Credit Scoring for M-Shwari using Hidden Markov Model
Credit Scoring for M-Shwari using Hidden Markov Model
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使用隐马尔可夫模型的 M-Shwari 信用评分
DOI：
10.19044/esj.2016.v12n15p176
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发表时间：
2016
期刊：
影响因子：
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通讯作者：
Weke Patrick
中科院分区：
文献类型：
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作者：
D. B. Ntwiga;Weke Patrick
基于移动的小额信贷工具M-Shwari的引入，提高了开发适当的决策支持系统以根据客户的信用评分对客户进行分类的必要性。这是由于缺乏关于穷人和无银行账户的人的适当信息，因为他们被正规银行部门拒之门外。使用了一种分类技术，即隐马尔可夫模型。M-Shwari账户中的不良客户稀少的存款和取款动态估计了用于训练和学习隐马尔可夫模型的信用风险因素。数据是通过模拟生成的，并根据客户的信用评分和信用质量水平对客户进行分类。该模型将80%以上的客户归类为信用质量中等和良好的客户。这一办法提供了一种简单和新手的方法，以迎合无银行账户的穷人和财务历史很少或没有财务历史的穷人，从而增加了肯尼亚的金融包容性。
The introduction of mobile based Micro-credit facility, M-Shwari, has heightened the need to develop a proper decision support system to classify the customers based on their credit scores. This arises due to lack of proper information on the poor and unbanked as they are locked out of the formal banking sector. A classification technique, the hidden Markov model, is used. The poor customers’ scanty deposits and withdrawal dynamics in the M-Shwari account estimate the credit risk factors that are used in training and learning the hidden Markov model. The data is generated through simulation and customers categorized in terms of their credit scores and credit quality levels. The model classifies over 80 percent of the customers as having average and good credit quality level. This approach offers a simple and novice method to cater for the unbanked and poor with minimal or no financial history thus increasing financial inclusion in Kenya.