[jira] [Updated] (FINCN-252) Machine Learning Scorecard for Credit Risk Assessment Phase 5

["Yemdjih Kaze Nasser (Jira)" <ji...@apache.org>]

     [ https://issues.apache.org/jira/browse/FINCN-252?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]

Yemdjih Kaze Nasser updated FINCN-252:
--------------------------------------
    Summary: Machine Learning Scorecard for Credit Risk Assessment Phase 5  (was: Machine Learning Scorecard for Credit Risk Assessment Phase 4)

> Machine Learning Scorecard for Credit Risk Assessment Phase 5
> -------------------------------------------------------------
>
>                 Key: FINCN-252
>                 URL: https://issues.apache.org/jira/browse/FINCN-252
>             Project: Fineract Cloud Native
>          Issue Type: Improvement
>          Components: fineract-cn-ml
>            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
(v8.20.7#820007)