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Fighting Fraud with Big Data Analytics
More companies today are using IT systems to store and manage enormous business data pouring out of the burgeoning communication channels. As more business components and the real world environments embrace digital technologies, fraudsters too are becoming IT-savvy. In the wake of these developments where a fraud can be hidden in large volumes of data, manual checks to detect threats doesn’t result in fruitful business outcomes. The financial institutions, in particular, are investing heavily to prevent fraudulent attacks in the wake of increased threat to user data. The advancements in technology are helping security professionals to adopt smart ways to detect fraudulent activities.
For instance, big data has emerged as the potent weapon to combat fraud. Big data analytics enables financial institutions to analyze massive data volumes and create precise predictive models to identify fraud in real time and take preemptive measures to thwart the damage.
The unique fraud detection and prevention techniques like real-time behavioral analysis through big data analytics paint a new outlook to the fraud detecting techniques. The use of data analytics allows institutions to detect unusual activities, obtain behavioral patterns, and locate uncommon transactions that in turn help prevent fraud. To protect the data that are handled by the third parties, organizations need to design procedures and policies around that data.
In general, companies restrict their fraud data analysis only to the financial aspect. More than financial data, data analytics also plays an important role in analyzing comprehensive data coming from internal and external sources to predict and create early-warnings for fraud. The use of big data analysis helps businesses to detect potential fraud instances and employ effective threat mitigation programs.