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Credit Card Fraud Detection

Access to credit proves indispensable in times of burning need. While a credit card can offer cash benefits, attractive discounts, and profitable deals to its customers, one can end up paying more than they initially bargained for. A user’s credit card PIN alone is adequate for a scammer to commit financial fraud.

According to Juniper Research, in the years to come, businesses are projected to incur losses of more than US$200 billion from online fraud. This amount is attributed to the rising number of fraud attempts that are sophisticated in nature. Despite the strong countermeasures, fraudsters have managed to evade detection successfully.

Advanced graph analytics service in graph databases enables detection of suspicious patterns way more advanced than others. This plays a pivotal role in identifying and preventing fraudulent activities online prior to thier occurrence.

Key Components of CreditCard Fraud Detection include

Transaction Monitoring

Real time monitoring of credit card transactions is essential to detect unusual patterns and behavior that might indicate fraud.

Machine Learning Models

Historical transaction data can be used to train the conventional machine learning models to identify patterns of normal versus abnormal behavior.

Behavioral Analysis

Analyzing the deviation in the typical user behavior, spending patterns at unusual times and locations are also an important aspect to detect fraud.

Credit Card Fraud Detection Challenges

Limited Relationship Analysis

Traditional database approaches are not well suited for analyzing complex connections and relationships between the entities. Hence, graph databases play a crucial role in identifying suspicious patterns and potentially fraudulent activities.

Imbalanced Data

Data where fraudulent activities are infrequent compared to legitimate transactions poses a challenge for effectively detecting fraud. Traditional approaches may struggle to handle such issues, resulting in biased results.

Lack of Machine Learning/Analytics

Frauds can have sophisticated patterns or have multiple layers of deception. Traditional approaches do not have the ability to analyze and capture such complex fraudulent schemes.

Enhancing Credit Card Fraud Detection with TigerGraph

Conducting advanced relationship analysis such as identifying users engaging in coordinated fraudulent activities, can be significantly simplified using graph traversals. This, in turn, provides ease of identifying and analyzing suspicious patterns.

Combining Machine Learning and graph can deliver better results altogether.

TigerGraph allows the implementation of custom algorithms and extensions that can detect anomalies in the data. Thus, improving the overall performance by filtering out suspicious transactions.

Solution Overview

TigerGraph enables real-time analysis of thousands of data points and the relationships between them to deliver prompt fraud alert scores. Graphs can be employed for fighting financial fraud by analyzing the links between different nodes to unveil indicators of fraudulent behavior. A key feature of TigerGraph outperforms the traditional database approaches, given its ability to scale and expedite. While traditional database approaches rely on huge table joins, graphs are more efficient in handling greater query loads effectively.

The solution includes designing a schema on TigerGraph and loading the appropriate data. Graph algorithms like Eigenvector Centrality and Louvain Community Detection are used to enhance the existing features in the graph. TigerGraph is used as a feature generator in this use-case. These newly generated features are then used as inputs to extensively train multiple machine learning algorithms and a pipeline is created to predict if a transaction is fraudulent or non-fraudulent.

Overview of
The Graph Schema

A graph representing similarity between multiple transactions.

Snapshots of the Dashboard

Transactions Insights

Community Analysis

Machine Learning – Performance Metrics

Explore the Possibilities with Us

We invite you to explore our TigerGraph Data Analytics CoE and discover how it can benefit your organization. Whether you are looking to enhance your data analysis capabilities, optimize your processes, or uncover hidden patterns in your data, we are here to assist you every step of the way.

Contact us today to learn more, schedule a consultation, or start a collaborative project. We are excited to embark on this data-driven journey with you!

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