The Next Big Data Arena: Fraud Prevention


Although companies have spent millions of dollars on analytics software designed to sift through mountains of unstructured data for signs of theft and fraud, a recent flurry of high-profile hacks could drive those firms to double down on “counter-fraud” tools.

At least, that’s the hope of IBM and other tech vendors.

IBM has announced a “Smarter counter fraud” initiative, involving software that scans for “fraudulent indicators.” The platform can “learn” from previous attacks to predict new ones, as well as visualize fraud patterns. IBM claims the underlying analytics are more than capable of drawing the non-obvious connections so essential in sniffing out electronic theft. In keeping with Big Blue’s recent embrace of the cloud, these tools are offered as a service.

But sophisticated analytics only go so far—at a certain point, an actual human being needs to take a look at the data indicating possible fraud and make a decision about what to do. That’s where things get problematic: according to an extensive report by Bloomberg Businessweek, for example, company officials at Target declined to respond to data suggesting a massive breach was in progress, which eventually led to the theft of millions of customers’ personal information.

Target security personnel and law enforcement officials all confirmed to reporters working on that story that the automated fraud-detection system, known as FireEye, worked perfectly; none seemed too clear on why warnings were ignored by the company’s executives. The company later admitted to disregarding the information in a widely circulated statement to the press: “With the benefit of hindsight, we are investigating whether, if different judgments had been made, the outcome may have been different.”

Given the need to keep personal data safe in a risky environment—to retain customers, if nothing else—companies will spend more on analytics tools helpfully promoted by tech vendors who see a sizable buck to be made in fraud protection. No matter how advanced the software, however, poor human decision-making is a problem that so-called “Big Data” can’t solve.


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One Response to “The Next Big Data Arena: Fraud Prevention”

  1. John Doe unemployed americann citizen

    personally I’m developing my own fraud detection system for welfare and medicare fruad and have found that both systems are to dependant on human sight rather than computers built with AI in them to provide a fool proof detection for stopping fraud at it’s inception. Seems to me that people have it in thier minds that they can scan more by eye than a computer can by the data. This needs to happen ASAP as their are billions that are being stolen from bot systems and no governemnt agency FBI included can possibly stop it. We can only prevent the crap from leaving the horse and after verification can and should the monies be released to the people who need it. Case in point look at the medicare fruad being perpetrated on the american people. I bet no one knew that the claim only has 72 hours in the system before it has to be paid. Well as a person who spotted this problem years ago while working on the system that processes those claims I asked where is the fraud detection in the system. I was summarilly released from my job as a Sr oracle DBA but the question was never answered and the problem still exists. Upon further investigation I came to the conclusion that if I were robbing peter and paull was paying me for the service why would I care is fraud existed in the system. the nswer to my question is I WOULDN’T CARE EITHER. I’m getting paid on both ends. So I have been hashing a solution that involves the Social Security Administration and the Department of Human Services and of course the FBI who would be working the law enforcement end of things and nd investigating the possible fraudulent claims before they ever got close to the 72 hour limit. It would limit the liability of the federal governement and insure that the tax payer dollars couldn’t be stolen by some unscrupulous thieving doctors looking to make a quick buck.