AI can help fintechs fight fraud as a service

We have entered a perfect storm for financial crime where fraud is thriving. This maelstrom has been created by the combined effects of geopolitical events and the ongoing global pandemic, which has led to the acceleration of the digitalization of society, by necessity, even faster than it was before.

While all sectors are suffering, fintech has been particularly hard hit, making it all the more difficult to strike the right balance between preventing financial crime and maintaining high levels of customer service and satisfaction – and critical for the company. As the situation continues to evolve and worsen, it has become a priority for fintechs to learn how to effectively combat and defeat fraudsters, while continuing to grow their business.

As businesses rise to this challenge, they need to consider:

  • What key areas should fintechs focus on? Where does fraud occur?
  • How can fintech minimize the negative impact of fraud on operations and finances?
  • Can technology defend itself against fraudsters?

“Fraud as a service” takes center stage

It sounds like a simple clever pun based on the software as a service (SaaS) model, but fraud as a service (FaaS) is a real phenomenon. Mirroring the cloud model through which fintechs deploy many of their capabilities through services, bad actors with worse intentions are leveraging similar technology to commit service-based crimes on an unprecedented scale. FaaS occurs when an individual or group of fraudsters facilitates fraudulent online activity by providing tools and services to others.

This sneaky method allows fraudsters to purchase and exploit data and tools using stolen or synthetic identities, which they then use for fraudulent purposes. FaaS also enables large-scale fintech attacks in which fraudsters overwhelm financial systems with bad traffic and conduct illegal transactions at high volume. By using this approach, fraudsters are emulating fintechs’ use of fast, easy, and cost-effective online processes, along with the best analytics and data, only to defraud instead of enrich. The result: software fights software on an unmanageable scale.

Sensible point

One area where fraud is particularly pernicious in the industry is onboarding, where it’s vital for fintechs to balance fighting crime while ensuring a high level of customer service. Issues with identity theft and fraudulent documents often arise when onboarding new customers, which can hurt a company’s growth goals and hamper its customer service goals.

Identity theft and document fraud are on the rise and are already costing the global economy billions of dollars a year, with no end in sight. Beyond onboarding, institutions are asking for more documentation to verify client identity, requiring proof of residency and age, as well as proof of income, in addition to standard IDs. Whether it’s altering bank or government documents or creating fake documents, the fact is, all it takes is one valid fake for a bad actor to wreak havoc.

If that same fraudster has access to multiple stolen identities, the fintech will face large-scale attacks via serial fraud attempts. Often a successful case of fraud is the precursor to other crimes such as money laundering, human trafficking and even terrorism.

Use AI to scrutinize identity

It may seem hopeless when learning of large-scale attacks executed at high speed, but resisting and eradicating fraud is possible with AI. Typically, new fraud patterns are difficult to detect until they have been observed for some time, at which time fintechs can react and deploy defenses against the attack. Usually the time lag is too big and the damage is done. But AI can subject every customer interaction, from documentation to behaviors, to a level of forensic analysis that would be impossible for humans to achieve at the same speed. The technique can filter out everything from fake documents and stolen data to serial fraud attempts executed by bots.

Integrating AI ensures that fintechs will authorize fewer high-risk identities and document transactions in their processes, helping them outsmart fraudsters and beat them at their own game.

However, it is not enough to simply invest in AI technologies; human capabilities are equally important to achieve large-scale financial crime prevention in an AI-centric model. Fintechs must not only understand the data they are working with and have AI expertise, but must also ensure alignment with business goals and establish an appropriate operational workflow. By mixing these critical elements, fintechs box successfully fight against financial crime.

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