About
I build the risk and analytics function, not just the reports. AtFairSquare Holdings (National Funding) I'm the sole risk & BI owner across five lending brands — National Funding, QuickBridge, SmallBusinessLoans, NFXprs, and Finova Capital — carrying a $550M+ annualized originations book and the surveillance, forecasting, and executive-reporting stack behind it, built from zero.
The thread through everything I do: finding the number everyone else missed. A $1M-per-month write-off reconciliation gap — now fixed company-wide. A syndication expansion led under incomplete data with 3-scenario stress analysis, finishing 59.7% above target. In 2025, net charge-offs came in $15.1M below forecast (−29.6%) on the book I instrument.
My toolkit is the full credit-risk stack — PD/LGD/EAD modeling, vintage and roll-rate analysis, risk-based pricing, line management, and fair-lending compliance (ECOA/FCRA/TILA) — built on Python, SQL, Snowflake, dbt, and Tableau, and increasingly automated by an AI layer I engineered: custom Claude skills, MCP integrations across Jira, dbt, Domo, and Microsoft 365, LLM-generated executive commentary, and a governed semantic layer designed for AI consumption.
Outside the day job I research how AI is rewiring financial risk — published in IEEE, ACM, and Springer with 40+ citations, featured in the Free Press Journal, quoted in Republic World and NDTV, and currently writing Risk at Machine Speed, a book on automated decision systems in credit-risk operations.
Skills
AI & LLM Engineering
- Claude (custom skills, subagents, agentic workflows)
- Custom MCP server development
- Model Context Protocol (MCP) integrations
- Prompt & context engineering
- Retrieval-augmented generation (RAG)
- Structured outputs & tool use
- LLM evaluation & regression testing
- AI agent orchestration
- Semantic layers for AI consumption
- AI-assisted development (Claude Code)
AI Governance & Model Risk
- AI governance frameworks
- LLM output validation & hallucination testing
- Model-risk management (SR 11-7 style controls)
- Algorithmic bias & fairness testing
- AI explainability
- Human-in-the-loop review design
- AI use-policy & audit documentation
Risk Methods
- PD / LGD / EAD modeling
- Loss forecasting
- Vintage & roll-rate analysis
- Securitization reporting
- Risk-based pricing
- Scenario & stress analysis
- A/B testing
- Fair-lending compliance (ECOA/FCRA/TILA)
Data Engineering & BI
- SQL (Snowflake)
- Python
- R
- dbt
- ETL / ELT
- Data modeling
- Data quality testing
- Git
- Tableau
- Domo
- Power BI
- Streamlit
Certifications: CSM®, Six Sigma Green Belt, Tableau Desktop Specialist (TDS-C01), IBM Data Science.
Experience
Senior — Business Intelligence & Risk Analyst · FairSquare Holdings (National Funding)
Jun 2024 — Present- Sole risk & BI owner across 5 lending brands. Own the weekly executive report read by 85+ leaders including the C-suite on $550M+ in annualized originations — rebuilt from scratch, cleared through one SVP and three VPs weekly, with LLM-generated macro commentary for non-analyst readers.
- Built the AI leverage layer: custom MCP servers wiring Claude into Snowflake, dbt, Jira, Domo, and Microsoft 365; a reusable Claude skills library; and agentic workflows that turn raw stakeholder asks into scoped, executed work — plus the governed AI-ready semantic layer with an LLM evaluation harness.
- Called the risk right under ambiguity: 2025 net charge-offs came in $15.1M favorable (−29.6%), gross $10.8M favorable, recoveries +30.3% over plan; pushed syndication +59.7% past target on a 3-scenario stress model built with incomplete data.
- Stopped bad numbers before the board saw them — a 2× funded-dollars overstatement, a $0.2M growth misstatement, and a cross-join defect inflating a ~$700M metric by five orders of magnitude; turned a $1M+/month reporting gap into standing company protocol.
- Re-platformed the money-critical reporting: moved month-end accounting and securitization (facility tapes, SPV excess-spread) off legacy SQL Server onto Snowflake + dbt — June close reconciled loan-for-loan, and 100% of external-auditor discrepancies were root-caused and closed across two audit cycles.
- Led the BI re-platform: own Risk & Portfolio workstreams of a company-wide Domo→Tableau migration (400+ assets); standardized KPIs across all five brands, cutting manual reconciliation ~30%; coached 16 analysts and 3 interns in the domain.
- Shipped fast and self-directed: cut the weekly performance review from 2 days to same-day and closed 68+ tickets across six departments in H1 2026, most self-scoped straight from stakeholder conversations.
- Custom MCP servers
- Credit policy
- Securitization reporting
- Snowflake
- dbt
- Tableau
- Python
Student Analyst, Economic Research & Forecasting · Pacific Life
Jan 2024 — Jun 2024- Built 4-quarter macro forecasting models (ARIMA, LSTM, Random Forest, Prophet) on Bloomberg Terminal data; ARIMA won on out-of-sample accuracy. Delivered an interactive Streamlit forecasting dashboard and presented to senior research leadership.
- ARIMA
- LSTM
- Streamlit
- Bloomberg Terminal
Teaching Assistant, MGMT-90 · UC Irvine — Merage School of Business
Sep 2023 — Jan 2024- Supported instruction and grading for a foundational business course while completing the MS in Business Analytics.
Digital & IT Intern (Wellness Forever) · Technology Consultant (D-Sys Data Solutions) · Earlier
2021 — 2022- Analyzed 90,000+ SKUs to build and lead the impulse product category (+25% sales); cut partner-onboarding turnaround 50% through workflow digitization; built data-hygiene pipelines that reduced data errors 25%.
Risk Lab
Credit-Risk Toolkit
Three models from my day job, live in your browser. Drag the assumptions and watch the portfolio respond — the same math behind loss forecasting, delinquency surveillance, and pricing on a $277.5M book.
Vasicek single-factor model — the Basel II framework behind bank capital rules. Expected loss, 99.9% VaR, and the capital buffer between them.
Delinquency bucket flows — how monthly roll rates compound into 12-month charge-offs. The mechanics behind vintage & roll-rate surveillance.
What should this loan cost? Stack funding, operations, expected loss, and target margin into a break-even and target APR.
Built from scratch with no charting library — Vasicek/Basel II capital math, delinquency roll-rate mechanics, and APR construction, straight from the day job.
Projects
Property Analyzer
A real-estate underwriting engine that runs entirely in the browser: short-term + long-term rental analysis, cost-segregation tax depreciation, 3/5/10-year profitability, a recommended offer price, and a weighted 0–100 rating — from nothing but an address (RentCast autofill, every assumption editable, live recompute). Full analyses encode into shareable URLs. Built with Next.js 15 + React 19 through AI-assisted development, with the finance engine regression-tested against the original Excel model.
Kite Algo Bot — Automated Trading System
GitHub ↗A rule-based algorithmic trading system for Indian equities (Zerodha Kite Connect) with one shared decision pipeline across backtest, paper, and live modes — an order only fires when every gate passes: signal-quality score, risk:reward, position sizing, margin, session timing, and kill switches (the same risk-controls discipline I apply professionally). Below: a real backtest replay from that pipeline.
Backtest Replay — Swing Style, 365 Days, US Large-Caps
Generated by the bot's real decision pipeline on synthetic market data over a config-driven US large-cap universe — the same code that runs paper and live modes. A demo of the system's mechanics (gates, sizing, kill switches), not a performance claim or investment advice.
The Book
Forthcoming · 2026
Risk at Machine Speed
AI-Augmented Credit Risk Analytics for Lending Professionals
"The institutions that answered in hours were not smarter. They were better organised. They had built the plumbing before they needed it — which meant they could ask the question before anyone told them to. This book is a guide to building that plumbing."— from the Prologue: March 10, 2023, 6:47 a.m. The morning SVB died.
Twenty chapters across five parts — from the crises that break portfolios (SVB, tariffs, COVID concentration, the quiet disasters that never make the front page) to what AI tools actually do in a risk war room, where they fail in a regulated environment, and a 90-day build plan for the infrastructure.
- CH 01
The Day SVB Died
What supervision missed, what the data didn't — and the counterparty bank monitor that answers in hours, not days.
- CH 08
The 2008 Ghost
What AI would have found in the mortgage data — re-running the crisis with modern tooling.
- CH 09
Ukraine, Wheat, and the Bakery Chain That Ran Out of Margin
How a war 6,000 miles away compresses the margins of a borrower on Main Street.
- CH 14
The Automation Trap
Knight Capital lost $440M in 45 minutes. What it teaches about putting machines in the decision loop.
- CH 19
The 30/60/90-Day War Room Build
A working plan for standing up AI-augmented risk infrastructure in one quarter.
- CH 20
The Next Crisis Is Already in Your Data
The signals sitting in your portfolio right now — and the plumbing needed to hear them.
Grounded in named, sourced case studies — SVB, Knight Capital, the 2008 mortgage data, Ukraine's wheat shock, crypto winter — with working SQL and Python builds for each monitor.
Research & Media
40+ citations
Peer-reviewed research — IEEE, ACM & Springer
Publications on predictive analytics for financial risk, AI-driven credit decisioning, comparative BI & data-analytics methods, and online transaction risk factors.
Google Scholar profile ↗Industry white paper · in progress
Migrate the Mess, or Mess Up the Migration
What the AI era demands from your semantic layer. AI agents now consume BI metrics built for human eyes — and a migration is the once-a-decade window to fix that. Centerpiece: a lending case study where three versions of “Delinquency Rate %” silently disagreed in executive reporting for over a year, and the five properties (canonical definitions, explicit grain, logic separation, rich metadata, governed ownership) that make a metric layer AI-ready. Features practitioner interviews across Domo, Tableau, Looker, Power BI, and Qlik.
Free Press Journal · Apr 2026
The Analyst Building Credit Risk Infrastructure for America's Small Business Lenders
Profile on my work building the credit-risk systems behind $275M+ in SMB lending across five brands — and the forthcoming book.
Read the profile ↗Republic World · Mar 2026
War-risk premiums and global oil shipping costs
Quoted as a risk expert on war-risk insurance mechanics during the West Asia conflict — premium escalation from 0.25% to 1% of hull value and its knock-on effect on energy prices.
Read the article ↗NDTV · Expert commentary
GST collections, MSME challenges & India's digital tax infrastructure
Quoted on tax-reform challenges facing MSMEs — GSTN digital infrastructure, adoption hurdles, and input-tax-credit mechanics.
Read the article ↗International financial services
Invited industry training — Ferrum Capital
Invited to deliver training on credit-risk analytics based on published research.
Recognition
Industry Advisory Board — Southeastern Louisiana University
Incoming board member, advising on analytics and industry alignment; first board term begins December 2026.
Journal peer reviewer — Library Hi Tech (Emerald Publishing)
Invited reviewer for an established, indexed, peer-reviewed journal — evaluating other researchers' work in information systems and analytics.
Conference peer review & judging
Peer reviewer, IEEE INDISCON 2026 — Signal Processing, Computing & Data Science track; reviewer, ICAIS 2026 (International Conference on Artificial Intelligence Systems); invited judge, TECHNEX 2026, IIT (BHU) Varanasi — selected by invitation from practitioners with demonstrated expertise; judge, NMIMS University national hackathon.
Education
MS Business Analytics — UC Irvine, Merage School of Business. MBA Technology Management & BS Information Technology (Hons.) — NMIMS University, Mumbai.
Certifications & compliance
CSM®, Six Sigma Green Belt, Tableau Desktop Specialist (TDS-C01), IBM Data Science. Fair-lending compliance: ECOA, FCRA, TILA.
Let's talk about risk, data — or a role.
I'm always open to conversations about risk analytics, AI governance, and BI leadership roles in financial services.
hello@koustubhsharma.com