How to Create Predictive Fraud Detection Using Big Data

How to Create Predictive Fraud Detection Using Big Data

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It’s only natural to start feeling concerned about whether your enterprise’s data is truly secure once you take a look at all of the high-profile data breaches that are happening. Fraud is a serious problem that can be very messy and complicated to fix. The conventional way of fighting hacks and attacks in the past was to detect them and try to pick up the pieces as quickly as possible in the aftermath of a breach. However, the stakes are officially too high to simply build a security protocol that’s strictly defensive. A successful plan for fraud management and prevention needs to be offensive and predictive.

How Big Data Is Useful for Fraud Prevention

The tactics being used by hackers are sophisticated, evolving and hard to detect. An enterprise should never be placed in a position to catch up with hackers. The good news is that data can be used as a powerful gatekeeper that predicts, detects, stops and reports threats before sensitive information can be accessed and swiped. Data can be used to prevent threats because it allows enterprises to predict threats based on user patterns, customer activity and trends in the broader world. Schemes can be detected by referencing and analyzing data that is being pulled in from various points. Here’s an example of what that looks like in the real world:

  • -Tracking how often a user or customer accesses an account from a mobile device or computer
  • -Tracking geographical login locations
  • -Calculating average engagement times for typical or specific users

Having the ability to take in and process this information creates a baseline for normal, non-suspicious activity. This baseline can then be used to detect any anomalies that could indicate that an unauthorized user is accessing an account, network or server. Specific triggers can also be built into a system based on the known behaviors of certain malware programs or hacking schemes. This means that a platform can intuitively know to shut out certain users or access points even if it has never previously encountered the suspicious malware or activity that is posing the danger. The efforts of individual enterprises and the collective push for better security measures can lead to a world where hackers are eventually cut off at the source and threats are minimized.

Bringing in the User to Prevent Fraud

While a platform that can analyze data intelligently is useful for keeping dangers at bay, it’s also essential to have a human component when putting together a comprehensive and effective plan for fraud management. End users should be part of the solution instead of being viewed as part of the problem. In addition to investing in employee training for good cyber practices, it’s necessary to have someone who is capable of looking at data and making sense of it. This will enable an enterprise to know when it’s time to adjust or tweak the specific information that a platform receives, reports on and acts on. Enterprises can also use reports to decide how to adjust responses and add new trigger points. A platform should also be able to deliver clear and easily digested reports that decision makers can get clear pictures from. Visualized reports that show how activity breaks down in terms of time, frequency, location and other factors can help decision makers to get a picture of what typical activity looks like. This is how fraud is ultimately detected and prevented.


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