The Challenge.
- Detecting fraudulent behaviour is one of the most important problems in the banking industry.
- Fraud detection models must be both robust and extremely fast to ensure quick reaction to a fraudulent transaction.
- The fast-moving landscape of financial crime means algorithm must be able to quickly adapt and learn from new trends.
The Solution.
- Designed and built a Machine Learning model to identify fraudulent credit card transactions.
- Integrated the model into enterprise systems to create alerts in real time when detecting fraudulent behaviours.
- Built a continuous feedback loop to continue training the Machine Learning model so always learns from the latest trends.
The Results.
- Reduced costs by decreasing the number of fraudulent transactions allowed to go through.
- Improved customer experience by more quickly identifying and blocking fraudulent behaviour, as well as reducing the number of cases where a transaction was falsely labelled as fraudulent.
- Reduced call center costs by decreasing the volume of calls for claims related to fraud.