Contact person: Business Client

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Location: Mumbai, India

Budget: Recommended by industry experts

Time to start: As soon as possible

Project description:
"I need an AI-driven tool that can turn nothing more than a list of CSV column headers from banking-related financial data into a detailed risk assessment in under eight seconds. Paste the headers, hit “Run”, and the model should return an executive-ready report that flags key financial risks, quantifies their significance, explains the rationale, and suggests follow-up audit procedures.

Scope
• Input: CSV headers from banking datasets (balance sheet, P&L, GL extracts, etc.).
• Processing: Large Language Model (GPT-4, Claude, or comparable open-source alternative) chained with a rules/ontology layer specific to banking audit standards (Basel, COSO, PCAOB).
• Output: A comprehensive, well-structured risk analysis—no high-level summaries; I want full detail that an audit partner could sign off on.

Key requirements
• Sub-8-second end-to-end response time for typical files (≈50–250 columns).
• Clear segregation of inherent, control, and detection risk, with scoring logic exposed in the code.
• Web interface or lightweight desktop app where I can paste headers or upload the CSV.
• Written in Python; using LangChain, FastAPI, Streamlit, or similar is fine as long as dependencies stay mainstream.
• All prompt engineering, taxonomy libraries, and custom code handed over with brief but precise documentation and a README that lets me redeploy on my own GPU/CPU box.

Deliverables
1. Running prototype hosted on your sandbox (URL or demo video).
2. Source code and environment files.
3. Risk taxonomy/ontology file tailored to banking financial data.
4. Deployment guide and short user manual.

Acceptance criteria
• I paste a header set from a real bank GL extract and receive a multi-section report (risks, rationale, audit steps) in ≤8 s.
• At least 90 % of identified risks align with those in my benchmark answer sheet.
• No personally identifiable data leaves the tool during processing.

If you have shipped anything similar—AI knowledge engines, audit analytics, or high-speed LLM apps—let’s talk." (client-provided description)