The fight against money laundering: Machine learning is a game changer
By PK Doppalapudi, Pankaj Kumar, Adrian Murphy, Christophe Rougeaux, Rick Stearns, Scott Werner, and Shuo Zhang, contributing writers for Mckinsey.com
The volume of money laundering and other financial crimes is growing worldwide—and the techniques used to evade their detection are becoming ever more sophisticated. This has elicited a vigorous response from banks, which, collectively, are investing billions each year to improve their defenses against financial crime (in 2020, institutions spent an estimated $214 billion on financial-crime compliance). What’s more, the resulting regulatory fines related to compliance are surging year over year as regulator’s impose tougher penalties. But banks’ traditional rule- and scenario-based approaches to fighting financial crimes has always seemed a step behind the bad guys, making the fight against money laundering an ongoing challenge for compliance, monitoring, and risk organizations.
MIT Sloan research on artificial intelligence and machine learning
By Tracy Mayor, contributing writer for MITSloan.mit.edu
There’s little question artificial intelligence and machine learning are playing an increased role in making business decisions. A 2022 survey of senior data and technology executives by NewVantage Partners found that 92% of large companies reported achieving returns on their data and AI investments — an increase from 48% in 2017.
The search for new metals is now easy with machine learning
By Meghmala, contributing writer for Analyticsinsight.net
According to a recent study, machine learning could aid in the creation of new metal types with advantageous characteristics like resistance to rust and high temperatures. A variety of industries could benefit from this; for instance, spacecraft could be improved with metals that function well at lower temperatures, while boats and submarines could benefit from corrosion-resistant metals. Currently, attempts to produce new metals are mostly conducted in laboratories by scientists. Typically, they begin with one well-known element, such as iron, which is readily available and malleable, and then add one or two more to examine how it affects the base material. Trial & error is a hard process that invariably produces more failures than successful outcomes.
How AI solutions can defend against cyberattacks
By Gerasim Hovhannisyan, contributing writer for Forbes.com
In part, because hybrid and remote workplaces are the new normal for most companies, the sophistication of cyberattacks and the risks they pose have grown rapidly over the last few years. In fact, these new work styles have opened up a whole new set of phishing methods for threat actors.
Did you see an interesting article in the last week? Share it with us! Send it to info@lityx.com.
