Effective fraud protection relies on deep analytics
Banks are seeking better fraud protection with deep analytics. These tools go beyond simple account monitoring in an attempt to mitigate the billions of dollars fraudsters steal each year through online and mobile channels, according to the Aite Group.
ABA Banking Journal reports that analytics can be the virtual eyes and ears of a bank, providing fraud protection in the same way that observant tellers once watched customers' mannerisms and listened to verbal cues to detect suspicious activity in person. For this reason, nearly one in five banks cite fraud and risk management as one of the reasons they expect to boost spending on predictive analytics in the next couple of years.
Behavioral analytics offer faster responses
Behavioral analytics is becoming an increasingly important fraud detection tool. Information Age reports that behavioral analytics focusing on the observed characteristics of bank customers enables risk managers to detect and respond more quickly to suspected banking fraud by continuously monitoring activity across multiple channels. When a transaction is outside of an account holder's typical behavior, such as an abnormally expensive purchase or a high number of transactions in a short amount of time, the bank can alert the customer about the suspicious activity and block further transactions.
Beyond examining transaction amounts and frequency, behavioral analytics also examines how users behave when using an online or mobile account. These tools can see if users navigate the site the same way, input data in the same manner and consistently enter information. Behavioral analytics also examines behavior across accounts to see if there are several identical patterns. For instance, a group of people making identical transactions could indicate a distributed attack. These attacks are invisible to traditional monitoring because the fraudster uses each account only a few times, but analytics spots the related transactions across accounts.
By combining information from different log monitoring alerts, fraud analytics uncovers threats that may otherwise be overlooked, according to BankInfoSecurity. One of the weaknesses of many organizations' fraud protection capabilities is there's no way to relate seemingly individual alerts to show a pattern of fraud. Gartner Analyst Avivah Litan told the source there can be as many as 500,000 alerts in a single day, far too many for a company to review on its own. Analytics, however, can reveal clusters of related alerts, allowing banks to profile the users and devices. Such profiling may help stop breaches or, at least, limit their impact.
Aite Group Analyst Julie Conroy goes a step further, calling it counterproductive to monitor logs without analytics because, on its own, log monitoring produces too many false positives.
Graph analytics reveal fraud indicators
By mapping data into objects or nodes and then plotting the connections or edges between these objects, graph analytics provides a visual representation of data from internal and external sources. By displaying data in this manner, graphic analytics enables banks, credit card companies and other users to quickly recognize relationships that could indicate fraud, according to Datanami. For example, Computerworld reports that graph analytics uncovered fraud at the Swiss branch of HSBC by uncovering the relationships between criminals, drug traffickers and tax evaders.
The majority of senior executives expect fraud attempts to increase in coming years, according to IDology, and banks will continue to rely on analytics as a critical fraud protection technology to thwart attacks. Learn more about leveraging the advanced analytics within IBM Counter Fraud Management to detect financial crime earlier and respond more quickly with necessary countermeasures in a brief demo video.
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