Quantitative Systems for Credit

I help private credit funds, fund admins, and credit-focused fintechs move faster: building the models, portfolio analytics, AI-driven automation, and data infrastructure that turn manual analyst work into reliable systems they can scale on.

Ex-Morgan Stanley Quant Dev · 5+ years building production systems · RMBS, CLO & Whole Loans
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The problem
What I see at most credit
and structured finance teams
01

Analyst hours lost to data work.
Senior analysts spending a significant portion of their time on data wrangling, reconciliation, and report production instead of investment work.

02

No single source of truth.
Critical portfolio data scattered across Excel, PDFs, fund admin systems, and inboxes — with no consolidated view.

03

Processes that don't scale.
Manual workflows that worked at €200M AUM breaking down at €1B+ — and replacing them feels too risky to start.

Background
Where I come from
Morgan Stanley — Quantitative Developer
RMBS · CLO · Whole Loans · 5 years
Owned end-to-end maintenance and modernization of the firm-wide RMBS portfolio valuation, risk, and stress-testing models.
Maintained the RWA calculation models for all fixed income structured products across the firm, supporting regulatory reporting and capital management.
Led automation initiatives that eliminated hundreds of analyst hours per month across credit modeling workflows.
End-to-end project ownership: from stakeholder requirements through delivery, with full autonomy over planning and execution.
Daniele Sbaffo
Track record
Beyond Credit
Alongside this practice, I run Vestige: a SaaS product used by european audit firms. The discipline of shipping software paying customers depend on carries directly into engagements.
"Vestige saved us hundreds of hours during audit season."
Gabor, CEO and Senior Auditor MSzH
Capabilities
What I do
I work across the full stack of financial data problems — from raw data ingestion to production-grade analytical systems.
01

Data Infrastructure

From messy source data to reliable analytics.
Ingesting and normalizing servicer tapes across formats and jurisdictions; consolidating fund admin reports, custodian feeds, and Excel inputs into a single source of truth; building reconciliation systems that catch breaks before month-end.

02

Quantitative Modeling

Valuation, risk, stress test, and cash flow modeling for credit.
Rebuilding legacy structured credit models in modern Python with full test coverage; recalibrating prepayment and default models against recent vintages; building portfolio-level scenario frameworks that run in minutes, not days.

03

Workflow Automation

Replacing manual analyst work with systems that run on their own.
Automating monthly investor reporting; building covenant tracking and breach alerting; turning recurring spreadsheet processes into versioned, auditable pipelines.

04

AI / ML Implementation

Modern techniques where rules-based approaches break down.
Extracting structured data from offering memos, indentures, and credit agreements; classifying and tagging unstructured loan documentation; anomaly detection on portfolio metrics; LLM-assisted review of large document sets.

What's different
Why work with me
01

Specialist depth where it's rare.
Structured credit is a small world. I've spent 5 years inside it at Morgan Stanley, building the kind of production systems most credit teams can't easily hire for.

02

Technical capability credit firms struggle to access.
Modern data engineering, automation, and AI work usually requires either expensive consultancies or technical hires who then need months to learn credit. I bring the technical execution directly, without the domain ramp.

03

Senior on the actual work.
No junior layer, no offshored implementation. The person you scope with is the person who builds.

04

Scoped engagements with clear outcomes.
I work on fixed-scope projects and retainers, not open-ended time-and-materials. Every engagement starts with a defined deliverable and an exit point.

How I work
Three ways to engage
01

Diagnostic · 1 to 2 weeks
I assess your current systems and workflows. You get a written findings document with prioritised recommendations and a delivery roadmap.

02

Build · 4 to 12 weeks
Scoped delivery of a defined system, model, or pipeline. Fixed scope, fixed timeline, production-ready output your team can maintain.

03

Retainer · ongoing
Fractional senior support for teams without in-house quant or data engineering capacity. Predictable monthly engagement, scoped per quarter.

If any of this sounds familiar, let's talk.
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