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How Graph Analytics Helps with Fraud Detection

Data science has become an accessible and approachable organizational aspect that can be used for the company’s data analysis with gigantic machine learning algorithms. It can efficiently extract conclusive elements from the metadata, comprehend recurring patterns, and forecast trends. Data science also helps organizations in search engine optimization and potential frauds detection, which brings us to an important question. What if the problem is not about operating the extensive data but how to protect it? In her article for ‘Towards Data Science,’ Lina Faik shares how graph analytics help fraud detection.

Detecting Frauds

It is a challenging task in the banking sector to get hold of shell companies that conceal fraudulent financial transactions, as they usually rely on transaction amounts and similar banking traits. Likewise, detecting frauds in the healthcare sector is a problematic task. There are primarily four fraud models found in the health care sector, i.e., billing a service not provided, replicating a claim for free benefits, falsifying the provided service, and charging a more expensive service than provided. It involves peers, doctors, beneficiaries, and medical staff acting together to develop false claims.

How Does it Help?

Once a dataset has been converted into a graph, a wide variety of information can be divulged from each node. Using graph analytics can extensively improve the predictions of the business model. It uses existing network configurations to detect new patterns and add essential data on how different business divisions are interconnected and the basis of each relationship. However, using graph analytics can result in high computational expenses. It would be better to add on some manually constructed features from the selected information in the engineering model.

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