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| Hauptverfasser: | , |
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| Format: | Preprint |
| Veröffentlicht: |
2025
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| Schlagworte: | |
| Online-Zugang: | https://arxiv.org/abs/2506.12099 |
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| _version_ | 1866915343080882176 |
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| author | Aslam, Thabassum Aslam, Anees |
| author_facet | Aslam, Thabassum Aslam, Anees |
| contents | SocialCredit+ is AI powered credit scoring system that leverages publicly available social media data to augment traditional credit evaluation. It uses a conversational banking assistant to gather user consent and fetch public profiles. Multimodal feature extractors analyze posts, bios, images, and friend networks to generate a rich behavioral profile. A specialized Sharia-compliance layer flags any non-halal indicators and prohibited financial behavior based on Islamic ethics. The platform employs a retrieval-augmented generation module: an LLM accesses a domain specific knowledge base to generate clear, text-based explanations for each decision. We describe the end-to-end architecture and data flow, the models used, and system infrastructure. Synthetic scenarios illustrate how social signals translate into credit-score factors. This paper emphasizes conceptual novelty, compliance mechanisms, and practical impact, targeting AI researchers, fintech practitioners, ethical banking jurists, and investors. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_12099 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | SocialCredit+ Aslam, Thabassum Aslam, Anees Computers and Society Artificial Intelligence SocialCredit+ is AI powered credit scoring system that leverages publicly available social media data to augment traditional credit evaluation. It uses a conversational banking assistant to gather user consent and fetch public profiles. Multimodal feature extractors analyze posts, bios, images, and friend networks to generate a rich behavioral profile. A specialized Sharia-compliance layer flags any non-halal indicators and prohibited financial behavior based on Islamic ethics. The platform employs a retrieval-augmented generation module: an LLM accesses a domain specific knowledge base to generate clear, text-based explanations for each decision. We describe the end-to-end architecture and data flow, the models used, and system infrastructure. Synthetic scenarios illustrate how social signals translate into credit-score factors. This paper emphasizes conceptual novelty, compliance mechanisms, and practical impact, targeting AI researchers, fintech practitioners, ethical banking jurists, and investors. |
| title | SocialCredit+ |
| topic | Computers and Society Artificial Intelligence |
| url | https://arxiv.org/abs/2506.12099 |