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 algorithms must be able to quickly adapt and learn from new trends.
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 it always learns from the latest trends.
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.