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Big Data for Fraud Prevention

Big data analytics is an effective solution for identifying behavioral patterns and establishing strategies to help detect and prevent fraud in various business sectors.

Monday, September 6, 2021

Most companies are not aware of the information they have and how to leverage, analyze and understand it, which can result in the loss of a large amount of potentially useful data by normalizing fraud and other criminal activities in their processes and make them difficult to prevent and detect.

Also read: "Walmart Vs. Soriana: Consumer Foot traffic Analysis"

Fraud detection through Big data analysis, data mining and machine learning models uses trends, patterns and behaviors to detect and prevent suspicious activities in purchasing processes, credit activities, accounts or transactions, internal and external processes, among others. This makes possible to automatically detect fraud and allow organizations to consolidate, map and normalize large amounts of data that can be effectively analyzed to design strategies that detect and establish connections between anomalous trends, notice a cybernetic attack or mark a security breach.

Here are some of the general benefits of the implementation of fraud analytics in businesses:

Identify irregular and unusual patterns, business problems or risk areas where activities or processes may result in fraud.
Saves costs and maximizes revenue.
Detects anomalies across channels, comparing data from different information sources to find discrepancies, such as social networks, databases, call centers, etc.
Predicts suspicious activity before it causes damage to an organization's assets or goods.
It provides an internal view of processes and identifies where there's more opportunity for fraud activities by creating strategies that are better adapted to the operations of a business.

You may be interested in: "Location analytics can drive retailers to success: Case Study Home Depot Vs. Ace Hardware."

As specialists in data analysis, at PREDIK Data-Driven we identify anomalies in payments, fraud problems in systems or processes, test the effectiveness of cybersecurity controls, identify and predict patterns and behaviors in internal and external data sources processes where fraud can or is more common, among many others.

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