2021 Fiscal Year Final Research Report
Credit Scoring System to Resolve Financial Exclusion of Small-Scale Farmers in Developing Countries by Mobile and Satellite Data
| Project/Area Number |
19H04100
|
|---|
| Research Category |
Grant-in-Aid for Scientific Research (B)
|
| Allocation Type | Single-year Grants |
|---|
| Section | 一般 |
|---|
| Review Section |
Basic Section 60060:Information network-related
|
|---|
| Research Institution | Keio University |
|---|
Principal Investigator |
Kohtake Naohiko 慶應義塾大学, システムデザイン・マネジメント研究科(日吉), 教授 (20549836)
|
|---|
| Co-Investigator(Kenkyū-buntansha) |
中島 円 慶應義塾大学, システムデザイン・マネジメント研究科(日吉), 特任教授 (60607802)
小高 暁 慶應義塾大学, システムデザイン・マネジメント研究科(日吉), 特任講師 (60750429)
|
|---|
| Project Period (FY) |
2019-04-01 – 2022-03-31
|
| Keywords | 信用評価 / 衛星データ / モバイルデータ / 機械学習 / 途上国農家 |
|---|
| Outline of Final Research Achievements |
This research targets establishing an alternative credit scoring which can be used to support lending decisions to extend credit services to small-scale farmers in a developing country. Suitable data, credit score analysis models, and scoring results were evaluated. Appropriate credit scoring factors were designed, factoring in the socio-economic context of the farmers concerned which were established through field work observation and interviews. Data relating to farm activities and farmer characteristics, collected by a mobile application, was applied to the development of algorithms to predict the designed credit scoring factors. Additional sources of data such as open geospatial data sources were also be considered to improve model performance as needed. Geospatial analysis and machine learning were the main analysis methods used. The former was used to analyze location data. Meanwhile, various machine learning algorithms were applied and compared to identify the most fitting.
|
| Free Research Field |
システムズエンジニアリング
|
| Academic Significance and Societal Importance of the Research Achievements |
本研究の成果は、発展途上国の小規模農家のモバイルデータと衛星データ、またそれらの組み合わせによる新たな非金融データの指標を開発するとともに、機械学習によるデータ駆動型の小規模農家の信用評価の手法構築を通して学術的に寄与するものである。借用履歴に代わる信用評価の仕組みによって、機関側は想定される程度よりも低いリスクで小規模農家に対しての資金供給を行うことができ、小規模農家は金融へのアクセスが改善される。その結果、農村地域でも資金循環が改善し、投入資源の増加、ひいては農業の収量の増加を通じて貧困から離脱する農家が増えることで地域の発展に繋がる。本研究は、こうした未来の実現にも寄与するものである。
|
