Risk appetite for financial crime risks: a well-needed tool for decision-making
Risk appetite in financial crime. Not an easy one. I wanted to share this interesting article from 塔塔咨询服务公司 which sets the basics quite right: ??Effective Financial Crime Risk Management: The Power of Integration. ??
Aligning risk appetite with financial crime risk controls is essential for financial institutions to effectively manage risks. This integration enhances compliance, strengthens decision-making, and ensures regulatory adherence—all while safeguarding financial stability.
? What is Risk Appetite?
Risk appetite refers to the level of financial crime risks an institution is willing to accept while pursuing its business goals. This strategic decision, made by senior management and the board of directors, helps balance profitability with an institution's ability to manage risk.
? Key Components of a Financial Crime Risk Framework
A robust financial crime risk framework includes:
- ?? Financial Crime Risk Controls: Systems such as KYC, screening, and transaction monitoring to detect and prevent illegal activities.
- ?? Risk Assessment: Continuous evaluation of these controls to identify vulnerabilities and ensure compliance with legal and regulatory requirements.
Integrating these controls with risk appetite allows institutions to create a proactive and dynamic approach to managing financial crime risks.
? Key Benefits of Integration
1. Proactive Risk Management: Aligning risk appetite with financial crime controls allows institutions to anticipate and manage emerging threats before they escalate.
2. Efficiency in Compliance: An integrated framework streamlines compliance processes, reducing duplication and ensuring regulatory efficiency—crucial in today’s high-scrutiny environment.
3. Better Decision-Making: A clearly defined risk appetite helps decision-makers evaluate risks more quickly, enabling better choices around customers, transactions, and growth.
? Challenges in Integration
While integration offers tremendous benefits, it also presents several challenges:
- ?? Regulatory Compliance: Keeping up with constantly changing global regulations can be difficult.
- ?? Operational Complexity: Assessing risks across various geographies and business lines adds complexity.
- ?? Technological Limitations: Integrating legacy systems with modern data analytics tools can be challenging.
- ?? Governance: Clear roles across the three lines of defense—**business units**, risk management, and internal audit—are essential for effective implementation.
Then I asked myself whether I could easily find online templates of risk appetite statements in the field of AML, sanctions, CFT etc….It was not easy so I asked…ChatGpt. And after a few prompts, the result is actually not bad and is in my view a solid basis for reflection. I find some of the metrics a bit theoretical and some thresholds a bit bizarre, but I left it as ChatGpt proposed it.
QUOTE
?? Enhancing Financial Crime Risk Management: Key Metrics & Indicators ??
?? Risk Appetite Overview:
1. ?? Overall Risk Appetite Status
A traffic light indicator showing the institution’s overall adherence to risk appetite, updated daily for real-time insights.
Target: Green (Adheres to risk appetite).
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?? Money Laundering Risk:
2. ?? Suspicious Activity Reports (SARs) Filed
Bar chart that tracks SAR filings and ensures proactive reporting of suspicious activities.
Target: SARs should match monthly threshold. Benchmark: At least 90% of flagged transactions lead to SARs.
3. ?? % of Transactions Flagged as Suspicious
A pie chart showing the proportion of flagged transactions—helping identify areas for closer scrutiny.
Target: Flagged transactions < 2% of all transactions.
3. ?? Average Time to Investigate Flagged Transactions
A line graph that tracks the average time taken to resolve flagged transactions.
Target: Average time < 48 hours for high-risk alerts.
4. ?? Transaction Monitoring False Positives
A bar chart showing the rate of false positives in transaction monitoring.
Target: False positives < 10%.
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?? Sanctions Compliance:
5. ?? Sanctions Screening Accuracy Rate
A gauge chart to measure the accuracy of sanctions screening, ensuring no exposure to prohibited entities.
Target: Accuracy rate ≥ 98%.
6. ?? % of High-Risk Jurisdiction Transactions
A heatmap that tracks transactions from high-risk jurisdictions, focusing efforts where exposure is highest.
Target: High-risk transactions < 1% of total transactions.
7. ?? Sanctions Breaches Detected
领英推荐
A bar chart that highlights sanctions breaches, allowing for immediate remediation.
Target: No more than 1 breach per quarter.
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?? Terrorist Financing Risk:
8. ?? Terrorist Financing Alerts Generated
A line graph showing the volume of alerts for terrorist financing activities, helping to measure early detection systems.
Target: Alerts to decrease by 10% each quarter.
9. ?? Risk-Based Terrorist Financing Transactions
A heatmap that categorizes high-risk terrorist financing transactions for further investigation.
Target: High-risk transactions < 2% of flagged transactions.
10. ?? Terrorist Financing Transactions Detected in Real-time
A real-time alert system for immediate flagging of suspicious transactions.
Target: 100% flagged transactions within 30 minutes of detection.
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?? KYC (Know Your Customer) Risk:
11. ?? KYC Review Compliance Rate
A bar graph to track on-time KYC reviews and ensure timely customer due diligence.
Target: 100% KYC reviews on time.
12. ?? % of High-Risk Customers with Enhanced Due Diligence (EDD)
A pie chart indicating the percentage of high-risk customers undergoing EDD.
Target: 100% of high-risk customers undergo EDD.
13. ?? % of KYC Failures (Documentation Issues)
A gauge chart to track KYC documentation failures, helping to minimize onboarding errors.
Target: KYC failure rate < 1%.
14. ?? Customer Risk Rating Updates
A circular progress indicator to ensure annual updates for customer risk ratings.
Target: 100% customers with risk ratings updated annually.
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?? Regulatory Compliance Risk:
15. ?? Regulatory Violations and Penalties
A table for tracking regulatory violations, ensuring compliance with local and international laws.
Target: Zero violations per quarter.
16. ? Regulatory Reporting Timeliness
A trend line tracking timely submission of regulatory reports to meet deadlines.
Target: 100% reports submitted on time.
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?? Incident Management & Remediation:
17. ?? Financial Crime Incident Response Time
A trend line to track the average resolution time for financial crime incidents, ensuring prompt action.
Target: Average resolution time < 5 days.
18. ? % of Incidents with Remediation Actions Completed
A bar chart showing the completion rate of remediation actions for financial crime incidents.
Target: 100% incidents with completed remediation actions.
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UNQUOTE
Could I have come to the same outcome? Yes, probably but it would have taken me much more time for a result that would not necessarily have been much better. Conclusion: when used with caution, ChatGPT and other generative tools are productivity enhancers that should be complemented by human judgment to transform its output into something actionable.
Curious to see which other statements you would recommend to add to this list.
#ffc #financialcrimecompliance #aml #CFT #antimoneylaundering #sanctions #KYC #compliance #financialcrimefighters
Global Head of BFSI CRO FCRM Advisory Practice
2 个月Olivier, thank you for sharing this interesting AI-generated examples.? While writing the article, I wanted to emphasize the importance of linking the financial crime risk management framework more closely with FI’s risk appetite. This integration should make the framework that’s automatically triggered by events, like changes in the regulatory landscape, geopolitical shifts or entering new markets. Instead of following a strict review and update schedule, assessment of the existing financial crime risk controls should be based on the actual, up-to-date risk-related developments - and adjustment of the risk appetite, thresholds and metrics applied in (near) real-time. I also agree with John Cusack. ChatGPT isn’t quite ready yet. But its current outputs could be a hint at the future LLMs, capable of assessing financial crime risks with greater accuracy and precision, using a much larger dataset than any human can. These focused LLMs (or SLMs) might be able to offer a continuous risk assessment and suggest updates to the financial crime controls and risk appetite on an ongoing basis. That would be a true real-time feedback loop that the industry has been always dreaming of.
Chair, Global Coalition to Fight Financial Crime
2 个月Olivier Goffard - I agree these results are pretty decent and are created much faster than the traditional approach however and you know this but it’s also important to say: - Chat GPT didn’t produce anything - it summarised other people’s work - I’m waiting for a Chat GPT cheque or thank you as are many! - I would love Chat GPT to include the main sources for its results as this would help with taking the results seriously or not and (compensating acknowledging the knowledge - see above) - the results are only useful to the extent an expert can evaluate them as they are still quite generic and basic though and no nuance - you can’t rely on this as a source yet for anything important - they have no opinion or recommendations or the so what which is key - the results are not tailored to your FI - Euroclear is different to many FIs As such I’m confident Chat GPT has a long way to go before it also steals the important piece of what’s crucial and what people like you and me do