About
I sit at the intersection of credit policy, loss forecasting, and operational intelligence in fintech lending. At National Funding / FairSquare I lead credit-risk analytics across a $277.5M portfolio spanning five brands — National Funding, QuickBridge, SmallBusinessLoans, NFXprs, and Finova Capital.
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, and Tableau.
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.
Experience
Business Intelligence & Risk Analyst · National Funding / FairSquare
Oct 2024 — Present- Lead credit-risk analytics and BI infrastructure across a $277.5M portfolio spanning 5 lending brands — National Funding, QuickBridge, SmallBusinessLoans, NFXprs, and Finova Capital.
- 2025 performance vs. forecast: gross charge-offs $10.8M favorable (−16.6%), net charge-offs $15.1M favorable (−29.6%), recoveries $4.3M above plan (+30.3%).
- Discovered and resolved a $1M+/month write-off reconciliation gap through a systematic recon protocol — adopted as the company-wide standard.
- Led syndication portfolio expansion under incomplete data using 3-scenario stress analysis — finished +59.7% above target; tracked $698.1M in 2025 originations against forecast.
- Drove risk-vs-sales alignment through partial policy tightening plus a 90-day review framework — stabilized delinquency without killing volume.
- Built the unified risk-surveillance system from scratch (Snowflake + Tableau) and trained 12 analysts on it; automated reporting saves 20+ hrs/month and cut manual reconciliation 30%.
- PD / LGD / EAD
- Vintage & roll-rate
- Credit policy
- Snowflake
- Tableau
- Python
Risk Analyst · National Funding
Jun 2024 — Oct 2024- Eliminated 30 hrs/week of manual reporting by moving the team from spreadsheets to SQL/Python → Tableau pipelines — doubled refresh frequency, cut investigation time 20%.
- Stood up Domo as the team's reporting foundation from scratch; trained 12 analysts and managers. The infrastructure is still in active use.
- Domo
- SQL
- Python
- Tableau
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 (UC Irvine) · Digital & IT Intern (Wellness Forever) · Technology Consultant (D-Sys) · Earlier
2021 — 2024- TA for MGMT-90 at UCI's Merage School; at Wellness Forever, analyzed 90,000+ SKUs to build the impulse category (+25% sales) and cut partner-onboarding turnaround 50% through workflow digitization.
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.
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 & competition judging
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.
koustubh@koustubhsharma.com