Banking Risk Analysis in Tunisia: A Case Study of BTE Bank

Hanen Khanchel
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Abstract

The banking activity represents a strategic sector of sustainable economic development in Tunisia. Hence, Tunisian banks have the status of financial institutions that earn profits by providing financial services to customers by dealing with risks. Therefore, lending decisions for these establishments are strategic as they can avoid the risk of loan recourse. However, the assessment of borrowing sanctions in Tunisian banks is based on credit rating models. Consequently, it is important to assess the riskiness of the banking sector in Tunisia. Indeed, Tunisian banks have kept voluminous data concerning their clienteles which can be considered as critical knowledge assets which can be processed via underwritten credit management tools. This tools denote a recent development of statistical techniques and promising tools of data mining and data processing. The current study attempts to develop the rating model as a decision support system to credit approval evaluation at Tunisian banks based on applicant’s characteristics; the proposed model is mainly based on quantitative and qualitative criteria can be used to help credit officers make better decisions when evaluating future loan applications. A real-world credit application of cases of both granted and rejected applications from BTE bank was employed to develop the rating model. The experimental outcomes showed that this approach area promising addition to the existing classification methods. It therefore requires a high responsibility and commitment of managers in the process of evaluation and decision-making to reduce both the risk of default and the risk of debt distress.
突尼斯银行业风险分析:以BTE银行为例
银行业务是突尼斯可持续经济发展的战略部门。因此,突尼斯银行具有金融机构的地位,通过处理风险向客户提供金融服务来赚取利润。因此,这些机构的贷款决策是战略性的,因为它们可以避免贷款追索的风险。然而,对突尼斯银行借贷制裁的评估是基于信用评级模型的。因此,评估突尼斯银行业的风险非常重要。事实上,突尼斯的银行保存了大量关于其客户的数据,这些数据可以被视为关键的知识资产,可以通过担保信贷管理工具进行处理。这些工具表示统计技术的最新发展,以及数据挖掘和数据处理的有前途的工具。本研究试图根据申请人的特点,开发评级模型作为突尼斯银行信贷审批评估的决策支持系统;建议的模型主要基于定量和定性标准,可用于帮助信贷人员在评估未来的贷款申请时做出更好的决策。采用BTE银行批准和拒绝申请案例的真实信用应用程序来开发评级模型。实验结果表明,该方法是现有分类方法的有效补充。因此,它要求管理人员在评价和决策过程中具有高度的责任感和承诺,以减少违约风险和债务困境风险。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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