[
https://issues.apache.org/jira/browse/FINCN-252?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Yemdjih Kaze Nasser updated FINCN-252:
--------------------------------------
Description:
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]
was:
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
> 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
> 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)
