Alternative Scoring Factors using Non-Financial Data for Credit Decisions in Agricultural Microfinance
- Resource Type
- Conference
- Authors
- Simumba, Naomi; Okami, Suguru; Kodaka, Akira; Kohtake, Naohiko
- Source
- 2018 IEEE International Systems Engineering Symposium (ISSE) Systems Engineering Symposium (ISSE), 2018 IEEE International. :1-8 Oct, 2018
- Subject
- Aerospace
Computing and Processing
Engineering Profession
General Topics for Engineers
History
Support vector machines
Stakeholders
Biological system modeling
Data analysis
Data models
Kernel
Credit Scores
Stakeholder Requirements
Decision Making
Agriculture
Mobile Applications
- Language
Financial exclusion has a major socio-economic impact on the poor and unbanked. Financially excluded smallholder farmers face challenges accessing credit facilities to fund their farming activities because they lack the financial history data required to create credit scores for credit risk evaluations. Non-financial data sources such as mobile applications have been proposed for the development of credit scoring models. However, context-specific alternative scoring factors which are independent of financial history information, must be developed for credit decision systems that use nonfinancial data. This research proposes an approach to developing alternative scoring factors based on stakeholder's requirements. An example of implementation of the proposed method is given using data collected from farmers in rural Cambodia through surveys and a mobile application. Alternative scoring factors are developed based on stakeholder's requirements and collected data. Multiple logistic regression and support vector machine models are trained and tested on this data to evaluate the selected factors. Models are compared by area under the receiver operating characteristics curve values and accuracy. Additional considerations are made to determine the most suitable model in this context. This stakeholder requirements-based approach can be used to design credit decision systems using nonfinancial data for financially excluded persons and facilitate greater financial inclusion.