Wie messen Sie die Leistung von Betrugserkennungssystemen?

Measuring the performance of a fraud detection system is critical to ensure that it effectively identifies fraudulent activities while minimizing false positives and negatives.

The other way to look at it is as a customer insult. For example, if a customer is trying to make a purchase with their credit card and the transaction is incorrectly flagged as fraudulent, the customer might feel personally insulted by the implication that they are attempting fraud. This feeling of mistrust could lead to the customer deciding never to return to the business to avail themselves of its services. Such experiences can significantly impact customer loyalty and harm the business's reputation in the long run.

Imagine your system is a guard checking people. Sometimes, the guard mistakes good people for bad guys and stops them. This is called a "false alarm" or a "false positive". If this happens too often, people will get mad, and you'll lose money. So, you need to count how many times this happens compared to how many people the guard checks. You want this number to be as small as possible, but you also can't let real bad guys through.

Measure how often fraudulent transactions slip through undetected. Lowering this rate is crucial; every 1% improvement can potentially save your company from substantial financial losses.

Imagine your system is a guard checking people again. This time, the guard misses the bad guys and lets them in. This is called a "false negative". If this happens too much, bad people can steal money from your business. People will be mad, and you might get in trouble with the government. So, you need to count how many bad guys get past the guard compared to the total number of bad guys trying to get in. You want this number to be as small as possible, but you also can't stop all good people from coming in.

Use machine learning models to estimate both precision and recall, where TensorFlow can provide better model training and scikit-learn for check-pointing the metrics. They should incorporate realistic scenarios such as performing a large number of transactions at hours that are not standard operating times and hacked account ownership in the regions where they have been compromised to check the efficiency of the system and ensure that the framework for the detection changes with the tactics of fraud.

Evaluate how accurately your system identifies fraud. Precision measures the accuracy of flagged transactions, while recall assesses how well it detects all fraudulent activity. Balancing these metrics helps optimize system performance.

Combine precision and recall into a single metric. The F1 score gives you a comprehensive view of your system’s accuracy and reliability, helping to fine-tune detection capabilities.

In my opinion, measuring the performance of a fraud detection system is most effectively done through a cost-benefit analysis. This approach balances the costs of implementing and maintaining the system against the financial benefits of detecting and preventing fraud. It's not just about how many fraud cases the system catches, but also about whether the savings from preventing fraud outweigh the system's operational costs. By evaluating false positives, true positives, and the overall reduction in fraud-related losses, a cost-benefit analysis provides a clear picture of the system’s real value, ensuring that the investment in fraud detection is truly worthwhile.

In my experience managing fraud detection systems, focusing on reducing false positives and maintaining a balanced F1 score was crucial for improving overall system effectiveness and customer satisfaction.

It's not just about catching bad guys. You also need to think about how much your fraud system costs and how much money it saves you. You should compare different ways to stop fraud and see which one is best for your business. Think about how much it costs to build, run, and fix your system. Also think about how much it costs to deal with angry customers or pay back stolen money. But you also need to think about how much money you save by stopping fraud. This can help you make your customers happy and earn more money. By comparing the money you spend and the money you save, you can make your fraud system better and help your business grow.

Take, example related to cost-benefit analysis, a case like Target’s 2013 breach: the cost of fraud was far higher than the initial cost of systems integration approaches. To ensure that a business can determine whether the system is profiting by protecting assets while not overstraining the operations, previous mishaps are transformed into future measures against such mishaps and frauds.

Measure the accuracy of the fraud detection: - True Positive Rate: proportion of actual fraud cases correctly identified as fraudulent - False Positive Rate: proportion of legitimate transactions incorrectly flagged as fraudulent - Precision: proportion of flagged fraudulent trxs that are actually fraudulent - False Negative Rate: proportion of actual fraud cases incorrectly classified as legitimate - Accuracy: Overall % of correct predictions (both true positives & true negatives) Assess impact on the business: - Fraud Loss Rate: amount of financial loss due to fraudulent trx - Chargeback Rate: no. of chargebacks initiated by customers due to fraudulent trx - False Decline Rate: rate at which legitimate trxs are incorrectly declined