After one of the longest periods of US economic expansion on record, many experts are saying it’s time for a recession. That’s troubling in itself, in terms of the potential for job losses, financial uncertainty and economic stagnation. But it could also have a major impact on fraud, as motivation increases and operational cutbacks make scams easier to slip under the radar.
Organizations keen to mitigate the possible impact of such an event should look to machine learning-powered fraud solutions to offer highly scalable, effective decisioning even as resources are scaled down.
Time for recession?
Two-thirds of business economists in the US are predicting the next recession will land before the end of 2020. It’s not just that the country has seen economic growth for the past decade, and so is “due” for a contraction. There are also macro factors at play, such as the ongoing US-China trade war, Brexit-related uncertainty and a downtick in the EU’s largest economy, Germany.
What does this mean for fraud? Well, it grew following the last financial crisis, particularly in areas of identity theft, mortgage applications, and employee-related schemes. It’s not just that there’s more motivation during these times, as job losses take place and money becomes scarcer, but there may also be fewer middle managers in operational positions ready and able to stop it.
This can be a difficult problem for many organizations to crack: how to tackle rising fraud as resources are scaled down to preserve the bottom line?
Complicating matters further is the fact that many retailers, financial institutions, and other organizations operate globally today. So even if the country they are headquartered in is not in a recession, they may be exposed to rising fraud levels in countries that are experiencing a downturn.
Time To Respond
The firms keen to plan ahead must therefore first gain better insight into this fraud exposure. Think of it as a kind of stress testing approach. Who are your customers? Where are they based? What are the economic conditions like in those countries?
The good news is that modern, data-centric tools like Simility’s Adaptive Decisioning Platform offer significant advantages over legacy solutions. Combining large amounts of data from multiple sources allows in-house fraud experts to write rules within minutes, which can be immediately implemented into fraud models. These can be adapted to shifting circumstances brought about by macroeconomic conditions. Machine learning algorithms are then set to work uncovering anomalous patterns that may be indicative of fraud. The models will continue to evolve, based on new data and events, to adapt as fraudsters change their tactics.
There’s no guarantee that we’re hitting a recession anytime soon. But combatting fraud is ultimately about managing risk to acceptable levels. So, if an economic downturn in the future presents a major risk of fraud increasing, that would seem to be a risk worth addressing. Don’t wait until it’s too late to invest in advanced machine learning-powered solutions. Find the partners today that can help you weather the fraud risks of tomorrow.
To learn more about how Simility can help your organization mitigate fraud during a recession, schedule a demo today.