# Optimizing Credit Limit Adjustments Under Adversarial Goals Using Reinforcement Learning

arXiv: https://arxiv.org/abs/2306.15585 · HTML: https://mlsanity.com/paper/2306.15585/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 78 references that are in the map.
- First posted on arXiv 27 Jun 2023.

## Builds on (most cited first, 9 of 9)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [A Unified Approach to Interpreting Model Predictions](https://mlsanity.com/paper/1705.07874/) (2017)
- [OpenAI Gym](https://mlsanity.com/paper/1606.01540/) (2016)
- [Decision Transformer: Reinforcement Learning via Sequence Modeling](https://mlsanity.com/paper/2106.01345/) (2021)
- [AlphaStock: A Buying-Winners-and-Selling-Losers Investment Strategy using Interpretable Deep Reinforcement Attention Networks](https://mlsanity.com/paper/1908.02646/) (2019)
- [The Value of Big Data for Credit Scoring: Enhancing Financial Inclusion using Mobile Phone Data and Social Network Analytics](https://mlsanity.com/paper/2002.09931/) (2020)
- [The value of text for small business default prediction: A deep learning approach](https://mlsanity.com/paper/2003.08964/) (2020)
- [Super-App Behavioral Patterns in Credit Risk Models: Financial, Statistical and Regulatory Implications](https://mlsanity.com/paper/2005.14658/) (2020)
- [MQLV: Optimal Policy of Money Management in Retail Banking with Q-Learning](https://mlsanity.com/paper/1905.12567/) (2019)

## Built on by (most cited first, 6 of 6)

- [Towards Adaptive Categories: Dimensional Governance for Agentic AI](https://mlsanity.com/paper/2505.11579/) (2025)
- [Foundation Models for Credit Risk Prediction: A Game Changer?](https://mlsanity.com/paper/2605.18147/) (2026)
- [Are causal effect estimations enough for optimal recommendations under multitreatment scenarios?](https://mlsanity.com/paper/2410.05177/) (2024)
- [Evaluating utility in synthetic banking microdata applications](https://mlsanity.com/paper/2410.22519/) (2024)
- [Convolutional Attention in Betting Exchange Markets](https://mlsanity.com/paper/2510.16008/) (2025)
- [Reinforcement Learning in Operational Research: A Technical Review and Practical Roadmap](https://mlsanity.com/paper/2609.24750/) (2026)

## Abstract (arXiv)

Reinforcement learning has been explored for many problems, from video games with deterministic environments to portfolio and operations management in which scenarios are stochastic; however, there have been few attempts to test these methods in banking problems. In this study, we sought to find and automatize an optimal credit card limit adjustment policy by employing reinforcement learning techniques. Because of the historical data available, we considered two possible actions per customer, namely increasing or maintaining an individual's current credit limit. To find this policy, we first formulated this decision-making question as an optimization problem in which the expected profit was maximized; therefore, we balanced two adversarial goals: maximizing the portfolio's revenue and minimizing the portfolio's provisions. Second, given the particularities of our problem, we used an offline learning strategy to simulate the impact of the action based on historical data from a super-app in Latin America to train our reinforcement learning agent. Our results, based on the proposed methodology involving synthetic experimentation, show that a Double Q-learning agent with optimized hyperparameters can outperform other strategies and generate a non-trivial optimal policy not only reflecting the complex nature of this decision but offering an incentive to explore reinforcement learning in real-world banking scenarios. Our research establishes a conceptual structure for applying reinforcement learning framework to credit limit adjustment, presenting an objective technique to make these decisions primarily based on data-driven methods rather than relying only on expert-driven systems. We also study the use of alternative data for the problem of balance prediction, as the latter is a requirement of our proposed model. We find the use of such data does not always bring prediction gains.