[jira] [Commented] (FINCN-252) Machine Learning Scorecard for Credit Risk Assessment Phase 4
[ https://issues.apache.org/jira/browse/FINCN-252?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel=17394554#comment-17394554 ] Yemdjih Kaze Nasser commented on FINCN-252: --- Thanks for creating the repository, will push the changes there ASAP. > Machine Learning Scorecard for Credit Risk Assessment Phase 4 > - > > Key: FINCN-252 > URL: https://issues.apache.org/jira/browse/FINCN-252 > Project: Fineract Cloud Native > Issue Type: Improvement >Reporter: Ed Cable >Assignee: Yemdjih Kaze Nasser >Priority: Major > Labels: gsoc2021, mentor > > h2. Mentors > * Lalit Mohan S > * [~Fintecheando] > h2. Overview & Objectives > Financial Organizations using Mifos/Fineract are depending on external > agencies or their past experiences for evaluating credit scoring and > identification of potential NPAs. Though information from external agencies > is required, financial organizations can have an internal scorecard for > evaluating loans so that preventive/proactive actions can be done along with > external agencies reports. In industry, organizations are using rule based, > Statistical and Machine learning methods for credit scoring, predicting > potential NPAs, fraud detection and other activities. This project aims to > implement a scorecard based on statistical and ML methods for credit scoring > and identification of potential NPAs. > h2. Description > The approach should factor and improve last year's GSOC work > ([https://gist.github.com/SupreethSudhakaranMenon/a20251271adb341f949dbfeb035191f7]) > on Features/Characteristics, Criteria and evaluation (link). The design and > implementation of the screens should follow Mifos Application standards. > Should implement statistical and ML methods with explainability on decision > making. Should also be extensible for adding other functionalities such as > fraud detection, cross-sell and up-sell, etc. > h2. Helpful Skills > JAVA, Integrating Backend Service, MIFOS X, Apache Fineract, AngularJS, ORM, > ML, Statistical Methods, Django > h2. Impact > Streamlined Operations, Better RISK Management, Automated Response Mechanism > h2. Other Resources > 2019 Progress: > [https://gist.github.com/SupreethSudhakaranMenon/a20251271adb341f949dbfeb035191f7] > [https://gist.github.com/lalitsanagavarapu] > -- This message was sent by Atlassian Jira (v8.3.4#803005)
[jira] [Commented] (FINCN-252) Machine Learning Scorecard for Credit Risk Assessment Phase 4
[ https://issues.apache.org/jira/browse/FINCN-252?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel=17394356#comment-17394356 ] Ed Cable commented on FINCN-252: Repo is now available at https://github.com/apache/fineract-credit-scorecard/ > Machine Learning Scorecard for Credit Risk Assessment Phase 4 > - > > Key: FINCN-252 > URL: https://issues.apache.org/jira/browse/FINCN-252 > Project: Fineract Cloud Native > Issue Type: Improvement >Reporter: Ed Cable >Assignee: Yemdjih Kaze Nasser >Priority: Major > Labels: gsoc2021, mentor > > h2. Mentors > * Lalit Mohan S > * [~Fintecheando] > h2. Overview & Objectives > Financial Organizations using Mifos/Fineract are depending on external > agencies or their past experiences for evaluating credit scoring and > identification of potential NPAs. Though information from external agencies > is required, financial organizations can have an internal scorecard for > evaluating loans so that preventive/proactive actions can be done along with > external agencies reports. In industry, organizations are using rule based, > Statistical and Machine learning methods for credit scoring, predicting > potential NPAs, fraud detection and other activities. This project aims to > implement a scorecard based on statistical and ML methods for credit scoring > and identification of potential NPAs. > h2. Description > The approach should factor and improve last year's GSOC work > ([https://gist.github.com/SupreethSudhakaranMenon/a20251271adb341f949dbfeb035191f7]) > on Features/Characteristics, Criteria and evaluation (link). The design and > implementation of the screens should follow Mifos Application standards. > Should implement statistical and ML methods with explainability on decision > making. Should also be extensible for adding other functionalities such as > fraud detection, cross-sell and up-sell, etc. > h2. Helpful Skills > JAVA, Integrating Backend Service, MIFOS X, Apache Fineract, AngularJS, ORM, > ML, Statistical Methods, Django > h2. Impact > Streamlined Operations, Better RISK Management, Automated Response Mechanism > h2. Other Resources > 2019 Progress: > [https://gist.github.com/SupreethSudhakaranMenon/a20251271adb341f949dbfeb035191f7] > [https://gist.github.com/lalitsanagavarapu] > -- This message was sent by Atlassian Jira (v8.3.4#803005)
