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Adapting to the New Normal: Combating Money Muling and Financial Crimes in the Post-COVID Era

Fraud, Bribery & CorruptionAdapting to the New Normal: Combating Money Muling and Financial Crimes in the Post-COVID Era

The COVID-19 pandemic has triggered significant changes in both customer and criminal behavior, impacting the financial industry’s risk management frameworks. Criminals have adapted their methods to target individuals as money mules, taking advantage of economic stress and shifting financial circumstances. This article explores the increased prevalence of money muling, red flags to watch for, and the role of artificial intelligence (AI) in combating these new fraud and money laundering typologies.

COVID-19’s Impact on Behavior:

  1. Behavioral Shifts: The pandemic led to behavioral shifts in consumer groups and changes in criminal behavior, including a rise in financial crimes targeting potential money mules.
  2. Economic Stress: Economic stress caused by the pandemic has made both traditionally vulnerable groups and less vulnerable individuals susceptible to money muling.
  3. Accelerated Digitalization: The shift toward remote interactions and digital payments has facilitated money muling and other financial crimes.
  4. Supply Chain Shocks: Supply chain disruptions, exacerbated by events like Russian sanctions, have contributed to black markets, fraud, and trafficking, impacting the global economy.
  5. Continuing Economic Volatility: Ongoing economic volatility and inflation have driven both criminals and honest individuals to seek alternative funding sources.

Detecting Money Muling Risks:

  1. Behavioral Patterns: Money muling recruiters target individuals who behave normally, making it essential to focus on transaction patterns rather than customer profiles.
  2. Transaction Monitoring: Properly tuned transaction monitoring rules are crucial for detecting money muling, as they can identify unusual transaction patterns, such as rapid fund movement out of an account.
  3. Machine Learning: Machine learning can enhance transaction monitoring by dynamically risk-ranking inbound transactions based on customer profiles, reducing false positives and negatives.

Detecting Risk in Different Customer Groups:

  1. Retail Customers: For retail customers, monitoring should focus on turnaround time (how quickly funds leave the account) and known recipients of payments.
  2. Corporate Customers: Corporate accounts can hide illicit transactions more easily. Monitoring should consider the business’s profile, assessing whether transaction volumes align with its size and industry.

Challenges for Fintechs and Neobanks:

  1. Transaction Monitoring: Fintechs and neobanks can benefit from advanced transaction monitoring tools, leveraging AI to detect nuanced risks and rank alerts efficiently.
  2. Communication: Collaborative communication with other firms in the ecosystem can help share insights and improve detection capabilities.
  3. Adverse Media Checks: Conducting adverse media checks for customers, despite limited criminal history, can provide valuable information.

Machine Learning and Traditional Rules:

  1. Complementary Approach: Machine learning and traditional rules can work together effectively, with rules providing foundational knowledge and machine learning enhancing nuance and reducing false positives.
  2. Explainability: Regulators expect firms to retain control and explainability in AI systems to prevent reliance on black box technology.

Second Line of Defense:

  1. Independent Oversight: Second-line testing teams should be separate from the first line of defense, conducting independent reviews to assess the effectiveness of financial crime controls.

Holistic Financial Crime Risk Management:

  1. Complete Risk Assessment: Firms should perform an updated risk assessment covering the spectrum of financial crime scenarios.
  2. Identify Targeted Risk: Identify specific vulnerabilities and controls needed to manage unique risks.
  3. Build Controls Holistically: Develop controls based on the understanding of risk exposure for each product, considering transaction volumes and context changes.

Agile Risk Management Workflows:

  1. Technology: Advanced AI-driven transaction monitoring tools can support agile risk management by constantly learning and adapting.
  2. Governance Structures: Streamlined governance structures are essential to support a fast-moving, risk-based approach while ensuring accountability.

In summary, the financial industry must adapt to evolving customer and criminal behavior by leveraging advanced technology, embracing a holistic risk-based approach, and maintaining effective governance structures to combat money muling and other financial crimes effectively.

By FCCT Editorial Team

Disclaimer: The views expressed in this article are independent views solely of the author(s) expressed in their private capacity.

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