informa
/
5 min read
Commentary

The Dark Side of Digital Transformation: Massive Fraud

A holistic approach to combatting the growing problem of fraud requires an analytics strategy that draws data from multiple internal and external sources.

The COVID-19 pandemic has altered the way we work, play, and do business. So many of our activities have gone online, virtual or remote, that we have supercharged the evolution of internet-based, sustainable solutions. From virtual schooling to ordering groceries, practically everything we interact with has adopted a digital component.

This rapid evolution of digital solutions has come with a downside: record-breaking fraud.

The Federal Trade Commission (FTC) received more than 2.8 million fraud reports in 2021, including imposter scams and online shopping scams, fueled by burgeoning online transactions and the new digital channels that have proliferated amid the pandemic. All told, American consumers reported losing more than $5.8 billion to fraud last year, a nearly 76% increase from $3.3 billion reported lost in 2020.

To address the rise in global fraud, more and more companies and organizations are harnessing the potential of analytics to accelerate the volume of transactions they review and to identify suspected cases of fraud, according to the latest anti-fraud technology study by the Association of Certified Fraud Examiners (ACFE) and SAS.

Based on survey responses from nearly 900 professional fraud fighters worldwide, the 2022 Anti-Fraud Technology Benchmarking Report illuminates how organizations across sectors are using technology to fight fraud. Notably:

  • More than 40% of respondents reported accelerating their use of data analytics significantly (14%) or slightly (29%) during the pandemic.
  • Nearly all respondents (98%) said their organizations’ use of data analytics helped them detect more fraud much more quickly and accurately.

Fraudsters Seize Historic Opportunities

Not surprisingly, the most represented industries in the survey were government and financial services. Banks have been at the forefront of anti-fraud technology for decades, wielding advanced analytics to combat things like credit card fraud and money laundering. Strict compliance regulations and serious reputational risk, combined with greater resources, have allowed banks to be on the cutting edge of fraud fighting.

Conversely, a promising opportunity exists to partner with government agencies to enhance their fraud-prevention technology and help them tackle this growing threat. Fraudsters frequently target government programs, whether it’s overbilling the government for services or using stolen identities to file fraudulent tax returns or benefits claims.

This was especially true during the COVID pandemic. Pandemic relief programs rolled out quickly by governments around the globe proved to be attractive targets for fraudsters. In the US alone, the Department of Labor’s Inspector General testified to Congress that “at least $163 billion in pandemic (unemployment insurance) benefits could have been paid improperly, with a significant portion attributable to fraud.” That’s a staggering sum.

To turn the tide, many government agencies began using facial recognition-based identity verification. Ultimately, bias and privacy concerns, as well as access and user problems, led agencies such as the IRS to reconsider this approach. Fortunately, there is a more holistic analytics approach that draws from data sources that do not carry the same type of inherent equity and access bias, such as digital devices, IP addresses, mobile phone numbers and email addresses.

The identity verification takes place instantaneously and behind the scenes, detecting potential fraud while reducing friction for citizens applying for legitimate benefits. Only when something suspicious is indicated will the innovative solution introduce obstacles, like having to call a phone number to verify additional information.

The Road Ahead

The current fraud landscape has proven the need for organizations to be forward-thinking and proactive in advancing their fraud-fighting tools and capabilities. The silver lining is that, across industries, the majority (60%) of anti-fraud pros surveyed by the ACFE said they expect their anti-fraud tech budgets to grow over the next two years.

Where will they prioritize their investments? Advanced analytics topped the list, particularly artificial intelligence, and machine learning, cited by 26% of respondents. Predictive analytics/modeling followed a close second, noted by 22%. Such capabilities are a powerful force in boosting fraud detection accuracy and efficiency, enabling it to be done in real-time.

Whether an organization is far enough along in their analytics journey to make that leap, one universal anti-fraud tenet can help guide the leaders and laggards alike: Data is a fraud fighter’s best friend.

Data sets are immense and only growing larger. That’s especially true due to accelerated digital transformation caused by the pandemic and the pace of development of new apps and digital channels. With each new digital venue is a new opportunity to collect more data -- data that can ultimately be used to differentiate a valued customer or citizen from a fraudster, or a legitimate transaction or application from an attempt to defraud.

An effective fraud defense requires pulling data from a variety of sources. In complement to their own data sources (cited by 80% of respondents), organizations should not neglect to incorporate external and third-party data. The inclusion of public records, law enforcement and government watch lists, and device data, for example, can be a significant differentiator when using tried-and-true analytic techniques like exception reporting and anomaly detection or more advanced machine learning and AI models.

Criminals are continually changing schemes to exploit gaps in organizational defenses and abscond with billions of dollars. Anti-fraud professionals must have the right technologies for navigating and staying ahead of these committed and sophisticated fraudsters. From traditional data analytics to emerging options like biometrics and computer vision, fraud fighters must be aware of and deploy countermeasures that keep them ahead in this technology arms race.