The Upside: AI and financial crime risk management has been saved
The Upside: AI and financial crime risk management
Stories to inspire positive change
While it’s important to stay ahead of emerging risk and financial advisory issues, leading practices, and innovative solutions, we know you are busy and inundated with emails. On top of that, with all the negativity out there, we want to contribute something positive to your inbox. Introducing the Upside: a new series that shares an uplifting story to brighten your perspective on matters involving risk.
Back in 2008, the Chaitén volcano in Chile erupted for the first time in over 9,000 years. On May 2 of that year, a plume of steam and ash rose 50,000 ft into the atmosphere, forcing 4,000 residents to evacuate by boat.¹ On that fateful day, a previously “quiet” volcano showed what can happen if a serious threat goes undetected until it’s too late.
Most don’t make headlines, but more than 80 volcanoes erupt every year. Fewer than half of the world’s 1,500 active volcanoes are actually monitored, which translates into a lot of unprepared people.
Eruptions are often preceded by signals that can last from a few hours to a few years. And some of those signals are detectable by readily accessible data, such as satellite sensors. Looking to predict the unpredictable, a team of researchers set out to see if they could use artificial intelligence (AI) to identify those signals, with the hope of alerting people at risk prior to an eruption.
The team trained AI to use satellite sensors and seismic data to detect and quantify changes around volcanoes. Their system is currently being used to monitor 17 volcanoes around the world—and can easily incorporate new data inputs as their knowledge expands.²
Just how volcanoes display certain signals before they blow, financial crime can trigger alarms when the right transactional monitors are put in place. Combining human expertise with machine learning, analytics, and AI, organizations can detect potential fraud in financial transactions—flagging risks before the damage is done.
Machine learning can help spot potential anomalies, flagging suspicious activity to investigators so they can drill down to determine if the alert could be indicative of financial fraud. This information can then be used to teach AI to make those decisions on its own moving forward.
Because financial crime is one of the bigger challenges facing companies today, monitoring financial transactions can go a long way in helping organizations detect, prevent, report, and investigate suspicious activity.
Analytics can help organizations spot transaction irregularities that may not have been noticeable to the human eye—making transaction monitoring more risk-focused, compliant, and efficient.
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