Challenges Associated with the Use of Artificial Intelligence in Combating Financial Crimes
Artificial intelligence offers banks and other financial institutions many possibilities to improve operational efficiency, but on the other hand, it can pose a significant threat and increase financial crimes.
Financial criminals are exploiting this technology to create fake videos, voices, and documents to conduct sophisticated fraud that can’t be easily detected. In the United States alone, generative AI is expected to drive financial fraud losses at a 32% annual growth rate to $40 billion by 2027, according to a recent report from Deloitte.
Perhaps the right response from banks is to protect their systems with the help of advanced tools, and to leverage artificial intelligence technology to combat financial crimes. Indeed, financial institutions have started using this technology to combat financial crimes by monitoring financial transactions, preparing suspicious activity reports, automating fraud detection, and more.
But banks and other financial institutions face some challenges associated with using artificial intelligence to combat financial crimes, most notably:
The need for a balanced approach:
Banks face challenges when using AI in a way that eliminates the need for human intervention. Without a human element, AI technologies may be subject to bias and may not adapt to new threats because they operate differently from traditional financial crime systems.
Financial crime systems, especially anti-money laundering (AML), operate under fixed rules set by specialized teams based on state laws. But AI offers a new way to detect financial crimes. AI can spot suspicious patterns based on constantly evolving data sets. It analyzes financial transactions, historical data, customer behavior, and different contexts to detect suspicious activities and learns over time, providing more adaptive and effective monitoring.
Regulatory Compliance Challenges:
Financial institutions face stringent regulatory requirements, such as the EU Anti-Money Laundering (AMLD) and the US Bank Secrecy Act, which require clarity and transparency in the interpretation of decisions made by banks. AI systems, especially those based on deep learning, are difficult to interpret.
So when adopting AI, banks need careful planning, thorough testing, and human oversight; humans can verify and interpret automated decisions, making them understandable and explainable to regulators.
The need for human judgment for a comprehensive vision:
The adoption of AI cannot replace human monitoring systems, because humans can make accurate decisions in complex or ambiguous cases, which is essential in financial crime investigations.
Human analysts can also detect false alarms that an AI system might present, and prevent bias that can appear when the system is not well trained or relies on biased training data.
Hybrid approach: Combining human judgment and artificial intelligence
Financial institutions can combine a rules-based approach developed by experts with AI tools to create an integrated system that leverages the strengths of both approaches.
This hybrid system helps make AI applications more accurate and resilient in the face of emerging financial crime threats. To achieve this, AI models can be combined with continuous human monitoring.