Is your business at risk of financial fraud? Here’s a powerful way to fight back.

Using machine learning to fight fraud ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌   ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌   ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌   ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌   ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌   ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌   ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌   ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌   ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ 

TODAY'S NEWS. TOMORROW'S INSIGHTS. A daily newsletter from McKinsey & Company   Edited by Alexandra Mondalek   Associate Editor, New York Fraught with fraud In the news • The fine print. In an effort to step up Wall Street's vigilance against financial crime, one US regulator is introducing steep fines for broker-dealer members that are too lax with money laundering prevention. The cost of ignoring rules that seek to prevent financial fraud depends on the size of the firm. For smaller firms, failing to adequately monitor financial transactions and report suspicious activity could mean paying a penalty ranging from $10,000 to $310,000. Noncompliant larger firms could pay a minimum of $50,000, with no upper limit. [WSJ] • Fast-rising fraud. More financial institutions are reporting higher rates of fraud in the past year, with small firms experiencing the biggest increase, according to a 2022 survey of 200 senior leaders. About two-thirds of the respondents with assets between $5 billion and $25 billion said that they are losing more money to fraudulent transactions, while about half of companies with assets worth $100 billion or more said the same. Nearly all executives said they want to prioritize the use of technology to help prevent fraud and money laundering. [PYMNTS] Transaction monitoring—specifically, combining machine learning with other advanced algorithms—is where banks can reap one of the most immediate and significant benefits in their anti-money-laundering efforts. On McKinsey.com • Putting machines to work. There's growing momentum across the financial-services industry to prevent fraud and money laundering, creating strong interest in machine learning (ML) among leaders, say McKinsey partners Pankaj Kumar, Adrian Murphy, and coauthors. ML—which leverages granular, behavior-focused data to build sophisticated algorithms—can nimbly adjust to new money laundering trends and help companies reduce the risk of financial crimes. By replacing existing tools with ML models, one leading financial institution improved its ability to identify suspicious activity by up to 40%. • Transitioning to new tech. Even if a business has the right data to fuel an anti-money-laundering algorithm, implementing the new technology requires some additional time and investment. Firms will need to align stakeholders, develop a gradual technology transition plan, and revise their frameworks for transaction monitoring. For example, when integrating new monitoring technologies, it may be useful for employees to do so in conjunction with existing systems, so that they may be gradually phased out. See three key questions that banks need to address when transitioning to an ML model for transaction monitoring. Learn how ML fights fraud Was this forwarded to you? Sign up here. Or send us feedback—we'd love to hear from you. Follow our thinking This email contains information about McKinsey's research, insights, services, or events. By opening our emails or clicking on links, you agree to our use of cookies and web tracking technology. For more information on how we use and protect your information, please review our privacy policy. You received this email because you subscribed to the On Point newsletter. Manage subscriptions | Unsubscribe Copyright © 2022 | McKinsey & Company, 3 World Trade Center, 175 Greenwich Street, New York, NY 10007