Genetic algorithm based model for optimizing bank lending decisions

Highlights

• The bank lending decisions in credit crunch environments are big challenge.

• This NP-hard optimization problem is solved using a proposed GA based model.

• The proposed model is tested using two scenarios with simulated and real data.

• The real data is collected from Southern Louisiana Credit Union.

• The proposed model increased the bank profit and improved the system performance.

Abstract

To avoid the complexity and time consumption of traditional statistical and mathematical programming, intelligent techniques have gained great attention in different financial research areas, especially in banking decisions’ optimization. However, choosing optimum bank lending decisions that maximize the bank profit in a credit crunch environment is still a big challenge. For that, this paper proposes an intelligent model based on the Genetic Algorithm (GA) to organize bank lending decisions in a highly competitive environment with a credit crunch constraint (GAMCC). GAMCC provides a framework to optimize bank objectives when constructing the loan portfolio, by maximizing the bank profit and minimizing the probability of bank default in a search for a dynamic lending decision. Compared to the state-of-the art methods, GAMCC is considered a better intelligent tool that enables banks to reduce the loan screening time by a range of 12%–50%. Moreover, it greatly increases the bank profit by a range of 3.9%–8.1%.

Introduction

It is clear that the financial crisis was accompanied by a reduction in the credit supply available to all customers (Aguilar, Valenzuela, & Ortiz, 2015). According to recent studies (Judit, Wang, 2012, Michael, Rohwedder, 2010), the nature of the latest financial crisis in US has brought to the fore concerns regarding bank’s ability to continue with its traditional bank lending strategies. The main channel through which a banking crisis affect the real economy relates to its ability to provide the credit needed given credit constraints imposed on them. Hence, a key question at the heart of the any financial crisis is whether and how did the banking sector managed to distribute the limited credit available in a way that maximizes their profits in the time of crisis (Manju, Rocholl, & Steffen, 2011). Therefore, there is a need to set an optimal mechanism of bank lending decisions that will maximize the bank profit in a timely manner. The inability of banks to manage loan portfolio efficiently may result in a credit crunch. A credit crunch is often caused by a sustained period of careless and inappropriate lending, resulting in losses for lending institutions and investors in debt when the loans turn sour and the full extent of bad debts becomes known. These challenges have led to a rise in more formal and accurate methods to optimize the lending decision and minimize loan risks. In conjunction, bank lending decision has become a primary tool for financial institutions to increase profit, reduce possible risks, and make managerial decisions.

Unlike the traditional statistical and mathematical programming techniques such as discriminant analysis, linear and logistic regression, linear and quadratic programming, or decision trees; intelligent systems have proven their ability to overcome different challenges in financial research areas, specially in banking loan portfolio optimization (Eletter, Yaseen, Elrefae, 2010, Ghodselahi, Amirmadhi, 2011, Marque, Garci, Nchez, 2013, Nazari, Alidadi, 2013). However, most of them are focused on either credit scoring - to determine whether the applied customer is eligible to get the required loan - (Ghodselahi, Amirmadhi, 2011, Marque, Garci, Nchez, 2013), or portfolio selection (Berutich, Francisco, Luna, Quintana, 2016, Lwin, Qu, MacCarthy, 2017, Saborido, Ruiz, Bermúdez, Vercher, Luque, 2016) aiming to choose the optimum stocks that maximize the customer profit.

Thus, the problem of bank lending decision in a credit crunch environment- where all applicable customers are eligible to get the desired loan - is an NP-hard optimization problem that can be solved using meta-heuristic algorithms such as evolutionary algorithms (Bhargava, 2013, Ghosh, Tsutsui, 2003, Metawa, Elhoseny, Kabir Hassan, Hassanien, 2016). The evolutionary computing methods are highly capable of extracting meaning from imprecise data and detecting trends that are too complex to be discovered by either humans or other conventional techniques. For that purpose, this paper proposes an intelligent model based on Genetic Algorithm (GA) to organize bank lending decision in a highly competing environment with credit crunch constraint.

The main contribution of this paper is the creation of a GA model that facilitates how banks would make an efficient decision in case of a cut back on lending supply when faced with a liquidity shock, while staying focus on the main objective of bank profit maximization. The main focus of the GA model is two-fold: to stabilize systemically banks while achieving maximum profit, and to establish the capital base so that banks would increase lending efficiently. The GA model takes into account the margins along which banks adjust their loan portfolios in response to the crisis, either through imposing credit supply contractions across all types of borrowers, or disproportionally cut back credit for some specific types of loans. Multiple factors including loan characteristics, creditor ratings and expected loan loss are integrated to GA chromosomes and validation is performed to ensure the optimal decision.

The remaining of this paper is organized as the following: Section 2 discusses the literature review of the bank lending decision’s problem and the recent work to solve this problem using GA. Section 3 describes GAMCC and the GA data representation. Then, Section 4 discuss the experimental results. Finally, Section 5 concludes the paper.