About this project
Trade Matcher reconciles trades between two mock trading systems (System A and System B) — detecting matches, price/quantity discrepancies, and unmatched trades — a common and tedious problem in financial back-office operations. Beyond simple matching, it uses an LLM to investigate why a mismatch exists and propose corrections for anything it can't confidently explain, while keeping a human explicitly in the loop for every final decision. load_trades — loads mock trades from System A, System B, or both match_trades — pairs trades by symbol, flags anything outside price/quantity tolerance, or missing entirely from one side investigate_break — an LLM call reasons about why the mismatch might exist (FX timing, settlement windows, or genuinely unexplained) resolve_or_escalate — explained breaks are auto-resolved; unexplained breaks are escalated for human review propose_correction — for escalated breaks only, a second LLM call conservatively proposes a specific fix (which field, which system, what value) with reasoning — but explicitly never applies it run_reconciliation — orchestrates the full pipeline end-to-end in a single tool call and renders results in a live dashboard widget
BFSI & FinTech track
Build AI solutions for banking, payments, insurance, fraud detection, lending, and financial inclusion.