An Automation Renaissance: The Art of Flagging Fraudulent Activity in Real-Time

William Dawsey, Global Director of Finance and Payment Systems at Chetu Inc. offers insights into the changing tides within the payments landscape, discussing how emerging technologies will rattle the preexisting architecture. Chetu Inc. is a custom software provider specializing in payment gateway solutions, system integration, blockchain development, and other fintech solutions.

When we think of antiquities, we think of CD players and checkbooks, but we rarely think of the dollars, quarters, and dimes that are losing meaning in the age of plastic. Non-cash payments have taken over the payments scene, experiencing astronomical growth over the past decade with no sign of slowing down. The thing about digitalized exchange—it’s not impermeable. For this reason creditors and other lenders have needed to turn their attention toward a new battlefront: fraud management.

Fraud prevention and detection procedures have become a hot selling point among competing financial institutions, moving to the forefront of the marketing campaigns and existing beside enticing interest rates and loyalty programs. The advent of the internet has sent humanity through a whirlwind, posing new obstacles and challenging our previous way of life, one devoid of cyberattacks and digital security challenges. It has transitioned into a force we both bow down to out of gratitude and out of fear.

For one, when we opened the door to the digital age we were met by boundless potential, in the payments landscape in particular; we reap the benefits of lightening-speed transactions and carry small strips of plastic encrypted with our account information, depositing checks via ATM and investing in cryptocurrencies that exist in a purely digital form. But the virtualization of monetization is a double-edged sword, and as we become increasingly interconnected, we are vulnerable to unwanted information exchange and cyber warfare.

While consumers welcome the current fraud management standards, enjoying the little to no liability and fast-action rectification, creditors and bankers are left in a high-loss predicament. This has caused the majority of merchants to seek alternative methods in an attempt to revitalize their cyber security and gain the upper hand in the ongoing battle against hackers and data thieves, an imperative that has ushered in a fraud management revolution.

Currently, there are several different schools of thought igniting conversation and dismantling the plateau conventional, fraud processes have settled themselves on.

Robotic Process Automation (RPA) to the Rescue

Currently, competing merchants leverage similar fraud management systems. They input a set of rules and the system conforms to those rules, issuing alerts for any data flow that does not comply with the rules. A combination of RPA and machine learning accomplishes the same, but with greater efficiency and greater output integrity, all while cutting manual processes and the need for excessive manpower.

RPA first revolutionized manufacturing, offering assembly lines an alternative to human labor and lending robotic machinery for product packing. Recently, information technology specialists have applied RPA methodology in a less obvious way, filtering the code into software bots that preform mundane and repetitive tasks—background checks, credit processing, credit approval, payroll, etc. Despite its diverse application in the IT realm, one use is particularly compelling beyond the rest: fraud management.

Where RPA oversees the fraud prevention, machine learning tackles fraud detection. By programming machine learning software with proprietary algorithms, bots mine data from purchasing histories and analyze the data according to the known fraud patterns.

On the surface, the machine learning process appears closely-related to conventional fraud management processes, but machine learning follows a natural progression, evolving known inputs to establish ground-breaking outputs that are not predefined. An operator feeds inputs and outputs into the system, and once the bots are trained, the system will operate independently to flag suspicious activity in real-time, a feature manual processing is incapable of achieving.

Possible Challenges

Of the Chetu clients surveyed within the Payments Industry, 50% have expressed interest in Robotic Process Automation and machine learning development to update the architecture of their current fraud management campaigns. To maximize RPA functionalities and AI capabilities and turn a favorable ROI, the new system must work in conjunction with the preexisting system, evoking conversation between previously disparate systems.

Although this combination of technologies will ultimately lead to a tremendous reduction in overhead, the project development must be paired with integration processes to ensure the system has a productive environment to grow within. Considering RPA and AI have already proved they are evolving forces, penetrating unconventional markets, it is likely that they will grow into a more comprehensive force to usher all facets of payment processing through a renaissance.

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