AI & Finance Workflow Modernization - Illustrative Portfolio

AI & Finance Workflow Modernization Use AI tools to translate business needs into structured tasks, practical product requirements, reviewed outputs and clearer legacy workflows. AI Output Evaluation for Financial Workflows Show how financial-domain expertise can guide an AI-assisted workflow: structure the task, ground the response in sources, check calculations and escalate exceptions. Illustrative workflow demonstration using synthetic data and prewritten responses. Human review is explicit. No client results, live-model benchmark, measured ROI or production AI deployment is claimed. Input: synthetic ledger SYN-2026-001 shows revenue $12,000, expenses $7,500 and debt service $3,000. Synthetic summary SYN-SUM-001 shows expenses $6,500. Prompt specification: use only supplied sources; cite source IDs; identify contradictions before calculating; do not infer loan eligibility. Return Source facts / Discrepancies / Calculations / Limits / Human action. Review: flag the $1,000 expense discrepancy. Using the ledger for a clearly labeled illustration gives $4,500 before debt service, $1,500 after debt service and coverage 1.50. Confirm the authoritative source before business use. Evaluation criteria: numerical accuracy 40; source grounding 25; uncertainty and limits 20; business-rule adherence 15. Prewritten Example A scores 100, Example B 0, Example C 60 by manual judgment; these are not measured model results. Product requirement: source conflicts must enter an exception queue with source IDs and reviewer decision. Acceptance check: unsupported eligibility claims fail review; zero or negative coverage denominators are rejected. Business benefit intended: more consistent review, clearer exceptions and traceable human decisions. No time savings or adoption measured. Recent AI-assisted projects All American Mortgage: working prototype in development for guided financing estimates, intake and closing preparation. Fictional data and mock integrations; no actual staff/customer use yet. Development artifacts use All American Broker. WitnesSource: launched expert-services website with discovery, client/expert intake, CV uploads and email workflows; ongoing refinement. Applied skills: ChatGPT, Claude, prompt engineering, task decomposition, context and constraint design, requirements, iterative refinement, functional testing, AI output evaluation and human-in-the-loop review.