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Using Adaptive Behavioral Analytics to Detect Fraud

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While fraud threats are nothing new for payments processors and financial institutions, the degree and magnitude of such incidents have escalated in recent years. A February 2018 Javelin study found that nearly 16.7 million consumers were victims of identity fraud in 2017—up 8% from the previous year.

Fraud prevention solutions must be flexible and sophisticated enough to not only counteract increasingly-savvy fraudsters, but also distinguish true fraud from false positives, which occur when genuine activity is mistakenly treated as fraud. According to CreditCards.com, four out of five blocked transactions are actually genuine, and these misunderstandings often result in customers being locked out of their accounts. In many ways, the aftermath of false positives can prove more damaging and costly than an actual instance of fraud, as institutions miss revenue generation opportunities while simultaneously hindering customer loyalty and trust.

As consumer payment technologies evolve, so too will the complexities of fraud detection and mitigation. Therefore, it is vital that risk management teams end their reliance on rigid, manually-programmed rule sets or static machine learning models and instead capitalize on the advanced capabilities offered by today’s more versatile tools. By modernizing their fraud strategies with adaptive behavioral analytics, payments processors and financial institutions can better mitigate risk and increase revenue.

How Does it Work?

Unlike the static machine learning of the past, adaptive behavioral analytics are extremely proficient at differentiating between actual fraud and activities that appear suspicious but are ultimately genuine. As a result, friction in financial services and e-commerce is significantly reduced and customers can maintain confidence in their preferred transaction method.

Adaptive behavioral analytics empowers machine learning through a set of sophisticated, automated, self-learning algorithms that review account activities and notify security teams of anomalies.

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These algorithms construct baseline behavioral profiles to reflect a customer’s activity type and frequency. In every interaction—regardless of if a payment occurs—information is gathered and evaluated on the type of device that is used, how it’s used, its location and the amount of the purchase. Combined, these behaviors create a customer portrait that becomes increasingly more accurate over time. Every subsequent interaction then can be measured against the behavioral portrait, within milliseconds, to determine if their activities are fraudulent or genuine.

For example, if a user logs into his or her account at an abnormal rate or suddenly begins adding priority shipping to high-priced orders, the system will detect the irregularity and block future activity. However, if a user simply purchases an expensive holiday gift or books travel arrangements—behaviors that coincide with seasonal activity—the system will recognize and differentiate the fraudulent from the legitimate accordingly.

Adaptive behavioral analytics also optimizes the speed and convenience of fraud detection by processing volumes of data and delivering critical intelligence accurately and immediately. Through this more comprehensive investigation, the software enhances the customer profile to better understand and recognize behavioral trends—a welcome sight for security teams that previously spent hours sifting through reports to locate red flags.

Where Can Adaptive Behavioral Analytics Help Most?

The ubiquity of mobile technology has created a consumer audience who prefers to conduct business through a smartphone, tablet or another device that eliminates a trip to a physical store or bank branch. In turn, these consumers demand leading-edge mobile technologies that are intuitive, convenient and offer a full range of services.

The combination of the U.S. adoption of the EMV standard in 2015 and the rise in e-commerce has escalated the volume and prominence of Card Not Present (CNP) fraud. Whether through online purchase portals or apps that access mobile wallets, the digital entry of account information raises the likelihood of a person’s information becoming compromised.

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With more transactions taking place, the volume of both true fraud activity and regular behaviors that appear suspicious will increase. However, adaptive behavioral analytics enables a more refined detection between the actual fraud and genuine activity.

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It is the best of both worlds: a much-needed, innovative line of defense that combats payments fraud and clears a path for more revenue-generating transactions.

Protecting Your Company from Rogue Employees

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While employee malfeasance rarely takes down entire companies, it can result in serious fines, sanctions, court judgments, settlements and reputational damage. Big data analytics is one way leading companies are able to mitigate risk, by proactively detecting threatening or illegal behavior.

Traditional ERM Approaches Won’t Do

Compliance officers do their best. They generally work within enterprise risk management (ERM) frameworks to introduce corporate policies and procedures, conduct risk avoidance training and audits, and create inter-disciplinary committees. They work with IT to run compliance auditing software on critical structured data, including financial databases and transactional applications.

By targeting only well-behaved structured data, however, compliance officers can lose sight of one key fact—structured data is a small percentage of organizational data. Data storage analysts report that most organizational data are only 15% to 20% structured data and 80% to 85% unstructured.

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This leaves a huge volume of data that presents serious compliance risk to IP, especially electronic communications.

While e-mail, instant messaging, texting and social media are ingrained in our culture, traditional auditing software does not focus on communications. These threats often evade notice until the damage is done.

Here are some ways threats can escape the radar of employers that have traditional ERM approaches:

  • Limited ability to analyze unstructured data. The inability to monitor unstructured data leaves the company open to regulatory consequences and other risk.
  • Keyword searching to winnow down data sets often delivers a high volume of false positive results. Filtering techniques such as keyword searches may not be highly accurate and require intensive manual review.
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    The result is higher cost and longer timeframes for manual-review projects.

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  • Potential security issues. Communication platforms are rapidly proliferating. Employees might be sharing inappropriate corporate information on social media, yet these mentions often go unmonitored by the company, potentially missing evidence of employee misconduct.
  • Complex regulatory changes. Many governmental and industry regulations are already complicated, and their revisions only intensify complexity. For example, since introducing Dodd-Frank, regulators have written 224 of 400 expected rules and continue to modify existing rules.
  • Case-by-case approaches. Case-centric approaches to litigation, investigations and regulatory compliance matters impede applying learning and attorney work product on these cases to other matters. This inability lengthens legal reviews and investigations and multiplies costs. Case-based discovery also makes it difficult to discover widespread risky communications between employee groups and outside organizations.
  • Geographic and organizational silos. Relevant data is spread across different storage locations and eDiscovery platforms, creating distinct data silos.

A Cautionary Tale

Here is an example of risk that can go undetected until it’s too late, as it did at Wells Fargo. Banker 1 is responsible for reaching high quarterly sales goals. His manager increases his sales goals for the next quarter. Banker 1 emails a colleague complaining about how his goals are impossible to meet. Banker 2 suggests he try a creative process called “pinning,” which consists of a banker enrolling an actual customer in online banking to create a “sale.” The banker fills in the customer’s name and address but puts in a fake email address so the customer never receives banking communications. The banker meets his sales goals—and hopes the customer never finds out.

How Big Data Analytics Can Help

Analytics tools are already omnipresent in eDiscovery and compliance reviews. They include predictive coding, email threading and concept searching. They are highly useful for culling large data volumes to more manageable sizes. They also locate meaningful text and concept patterns so that reviewers can strategically work with high priority documents.

The catch is that these analytics can only filter to a point, and only work on a single-case basis. No matter how the case management software learns from tagging and work product, that learning cannot be applied across multiple matters if it resides on different review platforms or with different vendors. Each time a new case begins, reviewers and their software must start over. This leads to very long and repetitive document review processes, already the single most expensive activity in eDiscovery. Clients and attorneys also risk exposing sensitive information as the matter makes its way between document review platforms and multiple stakeholders.

A big data approach, versus specific analytics tools can continuously consolidate billions of documents into a central repository. It can also apply machine and human learning to enable the reporting of trends, new data relationships, and fresh insights into data across all cases—not just a single matter—for greater efficiency, cost control and risk mitigation.