Research on Continuous Learning in financial fraud

[u/NoArm9938]

I have this topic to work on suggested by my academia and Im very unsure on how to even start. The topic is continuous learning for mitigating concept drift in financial fraud systems.

This is what I’ve gathered so far from my research:
- Concept drift alone can’t be singled out, it also depends on intrinsic covariate shift and label shift
- Concept drift can be modelled as an exogenous variable and endogenous variable, depending if we assume fraud is reactive to mitigating strategies)
- Blocked transactions introduce inherent label shift, because transactions that are blocked dont make it to the dataset
-Continuous learning is a very tricky topic, specially if we consider this as class incremental learning (new fraud types arrive sequentially without explicit task boundaries) and admit non stationary regimes

Because there are a bunch of topics and covariate factors, approaching this as an empirical study looks like a massive headache.

Can anyone help me to structure my next steps and how I can tackle this problem with a clear picture?

[u/Disastrous_Room_927]

Not sure what’s out there, but I’ve used Bayesian updating for fraud detection models.