Model Retraining: Predicting the Likelihood of Financial Inclusion in Kiva’s Peer-to-Peer Lending to Promote Social Impact
The purpose of this study is to show how machine learning can be leveraged as a tool to govern social impact and drive fair and equitable investments. Many organizations today are establishing financial inclusion goals to promote social impact and have been increasing their investments in this space...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-07-01
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| Series: | Algorithms |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1999-4893/16/8/363 |
| _version_ | 1797585836117590016 |
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| author | Tasha Austin Bharat S. Rawal |
| author_facet | Tasha Austin Bharat S. Rawal |
| author_sort | Tasha Austin |
| collection | DOAJ |
| description | The purpose of this study is to show how machine learning can be leveraged as a tool to govern social impact and drive fair and equitable investments. Many organizations today are establishing financial inclusion goals to promote social impact and have been increasing their investments in this space. Financial inclusion is the opportunity for individuals and businesses to have access to affordable financial products including loans, credit, and insurance that they may otherwise not have access to with traditional financial institutions. Peer-to-peer (P2P) lending serves as a platform that can support and foster financial inclusion and influence social impact and is becoming more popular today as a resource to underserved communities. Loans issued through P2P lending can fund projects and initiatives focused on climate change, workforce diversity, women’s rights, equity, labor practices, natural resource management, accounting standards, carbon emissions, and several other areas. With this in mind, AI can be a powerful governance tool to help manage risks and promote opportunities for an organization’s financial inclusion goals. In this paper, we explore how AI, specifically machine learning, can help manage the P2P platform Kiva’s investment risks and deliver impact, emphasizing the importance of prediction model retraining to account for regulatory and other changes across the P2P landscape to drive better decision-making. As part of this research, we also explore how changes in important model variables affect aggregate model predictions. |
| first_indexed | 2024-03-11T00:11:43Z |
| format | Article |
| id | doaj.art-a1e4210746cf49e497f2618bf2b34d55 |
| institution | Directory Open Access Journal |
| issn | 1999-4893 |
| language | English |
| last_indexed | 2024-03-11T00:11:43Z |
| publishDate | 2023-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Algorithms |
| spelling | doaj.art-a1e4210746cf49e497f2618bf2b34d552023-11-18T23:56:21ZengMDPI AGAlgorithms1999-48932023-07-0116836310.3390/a16080363Model Retraining: Predicting the Likelihood of Financial Inclusion in Kiva’s Peer-to-Peer Lending to Promote Social ImpactTasha Austin0Bharat S. Rawal1Computer Science Department, Capitol Technology University, Laurel, MD 20708, USAComputer Science Department, Grambling State University, Grambling, LA 71245, USAThe purpose of this study is to show how machine learning can be leveraged as a tool to govern social impact and drive fair and equitable investments. Many organizations today are establishing financial inclusion goals to promote social impact and have been increasing their investments in this space. Financial inclusion is the opportunity for individuals and businesses to have access to affordable financial products including loans, credit, and insurance that they may otherwise not have access to with traditional financial institutions. Peer-to-peer (P2P) lending serves as a platform that can support and foster financial inclusion and influence social impact and is becoming more popular today as a resource to underserved communities. Loans issued through P2P lending can fund projects and initiatives focused on climate change, workforce diversity, women’s rights, equity, labor practices, natural resource management, accounting standards, carbon emissions, and several other areas. With this in mind, AI can be a powerful governance tool to help manage risks and promote opportunities for an organization’s financial inclusion goals. In this paper, we explore how AI, specifically machine learning, can help manage the P2P platform Kiva’s investment risks and deliver impact, emphasizing the importance of prediction model retraining to account for regulatory and other changes across the P2P landscape to drive better decision-making. As part of this research, we also explore how changes in important model variables affect aggregate model predictions.https://www.mdpi.com/1999-4893/16/8/363Kivasocial impactequitablemachine learningfundingAI |
| spellingShingle | Tasha Austin Bharat S. Rawal Model Retraining: Predicting the Likelihood of Financial Inclusion in Kiva’s Peer-to-Peer Lending to Promote Social Impact Algorithms Kiva social impact equitable machine learning funding AI |
| title | Model Retraining: Predicting the Likelihood of Financial Inclusion in Kiva’s Peer-to-Peer Lending to Promote Social Impact |
| title_full | Model Retraining: Predicting the Likelihood of Financial Inclusion in Kiva’s Peer-to-Peer Lending to Promote Social Impact |
| title_fullStr | Model Retraining: Predicting the Likelihood of Financial Inclusion in Kiva’s Peer-to-Peer Lending to Promote Social Impact |
| title_full_unstemmed | Model Retraining: Predicting the Likelihood of Financial Inclusion in Kiva’s Peer-to-Peer Lending to Promote Social Impact |
| title_short | Model Retraining: Predicting the Likelihood of Financial Inclusion in Kiva’s Peer-to-Peer Lending to Promote Social Impact |
| title_sort | model retraining predicting the likelihood of financial inclusion in kiva s peer to peer lending to promote social impact |
| topic | Kiva social impact equitable machine learning funding AI |
| url | https://www.mdpi.com/1999-4893/16/8/363 |
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