用社交媒体数据+伊斯兰伦理审核，打造可解释的智能信贷评分系统
SocialCredit+
- 通过对话助手获取授权，分析用户社交内容与关系网络生成行为画像
- 引入伊斯兰合规层识别非清真指标，确保金融行为符合宗教伦理
- 结合检索增强生成技术，提供清晰可读的决策解释，适合金融科技与伦理研究者
SocialCredit+ 是一种基于人工智能的信用评分系统，利用公开的社交媒体数据补充传统信用评估。系统通过对话式银行助理获取用户授权并获取其公开资料，采用多模态特征提取器分析帖子、个人简介、图片及好友网络，构建全面的行为画像。专门设计的伊斯兰合规模块根据伊斯兰伦理标准，识别非清真信号和被禁止的金融行为。平台采用检索增强生成模块：大语言模型访问领域知识库，生成清晰的文本化决策解释。本文描述了端到端架构与数据流程、所用模型及系统基础设施，并通过合成场景展示社会信号如何转化为信用评分因子。论文强调概念创新性、合规机制与实际应用价值，面向人工智能研究人员、金融科技从业者、伦理银行法学家及投资者。
原文摘要 · Abstract (English)
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.
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