One of the issues that I faced during my short stint building ML models for fraud detection in debit card transactions was dealing with class imbalance. I was not completely convinced that over sampling techniques or under sampling techniques would work. My initial experiments just resulted in more false positives. Just curious if you guys faced similar problems.
The other point I bring about is rather rhetorical - There are no open standards, model baselines and datasets in the Fraud domain. Compare building a model for fraud detection to building a model for image recognition or object detection There is a standard baseline, standard datasets and your model competes against that baseline. Because of the open nature of image recognition, the models have improved astronomically. I feel that a lack of such openness is fraud is holding back on innovation. I could be wrong in this assessment so please correct me if so.
The other point I bring about is rather rhetorical - There are no open standards, model baselines and datasets in the Fraud domain. Compare building a model for fraud detection to building a model for image recognition or object detection There is a standard baseline, standard datasets and your model competes against that baseline. Because of the open nature of image recognition, the models have improved astronomically. I feel that a lack of such openness is fraud is holding back on innovation. I could be wrong in this assessment so please correct me if so.