking and finance industry. Advanced analytics and reliance on advanced AI/ML for automated AML systems could be the next big step in combating money laundering in financial institutions.
Virtusa brings together tools, assets, and people in a unique combination that drives >30% efficiency and speed in delivering AML transformation.
Engineering tools
- Smart Requirements- Template and checklists for AML-specific requirement gathering to reduce efforts by up to 25% and increase the accuracy of requirements
- Guideline & best Practices- Regulatory guideline for red flags and threshold setting or scoring logic
- Test Automation- 400 test scenarios automated to run regression
Benefits
Precise requirements measured via RCI
SDLC automation led to accelerated delivery
Industry assets
- AI models for Transaction Monitoring – 30+ pre-trained AI/ML models, synthetic datasets with 10MM customers and 500MM transactions
- Cloud-native middleware – prebuilt microservices, boilerplate code generators, legacy connectors to core product processors
- Model Management Framework – for end users to calibrate the rules and thresholds
Benefits
Domain-driven approach
Spark innovation
Proven models for various use cases
Enabled Teams
- Training – hands-on training on Transaction Monitoring products
- Experts in AML-CDD, EDD, SAM, WLF, and Trade Surveillance OOB packages for Actimize and Mantas platforms
- Solution assembly sandbox – assembles digital solutions from the asset library
Benefits
AML experts
Agile execution
Improved team engagement