Electronic Resource
MQLV: Optimal Policy of Money Management in Retail Banking with Q-Learning
| العنوان: | MQLV: Optimal Policy of Money Management in Retail Banking with Q-Learning |
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| المؤلفون: | Interdisciplinary Centre for Security, Reliability and Trust (SnT) > Services and Data management research group (SEDAN) [research center], Charlier, Jérémy Henri J., Ormazabal, Gaston, State, Radu, Hilger, Jean |
| بيانات النشر: | 2019-09 |
| نوع الوثيقة: | Electronic Resource |
| مستخلص: | Reinforcement learning has become one of the best approach to train a computer game emulator capable of human level performance. In a reinforcement learning approach, an optimal value function is learned across a set of actions, or decisions, that leads to a set of states giving different rewards, with the objective to maximize the overall reward. A policy assigns to each state-action pairs an expected return. We call an optimal policy a policy for which the value function is optimal. QLBS, Q-Learner in the Black-Scholes(-Merton) Worlds, applies the reinforcement learning concepts, and noticeably, the popular Q-learning algorithm, to the financial stochastic model of Black, Scholes and Merton. It is, however, specifically optimized for the geometric Brownian motion and the vanilla options. Its range of application is, therefore, limited to vanilla option pricing within the financial markets. We propose MQLV, Modified Q-Learner for the Vasicek model, a new reinforcement learning approach that determines the optimal policy of money management based on the aggregated financial transactions of the clients. It unlocks new frontiers to establish personalized credit card limits or bank loan applications, targeting the retail banking industry. MQLV extends the simulation to mean reverting stochastic diffusion processes and it uses a digital function, a Heaviside step function expressed in its discrete form, to estimate the probability of a future event such as a payment default. In our experiments, we first show the similarities between a set of historical financial transactions and Vasicek generated transactions and, then, we underline the potential of MQLV on generated Monte Carlo simulations. Finally, MQLV is the first Q-learning Vasicek-based methodology addressing transparent decision making processes in retail banking. |
| مصطلحات الفهرس: | Q-learning, Monte-Carlo, Payment Transactions, info:eu-repo/semantics/conferenceObject |
| DOI: | 10.1007.978-3-030-37720-5_1 |
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| الاتاحة: | Open access content. Open access content info:eu-repo/semantics/openAccess |
| ملاحظة: | English |
| Other Numbers: | LULUX oai:orbilu.uni.lu:10993/40331 WOS:000654273800001 DOI:10.1007/978-3-030-37720-5_1 SCOPUS_ID:85078497904 1147217553 |
| المصدر المساهم: | UNIV OF LUXEMBOURG From OAIster®, provided by the OCLC Cooperative. |
| رقم الانضمام: | edsoai.on1147217553 |
| قاعدة البيانات: | OAIster |
| DOI: | 10.1007.978-3-030-37720-5_1 |
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