Comprehensive Payment Fraud Security Detection and Prevention
Fraudulent activities happen every day across a variety of industries, causing trillions of dollars in losses each year. While financial services and banking are among the hardest hit industries, other frequently targeted industries include retail, healthcare, information technology, federal government, public administration, and utilities.
Banking environments vary and complicate the ability to address the variety of fraud problems that might imperil the specific payment types an institution might offer. Gurucul’s comprehensive payment fraud security solution addresses a broad variety of payment types such as:
- Instant Payments
- BACS SEPA payments
- ATM/debit transactions
- ACH/bulk payments
- Bill payments
- P2P/email payments
- Checks processing
According to the Association of Financial Professionals, payment fraud activity continues to increase, and there are no signs of it abating any time soon. The level of payment fraud activity in 2018 was the highest on record, rising at a greater pace than in 2017. 82% of finance professionals report that their companies experienced attempted or actual payment fraud in 2018 — the largest percentage on record.
Limitations of Legacy Fraud Prevention Solutions
Legacy payment fraud prevention platforms have limitations that result in too many false positive alerts, loopholes that allow the fraudulent activity to go undetected, and determinations of fraudulent activity after the fact when it’s too late to prevent the loss.
A key factor vexing legacy fraud management systems is the emergence of so many different channels of potentially relevant data that reside in completely different systems and formats. Banking systems are a good example of this issue. Retail banks have their core systems that commonly handle transaction accounts (i.e., checking and savings) as well as mortgages and other loans. Meanwhile, banking transactions take place in numerous other separate systems, including mobile applications, web applications, credit card processing systems, debit processing systems, and automated teller machines. A hacker could be creating fraudulent accounts and transactions on one system and they can’t be correlated with activities or behaviors on the other systems because the fraud platform has no way to link and associate the data that resides in incompatible file systems and formats.
Gurucul Links Payment Data Across Channels
Gurucul Fraud Analytics links data from a multitude of sources to provide a contextual view and to highlight anomalous transactions based on historic user and community profiles. It analyzes online and offline activity, including public records, contact center interactions, point of sale transactions, ATM transactions, and so on. Gurucul Fraud Analytics mines and normalizes data and then creates a risk score from 0 to 100 for fraud and abuse. It’s used for real-time decision making or batch scoring of an event. It also can provide scores and risk factors for other systems to use in a decision.
Gurucul’s solution can drive corrective or response actions to payment fraud in other systems based on the value of the risk score. Such actions can be automated in order to be actuated in real time or near real time. Suppose a retail banking customer’s risk score is calculated to be a 90. This can trigger an event to go into the banking system to block an active transaction on the customer’s account — say, to put a hold on a payment transfer request that would send money to another account. The system could then send an email to the account holder to ask her to verify that she initiated this request before the hold is lifted. Gurucul Fraud Analytics links transactions from various stages of the entire payment lifecycle providing a comprehensive and contextual view into all activities.
Gurucul is working with companies across many industries to address their payment fraud detection and prevention needs. While there are many different use cases, the theme that is common among them is that organizations want the ability to do cross-channel fraud detection, to aggregate and link more data coming from many different systems. It is this cross-channel capability that shines a brighter light on not just transactions but also subtle behavioral activities and peer group analysis that would otherwise go undetected.