Fraud detection is the castle wall that keeps out the roving bands of fraudsters looking to steal your money and information. It is a combination of various technologies and tools that are designed to stop fraudulent transactions, whether they be credit card theft, account hijacking or even fake accounts. Best methods for fraud detection is the process of using strong risk-based data analytic tools, qualified or independent investigators and recognizing patterns in behavior that can help minimize fraud-related damage to your business reputation and financial well being.
Which model is good enough for fraud detection?
Statistical methods such as regression analysis, data matching and AI techniques like neural networks and machine learning are used to analyze the data in order to identify fraud-related patterns and anomalies. These are then compared against the normal behavior of a customer, and the systems generate alerts when they find suspicious activities. The system then either blocks the transaction or creates a case for investigation and review by an analyst.
Machine learning in fraud detection is an incredibly powerful technology that uses algorithms to study reams of historical data on your customer transactions. These algorithms identify patterns of real fraud, assumed fraud and genuine transactions and then use this to spot fraudulent activity going forward. This helps to eliminate instances of false positives and false negatives, making it an invaluable tool in fighting fraud.
This technology also comes in handy when it comes to AML (Anti-Money Laundering) monitoring, which involves assessing every deposit, withdrawal and transaction for suspicious activity. This helps you to meet regulatory requirements and ensures that you are doing your due diligence on customers.
