The Rise of AI in Fighting Financial Crime: Hype vs. Reality

Do you know? The global fincrime compliance costs for financial institutions total more than $206.1 billion, as per FinTech Global.

Financial crimes are on the rise and becoming more sophisticated and harder to detect. In short, fraudsters are evolving faster and also becoming tech-savvy. From money laundering networks using shell companies to AI-generated deepfakes, criminals are leveraging tech more than ever.

However, Artificial Intelligence is not just for fraudsters. AI-powered fincrime tools are proving to be extremely powerful in the fight against these financial crimes, enhancing detection and prevention capabilities and ensuring compliance.

But amidst the buzz, there comes an important question: How much of the hype is matched by reality? Is AI truly helping with the fight against financial crimes or are banks just investing in inflated expectations?

Let’s first start with the adoption and impact of AI in the fincrime industry.

Adoption and Impact of AI

AI Adoption is accelerating: According to a 2023 survey by PwC, 62% of financial institutions already use AI and ML in some capacity for AML activities, and this is expected to increase to 90% in 2025.

Hawk.ai finds 57% of banks already use AI to cut false positives and many are also deploying AI to identify new risks, clean data, and streamline investigations.

The total cost of fincrime compliance costs remains huge; about $61 billion in the U.S. and Canada.

Now let’s see how exactly AI helps with fincrime detection (with some real-life case studies)

The Reality: AI’s Impact on Financial Crime Detection

AI has proven its mettle in various aspects of financial crime prevention:

• Enhanced Fraud Detection: AI-powered fincrime tools can analyze huge amounts of transaction data in real-time, identifying patterns and anomalies that may indicate fraudulent activity.
  For instance, Mastercard’s new AI-powered fraud detection system features AI technology that scans around 160 billion transactions every year.
• Reduction in false positives: Traditional systems often flag legitimate transactions as fraudulent, leading to a wastage of resources.
  AI-powered fincrime tools leverage advanced technology to analyze data which helps to maintain accuracy and enhance both efficiency and customer experience.
  One of Velocity’s clients saw a 30% reduction in false positives, with fraud cases reducing by around 42% overall. (We can add link to our case study here)
• Cost Savings: By implementing AI in fraud detection, financial institutions can save a lot of costs overall.
  As per a study by McKinsey & Company, implementing an AI-driven fraud detection system could help reduce fraud-related costs by 30% to 50%.

Real-world case studies:

• Bank of America implemented a real-time fraud detection system, which resulted in a 50% reduction in fraud losses, with the system detecting and preventing over $1 billion in fraudulent transactions within the first year. (Source)
• Mastercard implemented a real-time fraud detection software and reported a 40% reduction in fraud losses, with the system detecting and preventing over $2 billion in fraudulent transactions. (Source)

These two case studies show that AI can markedly sharpen detection when implemented correctly.

The Hype: Limitations and Challenges of AI in Financial Crime Prevention

Despite its advantages, AI is not a solution for everything. Here are a few challenges:

• Data Quality Issues: The effectiveness of AI models heavily depends on the quality of data they are trained on. Poor or biased data can lead to inaccurate predictions and missed threats. Say no to outdated data and make sure to update it on a timely basis.
• Evolving Threats: Criminals are also leveraging AI to enhance their tactics, creating deepfakes and sophisticated phishing schemes that can bypass traditional security measures. So, financial institutions need to ensure you go with a solution that’s adaptable and flexible.
• Regulatory and Ethical Concerns: The use of AI raises questions about transparency, accountability, and compliance with regulations like GDPR. Ensuring that AI systems are explainable and fair is an ongoing challenge.

The Best Way: Integrating AI with Human Expertise

To maximize the benefits of AI in financial crime prevention, a balanced approach is necessary:

•  Always keep your team in the loop. By combining AI’s analytical capabilities with human judgment, financial institutions can ensure more accurate and ethical decision-making.
• Make sure your AI models are regularly updated with new data to adapt to evolving criminal tactics.
• Financial institutions, regulators, and technology providers must work together to establish standards and share intelligence.

AI + Human Expertise = A perfect balance approach.

Stay Ahead of FinCrimes with Velocity

AI in financial crime prevention is no longer just buzz. However, it will work only when it’s properly implemented.

Financial institutions need comprehensive data sets, advanced AI/ML-based compliance models, and robust analytics to keep up with the evolving threats.

Velocity is an ultimate fincrime solutions suite. Our top-notch AI-powered fincrime tool is designed to help financial institutions detect and prevent financial crimes. We aim to empower compliance teams to win the fight against criminals.

Book a demo or contact us to see how Velocity’s AI can strengthen your fraud detection, AML monitoring, and sanctions screening.

Get in touch with our team!