Data Analysis for Fraud Detection

By CFO Tech Outlook | Monday, September 24, 2018

Fraud is a vital issue in the finance domain, affecting banks and consumers alike; it is the hidden threat that can eat away the fabric of an economy. Leveraging real-time analytics and machine learning for many initiatives like risk management, fraud detection, compliance, and consumer metrics, leading banks and capital markets firms are on a quest to gain a competitive edge and comply with regulations that are required for extreme performance on big data workflows.

Potential cases of fraud across different verticals are being identified by machine learning that is being used to fight money laundering. Tools that compare millions of transactions at high speeds are being developed to distinguish accurately between the legitimate and fraudulent transactions on the buyer and seller sides.

An in-memory computing can handle real-time massive workloads and processing tasks at millisecond speeds, which is far removed from traditional databases. A smarter and faster machine learning insights is brought forth that provides a faster and simpler workflow.

A successful integration into business applications is required for machine learning to operate effectively. If the last lap of application integration is not possible, then a sophisticated statistical model or organized data won’t make much of a difference as the benefits of machine learning cannot be fully tapped into by financial institutions. The insight that a financial transaction is fraudulent can be made actionable through this essential integration; otherwise, the fraudulent attempt will go unchecked.

A significant challenge for fraud detection solutions is the undisputed presence of a wide variety of threat types. More advanced techniques are being used by criminal minds in order to siphon away revenue from companies. In the light of the complexity involved, fraud detection techniques used in data analytics need to excel at creating connections from raw data and ascertaining which interactions convey potential fraudulent behavior.

A basic, retrogressive analysis is not what defines fraud prevention but it is more about connecting the data ideas in order to detect and prevent potential fraudulent behavior. Any organization can determine the triggers and situational interactions, likely to produce fraudulent activity by enabling the use of fraud detection software. Based on comprehensive data analysis, this level of sharp prognosis is the solution that empowers a company to stop fraud in its tracks while saving time and money.

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