Financial Crime World

ALGERIA SEES GROWTH IN MACHINE LEARNING APPLICATIONS FOR FINANCIAL CRIME PREVENTION

In a bid to combat the rising tide of economic crime, Algeria is increasingly embracing machine learning (ML) and artificial intelligence (AI) solutions. The North African country’s financial institutions are leveraging these technologies to enhance their anti-money laundering (AML) and know-your-customer (KYC) processes.

THE COST OF FINANCIAL CRIME

According to recent estimates, the cost of fraud and AML failure in the UK alone stands at £1.3 billion in 2021, while globally, it is estimated that between $650 billion - $1.7 trillion is laundered annually. Fines for breaching sanctions and failings in KYC systems have also increased significantly, totalling £4 billion in 2022.

THE ROLE OF AI IN FINANCIAL CRIME PREVENTION

Against this backdrop, Algeria’s financial institutions are looking to AI and ML to better protect consumers from the harm caused by financial crime through improved prevention and detection capabilities. The technologies play a pivotal role in detecting suspicious activity through proactive identification of patterns, perpetual monitoring for anomalies, and prescriptive remediation.

Five Ways AI Can Help Manage Economic Crime Risk

1. Improved KYC/Customer Due Diligence

  • Financial institutions are enhancing their automated and digitized KYC/CDD processes using powerful machine learning algorithms.
  • This enables a better targeted approach to risk management.

2. Better Customer Product Suitability Assessment Process

  • Complete and well-defined risk assessment policies and processes are vital in understanding risk exposure and applying controls in a risk-based manner.
  • AI is having an increasing impact on the assessment of risk, with financial institutions developing sophisticated models based on historic customer activity and known indicators of risk.

3. Identifying Authentic Consumers and Financial Institutions

  • The use of AI, such as facial or voice recognition, is a key tool in the evolution of fraud prevention.
  • AI can help detect where a fraudster is impersonating a customer or imitating a bank or third party with the intention of requesting money as part of a scam.

4. Spotting Unusual Activity and Transactions

  • Alongside traditional rule-based transaction monitoring systems, ML has become an increasingly popular method of both detection and post-alert management within transaction monitoring.
  • Financial institutions are now applying increased focus on the development of robust ML algorithms to address the challenges of AML/CFT.

5. More Efficient Operational Enhancements

  • A significant proportion of the cost of a financial transaction can be linked to back-end exception management operations.
  • Post-alert application of AI/ML techniques can better support AML/CTF controls, ensuring resources are focused on the areas of greatest need and controls are applied in a risk-based manner.

THE BENEFITS OF ADOPTING AI

The benefits of adopting AI include increased operational efficiency, optimisation, and better risk management, which can also protect consumers from the harm caused by financial crime through improved prevention and detection. As perpetrators become increasingly innovative, it is essential that financial institutions stay one step ahead by embracing AI solutions to tackle much of the fraud that happens today and could take place tomorrow, reducing costs and time while improving both customer experience and protection.