The problem SafePay solves

Our AI/ML-driven credit card fraud detection system solves the critical problem of financial fraud. By identifying and preventing fraudulent transactions in real-time, it safeguards financial institutions and their customers from significant financial losses and enhances the overall security and integrity of the financial ecosystem. This system helps reduce the incidence of fraud, protect sensitive user information, and maintain trust in digital payment methods.

Challenges we ran into

Specific Bug or Hurdle and How We Overcame It

Hurdle: High False Positive Rate

One specific challenge we faced was a high false positive rate during the initial stages of model testing. This meant that our system was flagging too many legitimate transactions as fraudulent, which could inconvenience users and undermine trust in our system.
How We Overcame It:

1. Data Rebalancing: We addressed this by rebalancing our dataset to ensure a more even distribution of fraudulent and non-fraudulent transactions. This helped the model learn better distinctions between the two classes.

2. Feature Selection and Engineering: We revisited our feature set and engineered new features that provided better insights into transaction patterns. This involved incorporating domain knowledge and experimenting with various feature combinations.

3. Model Optimization: We tested different machine learning algorithms and hyperparameters to find the optimal combination. Techniques like cross-validation helped us select models that generalized better to unseen data.

4. Threshold Tuning: We fine-tuned the decision threshold of our model to find a balance that minimized false positives while maintaining a high detection rate for fraudulent transactions.

5. Ensemble Methods: Implementing ensemble methods like random forests and boosting improved model performance by combining the strengths of multiple algorithms.

6. Regular Feedback and Iteration: We continuously collected feedback from real-world deployment and iterated on our model based on new data and insights. This iterative approach allowed us to refine the system progressively.

By systematically addressing the issue through these steps, we were able to significantly reduce the false positive rate, enhancing the accuracy and reliability of our fraud detection system.