Editor’s Note
This article provides a concise definition of artificial intelligence as a set of statistical techniques for pattern recognition and prediction. It highlights practical applications within financial services, such as fraud detection and process automation.
Artificial intelligence is a set of statistical techniques that enable computers to see relationships, make deductions, and predict scenarios based on patterns learned from large amounts of data. Financial services companies are using AI techniques to automate administrative processes, including fighting credit card fraud, personalizing product offers, making recommendations to sales teams, and combating money laundering.
Traditional rule-based systems that look for warning signs of criminal activity or suspicious transactions based on pre-programmed patterns are giving way to AI-based systems that can detect behavioral characteristics related to money laundering. Historically, AML software has looked for signals that could indicate criminal activity, as well as supplementary information such as a client’s appearance on an international sanctions list, bank deposits just below the threshold requiring reports to the government, or transfers of amounts from an account that are similar to those recently deposited.
The challenge is that criminals employ evolving tactics to launder their income through what appear to be legitimate financial transactions. In addition to creating shell companies to make ownership tracing difficult, they invest in existing businesses that conduct most of their business in cash and then inflate their revenues. They also deposit their cash in small amounts at multiple financial institutions and channel cash through countries with less strict regulations. This means traditional AML methods are often ineffective while generating a very high number of false positives that can cost banks tens of millions of dollars per year.
Transaction fraud detection, electronic payments to suppliers, AML, and KYC are among the top five use cases for AI in financial services, according to research from AI chipmaker NVIDIA.
- Artificial intelligence can help banks reduce their regulatory compliance costs by detecting finer changes in customer behavior and adapting to new risks as they arise.
- The software helps combat money laundering by finding previously hidden risks and reducing the number of false positive alerts that AML teams need to investigate.
- Banks still maintain a similar number of reports to authorities about legitimately suspicious activity.
- The cost of inefficient processes is high, as global regulatory fines for failing to stop money laundering are always increasing.
Banks are under intense pressure to eradicate increasingly sophisticated money laundering techniques and avoid heavy fines while keeping their regulatory compliance costs under control. By replacing rule-based software tools with AI-based AML applications, banks can improve their identification of suspicious activities by up to 40%, according to McKinsey & Company, while substantially reducing their number of false positives.
AI approaches include applying machine learning to rate customers and predict their propensity to commit a financial crime. AML applications also…
