How to Prevent Fraud Using Big Data Analytics

With the evolution of digital landscape and the increase in online businesses, a transformation is taking place as to how people transact with money. Paper currency is rapidly becoming an ancient concept, and people are increasingly adopting plastic money and digital currency.


Although the ever-evolving digital realm seems thrilling, it is unpredictable, as well. Given that digitization is largely dependent on the use of the internet, enhancing online security to prevent fraud remains a major challenge.


This is where big data analytics step in!

Big Data is becoming a critical emphasis of study in the IT field.

The significance of using big data to prevent and detect fraud is becoming clearer each day.  Analytics offers a deeper understanding of suspicious activities, helps determine patterns, and navigates unusual transactions to detect and prevent fraud.

Big data makes behavioral analysis and real-time detections easier, giving a fresh new perspective to those seeking innovative and efficient ways to prevent fraud.

The following are ways to prevent fraud using big data analytics:


Identification of Suspicious Activities.


Big data analytics allow for quicker identification of suspicious activities or unusual behavior in real time before the damage is done. Analytics dive deep into all the available data, combining live transactions with the information from existing data warehouses to detect fraud as it occurs. All the financial transactions are screened against pre-set rules for fraud detection. The available data is combined with data from the customer’s social feed, and geo data from their smartphone apps and weblogs, etc. Big data also analyzes transactions for the past weeks, months, and year to identify consumer behavior and detect fraudulent patterns.


Analytics detect fraud attack patterns.


Typically, most fraudsters have a telltale pattern and tie scams to seasonal events. For example, tax frauds are most likely to occur during the tax season, and online transaction frauds reach a peak during the highest sale season. As such, predictive analytics can help uncover fraud patterns and anticipate the attacks well in advance to give enough time to strengthen security measures.


Analytics also help establish a relationship among several suspicious activities occurring in a single account or fraudulent activities across different accounts. With the help of deep analytics, commonalities can be established and fraud can be identified. For example, if several transactions are taking place from different devices in one day, the data generated will raise an alarm.


Data provides faster resolutions.


The faster that fraud is detected, the faster actions can be taken to remedy the situation. Big data enables faster fraud detection, therefore minimizing loss. Big data uses large data sets to make detection algorithms more accurate, thus reporting much faster. For instance, if two transactions take place from the same credit card in different cities within a short duration of time, it will raise an alarm and enable the bank to warn their customer.


Big data presents a reliable and effective way for financial institutions to detect and prevent fraud while enhancing the security of digital transactions.