The Challenge
Financial institutions face an ongoing battle against fraudulent transactions, which can result in significant financial losses and damage to customer trust. Traditional fraud detection systems often struggle to keep pace with evolving fraud patterns and may generate high numbers of false positives, leading to unnecessary customer friction.
Existing fraud detection systems built on platforms like Microsoft Azure may not provide the accuracy and confidence levels needed to effectively identify fraudulent transactions while minimizing false alarms. Financial data typically contains anonymized account information including incoming and outgoing accounts, transaction amounts, and balances, requiring sophisticated analysis to detect patterns indicative of fraud.
The challenge is compounded by the need to provide transparency and confidence in fraud detection models, allowing users to understand prediction probabilities and test hypotheses under unique circumstances.
Our Solution
We developed a fraud detection system that analyzes financial transaction data, identifying patterns and anomalies that indicate fraudulent activity. The system processes anonymized account information including incoming and outgoing accounts, amounts, and balances to generate predictive models.
Behind every dashboard and forecasted fraudulent situation that is highlighted, there is a model weighing probabilities and scanning the provided data to tell users where fraud is most likely occurring. The system provides probability distributions for each transaction, allowing users to understand both the likelihood of fraud and the confidence level of the prediction.
The platform includes interactive tools that allow users to test hypotheses and gauge how accurate the models are under unique circumstances. Users can adjust parameters using slider controls to simulate different transaction scenarios and see how the model responds, ensuring confidence in the output of the predictive models and how they work.
With additional data sources and information, our system can relate them to one another and build better models automatically, continuously improving detection capabilities.
The Impact
Up to 107% better fraud detection than existing systems
Our system has been able to catch fraudulent transactions significantly better than clients' existing systems built on Microsoft Azure.
Transparent probability-based predictions
The system provides clear probability distributions for each transaction, showing both the likelihood of fraud and the confidence level of predictions through narrower or wider distributions.
Interactive hypothesis testing
Users can test hypothetical situations and scenarios using interactive controls, allowing them to validate model accuracy under unique circumstances and build confidence in the system.
Continuous model improvement
The system automatically builds better models as additional data sources are integrated, improving detection capabilities over time without manual intervention.