Utilize AI algorithms not only to detect fraudulent credit card transactions but also extract actionable insights through analytics, thereby strengthening fraud prevention mechanisms
Talk to our expertsInfosys Card Fraud Detection employs AI algorithms for swift detection of fraudulent credit card transactions. Beyond detection, it harnesses advanced analytics to derive actionable insights, bolstering fraud prevention efforts.
Features of Confusion Matrix
The confusion matrix summarizes the test results of any fraud management system:
True positive
Both the expected and predicted values are positive (1-Yes), indicating that the model accurately identified a fraudulent transaction.
False positive
The model incorrectly predicted a positive outcome (1-Yes) or a fraud, when the actual value was negative (0-No), i.e., it was a genuine transaction which often leads to customer dissatisfaction.
True negative
Both the expected and predicted values are negative (0-No), indicating that the model correctly identified a non-fraudulent transaction.
False negative
The model incorrectly predicted a negative outcome (0-No) when the actual value was positive (1-Yes). In other words, it was actually a fraudulent transaction which can potentially result in financial losses for the bank.
Desired Outcomes and Minimization of Errors
True positive and true negative are desirable as they reflect accurate predictions. Conversely, banks aim to minimize false positives and false negatives to prevent customer dissatisfaction and financial losses, respectively.
This solution helps banks analyze test results across multiple models and approaches. Banks can determine their line of defense based on the following parameters:
- Improved recall: Fewer false negatives
- Balance with precision: Minimize customer dissatisfaction and the cost of manual interventions
- Evaluate cost/loss: Assess the financial impact of missed fraud alerts
When evaluating models, banks must decide on the tradeoff between prioritizing precision or recall.
