Leveraging Technology Tools for Efficient Fraud Detection

Fraud has become a menace for companies across industries as it causes loss of trillions of dollars in a single year. According to a 2018 Global Economic Crime and Fraud survey by PwC, 49 percent of businesses had experienced fraud and economic crimes over a period of two years. While the finance industry is the worst-hit by fraudsters, many other industries like retail, healthcare, information technology, public administration, and many others have also experienced fraudulent activities.

Traditional fraud detection solutions have limitations that enable malicious activities to go undetected. Lack of efficient predictive measures hampers the ability of these platforms to produce evidence of malicious activities before the fraud has taken place. A major shortcoming of these legacy platforms is that the data fed into the analytics platforms lack context, preventing IT from making an accurate assessment of risk. These platforms also fail to establish a correlation between the activities of different channels.

Many recent technological platforms like big data, artificial intelligence (AI), machine learning (ML) and other intelligent tolls offer new approaches to fraud analytics. These platforms can detect any abnormal behaviors and activities in real-time to provide an accurate risk assessment. Let’s delve deeper to discuss the elements required to implement these technologies for immaculate fraud detection for a company:

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Big Data Store: An effective data analytics platform enables a company to uncover information which includes hidden patterns, unknown correlations, and trends. Enterprises need a suitable architecture that can scale billions of data points over time. The big data system should also support structured as well as unstructured data sets.

Data sources: Analysis of Data from multiple sources provide more meaningful insights into the patterns, correlation, and trends. Enterprises should ensure that their processing engine ingests data from various sources including online and offline data.

AI and ML-Based models: AI and ML-based models can analyze large data sets automatically and in real-time to detect any malicious activities. These platforms use algorithms to look for patterns in data, enabling companies to make informed decisions.

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