Financial Services

Why high-quality data is crucial to fighting financial crime

Greed's powerful allure can corrupt both individuals and systemic structures, pushing them toward illicit means for accumulating financial resources. These unethical practices reveal themselves in a variety of ways, including:

• Manipulating stock prices for personal gain.
• Falsifying loan applications to secure funds with no intention of repayment.
• Disguising the source of illegally obtained money to make it appear legitimate (money laundering).
• Stealing personal information to gain unauthorised access to accounts (identity theft).
• Submitting false claims to obtain insurance payouts illegally (insurance fraud).

And this is just a glimpse. Financial crimes today are committed in a myriad of ways. Having transcended simple theft, they have evolved into highly sophisticated, often global schemes. While these crimes may seem less overtly "violent", their impact can be devastating, leading to significant losses for individuals, businesses, and entire economies.

Financial crime places a heavy economic strain, with rising costs for investigation and remediation. In 2023, 16% of surveyed organisations reported spending $25 million or more addressing such issues. Key figures from the year underscore the scale of the challenge:

• Global financial channels were tainted by $3.1 trillion in illicit money.
• Trillions were funnelled into destructive activities through money laundering.
• Financial fraud caused projected global losses of $485.6 billion.

Unfortunately, these crimes are projected to increase over the next year, with 67% of global executives and risk professionals anticipating so. This rise is primarily driven by criminals constantly evolving their tactics and exploiting advanced technologies. Paradoxically, these same technologies offer avenues for defence, with artificial intelligence (AI) emerging as a key player.

AI in financial crime prevention

Many organisations are increasingly turning to AI to prevent or address financial crimes. A 2024 survey by Biocatch underscores this trend with these figures:

• 74% of surveyed organisations are currently using AI for financial crime detection.
• 94% of organisations are using AI/ML techniques to detect risk from user behaviour.
• 87% report that AI has increased the speed of their organisation's response to potential threats.

While AI's role in combating financial crime is increasingly recognised, the critical importance of the data fuelling these systems often goes unnoticed. The performance of AI models relies heavily on the quality and structure of the data used to train them. Poor-quality data significantly diminishes AI's effectiveness and potential.

As conversations about AI evolve, it is essential for financial institutions to prioritise data quality when integrating AI into their business strategies.

The significance of high-quality data

“More data beats clever algorithms, but better data beats more data.” — Peter Norvig

This quote underscores the critical role of data quality in AI and machine learning. While having large datasets is beneficial for training robust AI models, the quality of that data significantly outweighs the sheer quantity.

High-quality data—defined by its accuracy, completeness, consistency, and relevance—serves as the cornerstone for building effective AI-powered financial crime solutions. Here is what quality data leads to:

• Accurate detection: High-quality data enables financial institutions to create more precise risk profiles and detection models. When data is complete, accurate, and up-to-date, ML algorithms can more effectively distinguish between:
  • A legitimate large transfer to a family member
  • A potentially suspicious international wire transfer that matches known money laundering patterns.
  
  
• Reduced false positives: Precise, well-structured data allows for more nuanced risk assessment:
  • More accurate customer risk profiling
  • Context-aware transaction screening
  • Dynamic risk scoring based on comprehensive data points

This helps reduce operational costs, improve investigative efficiency, and minimise unnecessary disruptions to legitimate customer transactions.

• Enhanced customer due diligence: Comprehensive and reliable data allows for more thorough customer background checks and ongoing monitoring. Financial institutions can:
  • Verify customer identities more accurately.
  • Assess potential risks associated with specific customers.
  • Create more nuanced risk categorisations.
  
  
• Improved regulatory compliance: High-quality data is crucial for meeting stringent regulatory requirements like:
  • Know Your Customer (KYC) regulations
  • Anti-Money Laundering (AML) compliance
  • Sanctions screening
  • Suspicious Activity Reporting (SAR)

Financial crime compliance is non-negotiable.

• Effective investigations: When a potential financial crime is detected, investigators need access to reliable data to understand the full scope of the activity and gather evidence. Detailed, reliable data provides investigators with:
  • Clear audit trails
  • Comprehensive transaction histories
  • Interconnected relationship mapping
  • Contextual information for deeper analysis

This enables a more thorough and efficient investigation.

• Advanced predictive analytics: High-quality data supports sophisticated predictive models that can:
  • Anticipate potential financial crime trends
  • Identify emerging fraud techniques
  • Develop proactive prevention strategies
  • Continuously refine detection mechanisms
• Cross-institutional collaboration: Quality data facilitates better information sharing and collaboration between financial institutions, law enforcement, and regulatory bodies, creating a more comprehensive approach to combating financial crime.

But, collecting and maintaining high-quality data comes with its own set of challenges.

Maintaining high-quality data: Challenges and tactical manoeuvres

These are some hindrances to maintaining data quality:

• Fragmented, diverse and complex data sources
• Rapidly changing financial landscapes
• Technological limitations
• Privacy and data protection regulations

The good news is that organisations can ensure data quality by adopting these strategies:

• Implement robust data governance frameworks
• Utilise advanced data cleansing technologies
• Monitor and validate processes continuously
• Invest in technology such as advanced data integration platforms, Master Data Management (MDM) solutions, and data quality management tools
• Train the staff on data management regularly

Conclusion

The financial crime landscape is increasingly complex and dynamic. High-quality data is not just an operational requirement but a strategic imperative for organisations seeking to protect themselves, their customers, and the broader financial ecosystem.

By prioritising data quality, financial institutions can build more resilient, efficient, and effective systems to detect, prevent, and mitigate financial crimes.

How can Infosys BPM help?

Facing the evolving landscape of financial crime requires robust and efficient solutions. Infosys BPM's financial services provide reliable, scalable, and cost-effective tools that are AI-powered to help organisations effectively combat these threats.