# Deep Q-Network-based Adaptive Alert Threshold Selection Policy for Payment Fraud Systems in Retail Banking

arXiv: https://arxiv.org/abs/2010.11062 · HTML: https://mlsanity.com/paper/2010.11062/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Oct 2020.

## Built on by (most cited first, 10 of 10)

- [Graph Computing for Financial Crime and Fraud Detection: Trends, Challenges and Outlook](https://mlsanity.com/paper/2103.03227/) (2021)
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- [Year-over-Year Developments in Financial Fraud Detection via Deep Learning: A Systematic Literature Review](https://mlsanity.com/paper/2502.00201/) (2025)
- [Application of Deep Reinforcement Learning to Payment Fraud](https://mlsanity.com/paper/2112.04236/) (2021)
- [LLM-Enhanced Self-Evolving Reinforcement Learning for Multi-Step E-Commerce Payment Fraud Risk Detection](https://mlsanity.com/paper/2509.18719/) (2025)
- [Non-Parametric Stochastic Sequential Assignment With Random Arrival Times](https://mlsanity.com/paper/2106.04944/) (2021)
- [From the Pursuit of Universal AGI Architecture to Systematic Approach to Heterogenous AGI: Addressing Alignment, Energy, & AGI Grand Challenges](https://mlsanity.com/paper/2310.15274/) (2023)
- [Temporal Knowledge Distillation for Time-Sensitive Financial Services Applications](https://mlsanity.com/paper/2312.16799/) (2023)
- [mt5se: An Open Source Framework for Building Autonomous Trading Robots](https://mlsanity.com/paper/2101.08169/) (2021)
- [Tradeoffs in Streaming Binary Classification under Limited Inspection Resources](https://mlsanity.com/paper/2110.02403/) (2021)

## Abstract (arXiv)

Machine learning models have widely been used in fraud detection systems. Most of the research and development efforts have been concentrated on improving the performance of the fraud scoring models. Yet, the downstream fraud alert systems still have limited to no model adoption and rely on manual steps. Alert systems are pervasively used across all payment channels in retail banking and play an important role in the overall fraud detection process. Current fraud detection systems end up with large numbers of dropped alerts due to their inability to account for the alert processing capacity. Ideally, alert threshold selection enables the system to maximize the fraud detection while balancing the upstream fraud scores and the available bandwidth of the alert processing teams. However, in practice, fixed thresholds that are used for their simplicity do not have this ability. In this paper, we propose an enhanced threshold selection policy for fraud alert systems. The proposed approach formulates the threshold selection as a sequential decision making problem and uses Deep Q-Network based reinforcement learning. Experimental results show that this adaptive approach outperforms the current static solutions by reducing the fraud losses as well as improving the operational efficiency of the alert system.