Data

As Fraud Feeds On Data, FIs Harness It With AI

Too Much Data Is The Problem – And The Solution

Last month, Nasdaq began using an artificial intelligence (AI)-powered tool to flag suspicious trades. The machine learning tool got up to speed after a year of processing trade data and non-public information.

This is just one example of how the explosion of data can increase a company’s fraud vulnerabilities, but can also provide a solution if captured and utilized.

The latest Fraud Decisioning Playbook highlights how eCommerce merchants, financial institutions and other businesses are embracing fraud decisioning solutions to enable secure, seamless experiences for legitimate users.

Current and Future State of Fraud

Research shows that the increased availability of personally identifiable information (PII) is making such attacks easier: The number of exposed U.S. consumer records containing PII increased by 126 percent between 2017 and 2018.

Some estimates claim that as much as 34 percent of U.S. consumers had their PII compromised in 2018 alone, and half of all consumers can find their birthdates, passwords, credit card details or even their Social Security numbers floating on the dark web. This means fraudsters have plenty of resources available as they attempt to access accounts.

The most trusted industries are financial services (46 percent), healthcare (39 percent) and insurance (30 percent). Half also have more faith in finance to be best prepared to deal with a data breach.

Increased fraud risk isn’t exactly a secret; 80 percent of IT business leaders expect cyberattacks or critical breaches to occur within the year. Three-fourths (76 percent) of businesses are in strong agreement that their IT security would be able to detect zero-day attacks, however, indicating general confidence.

Too Much Data 

IDC predicts that the “Global Datasphere,” the entirety of all digital data created or replicated, will grow from 33 zettabytes (ZB) in 2018 to 175 ZB by 2025, which translates to a 27 percent CAGR. By industry, this will be led by healthcare (36 percent), followed by manufacturing (30 percent) and financial services (26 percent). 

Additionally, by 2025, nearly 30 percent of this global datasphere will be real-time information, up from 15 percent of all data in 2017.

Data primarily falls into two categories: structured and unstructured. Structured data is information kept in businesses’ databases that has readily discernible meaning, while unstructured data is more difficult to quantify and harness, referring to the contextual information stored outside most businesses’ internal systems that gives meaning to structured data.

Companies fighting fraud are failing to tap into unstructured data. According to the Harvard Business Review, businesses use as little as 1 percent of their unstructured data, meaning many do not consider the context when scanning for fraud. And on average, less than half of structured data is used to make decisions.

Human analysts are often the most adept at detecting contextual particularities inherent in unstructured data like written and spoken language, but automated tools can help lighten the load. The study also found that 80 percent of analysts’ time is spent discovering and preparing data.

 Is Automation the Answer?

Machine learning (ML) solutions use AI to analyze human interactions and form predictive data to anticipate the most likely outcomes. This technology falls into two categories: supervised and unsupervised. Supervised ML systems rely on input and output data to learn how details are related, while unsupervised ML only requires input data.

Companies can use unsupervised ML solutions to understand their vulnerabilities better and predict fraud trends instead of reacting to them after the damage has been done. Anti-fraud efforts are not the only potential benefits, however. Implementing unsupervised ML can also reduce demands on human employees.

eCommerce solution providers have been teaming up with digital fraud firms to use AI to help merchants guard against potential fraudsters. These services can notify users of fraud’s likelihood, automatically reject high-risk transactions and review suspicious orders.

Oisin Hanrahan, CEO of on-demand professional hiring platform Handy, recently described how the company takes a hybrid approach and relies on AI, ML and human insights to stay ahead of fraudsters.

The company’s solutions use AI and ML tools to review individuals’ behaviors, identify trends over time and engage with its team of data scientists to understand important correlations. Advanced learning tools help new users onboard smoothly, but human beings will still be necessary to provide support and ensure seamless user experiences.

Jon Prideaux, CEO of mobile payment platform Boku, explained during a recent conversation with PYMNTS what offer fraud is and how exploiting sign-up offers can compromise marketplaces’ performances. The goal is to let marketplaces continue to put out attractive initial offers to bring in consumers, while equipping merchants with tools for the mobile, marketplace and sharing era.

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Latest Insights:

Our data and analytics team has developed a number of creative methodologies and frameworks that measure and benchmark the innovation that’s reshaping the payments and commerce ecosystem. In the November 2019 Mobile Order-Ahead Report, PYMNTS talks with Dan Wheeler, Wahlburgers’ SVP, on how the QSR balances security and seamlessness to secure its recently launched WahlClub loyalty program.

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