That this question uses the word Feedback and made reference to more than one channel of feedback, "Reward and regret," indicates a comprehension of corrective signaling. Some of the reinforcement learning literature that appears scientific lacks that understanding, so beware of that.
The temporal delay of fed back information is not unique to the case of banking fraud detection. It is central to security breach detection in general, including web site hosting and telecommunications hacking. It is also central to many other technology domains ranging from cyber-combat to chemical engineering to petroleum exploration.
Early control systems were of the PID form used in speed or direction governors. In those, temporal elements were only analyzed to avert oscillation, overshoot, and undershoot. Those are still relevant in fraud detection systems, but there are more requirements on the control system, specifically non-linearity in multiple dimensions.
Consequently, control theory has been extended more in the direction of measuring behavioral wellness. Early temporal elements in digital systems included random access memory for applications and persistent memory for programs and data. With the emergence of production ready AI, the temporal elements include acquired rules, fuzzy rule weights, convergence of network parameters corresponding to machine learning components, and other learned information.
The proof of concept in financial fraud detection is the same as for many other domains where the feedback can occur minutes, hours, days, or months after a decision was made or a signal propagated through an artificial learning network: The neural networks of higher life forms, where asynchronous adaptation extends DNA based evolutionary adaptation, pain feedback is augmented by more abstract forms of feedback. In humanoid and primate species, social satisfaction involves a specific signaling that involves neuro-compounds such as serotonin and oxytocin.
This kind of adaptation fits in asynchronicity between reflex and DNA adaptation, in the realm that ranges from Pavlov's conditioned response to the social phenomenon of commitment. The importance of these capabilities is a result of the fact that not all sensory input that provides useful feedback about a behavior exhibited by the biological or artificial control system immediately after it is exhibited.
There is some suggested reading below, and you may want to examine Bayes' Theorem and some of the software you can download in nearly every common programming language that implements what is called Naive Bayesian Categorization. It is through the mathematics of probability theory that the best causal models can be realized. What you probably want to do is learn the key elements of modeling causality with numbers FIRST and then consider how basic probabilistic causality modeling might be augmented with artificial networks.
Although Richard Sutton and Andrew Barton's Reinforcement learning: An introduction (1998 MIT Press) is considered an excellent overview, the early comparative works provide a more direct path to answer questions about algorithms.
When you embark on algorithm development that involves both learning and asynchronicity, it is important to know at the onset that real time programming, such as is now used in high speed trading, is not for the faint at heart. Real time processing places two reliability centered requirements on algorithms, and they should be addressed stringently if you want a stable, low maintenance system that works.
- State-safe — In machine learning, functions that process feedback must not alter a set of interrelated parameters while in use by the forward propagation of the circuit.
- Re-entrant — In machine learning, an interrupt from an incoming signal and a change of state must not frustrate the intent of the algorithm interrupted upon its resuming.
Regarding attacks to banking systems, there will be escalation. The countermeasures the banks take will be met by the countermeasure of the thieves. It is a game, and the banking industry is wise to employ researchers and engineers that understand that learning is feedback dependent.
You may not find the best final designs in the liturature for this reason. Banks naturally employ nondisclosure agreements (NDAs) to keep attackers from gaining knowledge about defensive strategies through web searches. (If it is on the web, it is probably already hacked.)
As researchers and engineers they employ, we are wise to employ asynchronous feedback and real time learning in fraud detection systems and seek a more informed position to stay ahead of engineers that don't value property rights for anyone but themselves.
A Unified Analysis of Value-Function-Based Reinforcement-Learning Algorithms, Csaba Szepesvari, Michael L. Littman, October 27, 1998
Asynchronous Methods for Deep Reinforcement Learning, Volodymyr Mnih et al, University of Montreal, 2016
Deep Reinforcement Learning for Robotic Manipulation with
Asynchronous Off-Policy Updates, Shixiang Gu, Ethan Holly, Timothy Lillicrap, Sergey Levine, 2016
Dynamic causal modelling, K.J. Friston, L. Harrison, and W. Penny, Institute of Neurology, UK, 2003