Financial services are evolving at an unprecedented pace, spurred on by COVID-19, and consumers expect those services to be delivered instantly and securely. But fraud attempts are evolving just as quickly and pushing financial institutions to seek new strategies to manage risk and stay ahead of criminals.
Successful fraud attempts can lead to financial losses, customer and member attrition, reduced shareholder value and reputational damage. The stakes are high, but financial institutions have access to the tools that can help them mount a powerful defense against fraud.
Gasan Awad, a Fiserv vice president of strategy and market insights for fraud and financial crimes, and Dave Excell, founder of Featurespace, an industry leader in combatting financial crime, understand how technology and an educated staff can position financial institutions to fight fraud.
Fiserv and Featurespace have recently formally agreed to work together, and Fiserv plans to launch a series of solutions incorporating Featurespace capabilities. Ahead of that, we spoke to Awad and Excell about the state of financial crime, how it’s evolving and how to fight it.
What are the key fraud trends affecting the financial services industry?
Awad: The need for real-time payments is growing. From a fraud perspective, we need to think through how we handle that, both upfront with consumers and in the back office with, for example, indemnification and recovery plans. We have to facilitate speed, but in a safe manner that also maintains convenience for the consumer.
The pandemic has accelerated the move from checks and cash to contactless and electronic payments. The rise in person-to-person payments is a good example. But with that shift, there are more scams, more account takeovers and more sophisticated identity fraud. We can’t forget about back doors such as call centers, so the more folks we deputize as fraud experts in the organization, the better.
That is where convergence comes in. We want to blend channel authentication, insights around biometrics and device geolocation, and transaction data to help us make more effective, precise decisions. Leveraging critical data points across your organization will lead to better outcomes, both to detect and prevent fraud and to enable people to transact how they want without unneeded friction.
Employing artificial intelligence (AI), machine learning and adaptive behavioral analytics allows us to get a more holistic, timely, accurate view of the client involved in the transaction and put that transaction and engagement experience in context for the optimal decision.
Excell: I see the rising level of sophistication in fraud attacks as being a key trend. As tighter controls are put in place around previously higher-risk mechanisms of transacting (such as the migration to EMV on card payments), fraudsters are pivoting to new ways of exploiting the financial system.
High-quality and low-cost compromised data continues to be readily available on the dark web. When coupled with a criminal’s application of machine learning, it puts more strain on fraud teams to continue adapting.
What do financial institutions need to do differently to reduce fraud and get ahead of criminals?
Awad: The challenge is to consistently manage fraud risk in this rapidly evolving ecosystem driven by extremely complex schemes.
Educate and train staff in key areas such as operations and call centers. Also, don’t forget about customers and members. The more educated and empowered they are, the more vigilant they can be around scams, account takeovers and identity theft. You can make a difference in this arena as the experts spreading your knowledge and educating people.
Share data within your organizations and use data in a holistic manner to drive the best insights. The more data you can leverage to truly drive a 360-degree view of the client, transaction and engagement, the better for your business and fraud-mitigation efforts. The use of communitywide consortiums to help drive out bad actors can help you deter fraudulent activities.
Excell: My best advice is to invest in an end-to-end fraud and financial crime solution that allows you to evolve with the changing landscape and prepares you for sudden and unexpected shifts in consumer behaviors.
The right solution provides effective measurement, meaning understanding where and how the fraud is taking place within the institution. There’s no point in fixing the wrong problem or making a quick fix criminals can work around.
There is always a balance. In areas where you may appear to be successful, it’s possible the experience of the genuine consumer is being damaged. It’s important to have baseline measurements in place, so when a change is introduced, the effect can be accurately quantified and adjusted if the expected result hasn’t been achieved.
Your solution should also facilitate faster decision making. As the speed of payments increases along with the pace of change in fraud attacks, agile institutions respond faster. Making decisions in real time enables fraud prevention or, if detected after the fact, communications to the consumer to ensure future attacks are blocked. Technology plays a role, but, operationally, the institution can institute processes that facilitate an introduction of new fraud-prevention strategies.
Why are AI, machine learning and adaptive behavioral analytics the best options to fight fraud?
Awad: Fraud and financial crimes constantly evolve and require tools that can adapt to new attack vectors. That is the advantage of leveraging AI and machine learning. They make fraud detection more predictive and enable a more proactive approach that can reduce false positives, drive efficiency and create good consumer experiences.
The ability to consume a huge amount of data and information around channels, transactions and multiple convergence points and respond effectively is critical, particularly for real-time requirements.
Excell: AI and machine learning facilitate monitoring for an unprecedented amount of data at scale, which contrasts to the current generation of fraud controls that are often biased against the most recent fraud attacks.
Adaptive behavioral analytics adds an additional layer of protection by focusing on good activity to create a baseline of what should be allowed. Anomalous deviations are used to identify new fraud attacks as they emerge. That combination enables institutions to make accurate decisions, detecting fraud and reducing friction while continually adapting to the changing behavior of consumers and financial criminals.
What is in the future for detecting and preventing fraud and financial crime?
Awad: I envision more intelligence in fighting financial crimes due to increased computing power and innovation. I also believe there will be more collaboration in the fraud and financial crimes arena around modern, constantly evolving consortiums that can be leveraged to make more effective decisions. High-power AI and machine learning solutions are now table stakes and will continue to evolve and assist us in managing and balancing speed, security and convenience.
Excell: AI and machine learning will continue to advance at an incredible pace. Fraud and financial crime are the principle use cases for the technology, so I expect to see it further improve the consumer experience while detecting and preventing more crime, more quickly. Today, that level of technology has been mainly accessible for large financial institutions with experienced data scientists. However, I expect it to become more universally available to protect all organizations and consumers from financial crime.