Credit Card Fraud
Detect Unauthorized Credit Card Transactions with Machine Learning
As multiple channels for interaction with retailers and e-commerce continues to increase, so do new opportunities for threat actors. This growth of channels as well as the increasing volume of customers and transactions presents a heightened risk of credit card fraud. This type of fraud often goes undetected under the massive number of alerts generated by legacy rules-based fraud prevention solutions. These challenges require accurate detection of anomalies, risk-based alerting and response automation capabilities. Gurucul Fraud Analytics powered by machine learning based predictive anomaly detection is the right solution for the credit card fraud complexities of today.
Gurucul offers an advanced fraud analytics solution which uses a combination of supervised and unsupervised algorithms to detect outlier risky behavior indicative of credit card fraud such as:
- Unusual account / profile changes
- Abnormal high value transactions
- Geo-location anomaly
- Unusual device usage
- Suspicious charges from merchants, etc.
Gurucul Fraud Analytics provides a comprehensive case management to manage and track high-risk credit card fraud alerts and subsequent processing. Alternatively, the product also supports integrations with external ticketing systems.
The Gurucul Fraud Analytics engine risk scores users and devices or entities associated with credit card transactions. Risk scores range from 0 to 100, and the higher the score, the higher the risk and more likely the underlying activity is fraudulent in some way. First, a baseline is established for each user (and entity), based on historical transactions. Then, the system calculates risk scores with real-time incoming data and activity. The system provides a continuous risk assessment, based on transactional and non-transactional attributes of the person’s (and entity’s) interactions of the day.
For example, a consumer with a credit card has a low reputation risk score. When he loses his wallet, a thief decides to use the credit card in a series of stores. The thief starts using the card in ways that the real cardholder would not. In real time, these anomalous events drive up the overall risk score, to a point where alerts are triggered, and mitigation steps are taken.
The purpose of risk scores is to drive decision-making and to enable timely response actions. The accuracy of Gurucul’s behavior analytics engine returns only the most meaningful results, which are prioritized and presented through a dashboard to give a risk manager the necessary information to act. While a legacy fraud management platform may return hundreds or even thousands of alerts per day, Gurucul’s Fraud Analytics product returns tens of alerts.