  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.
