Original Summary

I built FinSentry AI as an independent side project exploring a specific question: how can AI help investigate a payment without becoming the decision-maker? It brings case evidence, policy context, advisory AI findings and human approvals into one workspace. The AI recommends; people remain responsible for decisions. The 4:20 walkthrough follows a synthetic payment investigation: Missing screening input leads the advisory path to abstain rather than treat incomplete evidence as clearance. Separate maker/checker roles include a restriction on approving your own request. The illustrated approval maintains an existing hold. It does not release funds. An audit trail shows the steps, with the case still under investigation. Narrated demo (4:20, MP4 download) Project brief, architecture and demo notes This is a recorded, guided demo using synthetic sandbox data, not a live banking deployment or a paid client case study. It does not establish regulatory certification or production readiness. The public repository contains documentation and finished media, not the application source code. Feedback I'd value: is it clear where the AI recommendation ends and the human approval begins? What would make the evidence-to-approval flow easier to follow? Disclosure: AI assistance was used to draft this post.   submitted by   /u/Proper-Spread-35 [link]   [comments]

  • 情报分类：技术学习与提效
  • 分类依据：内容涉及技术、AI、软件工具或工程实践
  • 信息来源：Reddit · SideProject
  • 发布时间：2026/10/12 07:45:21