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Introduction

Banks and insurance companies globally are seeing an increasing trend in scams as fraudsters are becoming more sophisticated and can quickly change and adapt their approaches. Traditional methods of fraud detection play an important role in minimizing losses. However, they alone are unable to cope up with the increasing sophistication in frauds.

Even though we know that no method is perfect, Banks need to be agile to respond to threats and embrace new approaches and technologies to predict and prevent fraud.

One of the methods for Improvement fraud detection systems can be achieved by looking beyond the individual data points, to the connections that link them. Oftentimes these connections go unnoticed until it is too late, something that is unfortunate, as these connections.Oftentimes hold the best clues. Understanding the connections between data, and deriving meaning from these links, doesn’t necessarily mean gathering new data. Significant insights can be drawn from one’s existing data, simply by reframing the problem and looking at it in a new way: as a graph.

Graphs uncovering fraud rings and other sophisticated scams with a high-level of accuracy, and are capable of stopping advanced fraud scenarios in real-time.

Here, we will discuss some of the common patterns that appear in three of the most common damaging types of fraud.

  1. First-party bank Fraud
  2. Insurance Fraud
  3. Ecommerce Fraud
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