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首页> 外文会议>2018 17th International Symposium INFOTEH-JAHORINA >Using data mining approaches to build credit scoring model: Case study — Implementation of credit scoring model in microfinance institution
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Using data mining approaches to build credit scoring model: Case study — Implementation of credit scoring model in microfinance institution

机译：使用数据挖掘方法建立信用评分模型：案例研究—小额信贷机构中信用评分模型的实施

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摘要

The aim of this paper is to present a Credit Scoring Model applied by one Microfinance Institution in Bosnia and Herzegovina as well as to show how the most relevant attributes for its implementation were identified. The purpose of created Credit Scoring Model was to predict default clients and reduce credit risk of Microfinance Institution by applying data mining algorithm in order to find patterns for recognition of default clients and, thus, support decision making process of credit approval. Credit Scoring Model was build using Oracle Data Miner software package that uses Generalized Linear Model for classification. Created model showed great predictive confidence and accuracy, but also gave trustworthy results regarding feature selection, so the Microfinance institution decided to adopt this model as help in decision making process.
机译：本文的目的是提出一个由波斯尼亚和黑塞哥维那的小额信贷机构应用的信用评分模型，并展示如何确定与其实施最相关的属性。创建信用评分模型的目的是通过应用数据挖掘算法预测违约客户并降低小额信贷机构的信用风险，从而找到识别违约客户的模式，从而支持信用审批的决策过程。信用评分模型是使用Oracle Data Miner软件包构建的，该软件包使用Generalized Linear Model进行分类。创建的模型显示出很高的预测信心和准确性，但在特征选择方面也给出了可信赖的结果，因此小额信贷机构决定采用此模型作为决策过程的帮助。

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