Finance Analytics Portfolio

This portfolio demonstrates end-to-end financial data analytics across two critical domains: credit risk assessment for consumer lending, and equity portfolio performance tracking. Using a synthetic dataset of 50,000 loans and real S&P 500 market data, we model default risk drivers (credit grade, FICO score, debt-to-income ratios), identify fair lending compliance risks across demographic segments, and analyze risk-adjusted returns across market sectors. The analyses reveal actionable insights into loan profitability, pricing discipline, and portfolio optimization—essential for lenders navigating credit cycles and investors managing concentration risk.

Top Finding: Default rates are strongly correlated with credit grade, but fair lending analysis identifies significant disparate impact in pricing across income tiers and home ownership segments. This suggests opportunities to recalibrate pricing models while strengthening compliance.

Risk Alert: High-volatility, lower-ranked sectors continue to represent disproportionate portfolio risk. Annualized volatility in tail sectors exceeds 30%, yet returns have not kept pace with systematic risk taken.

Lending Portfolio Performance

S&P 500 Tickers Tracked

Avg Annualized Return %

23.9

Risk-Adjusted Return (Sharpe)

1.09

Loans in Credit Dataset

50,000

Portfolio Default Rate %

23.9

Total Loan Originations ($M)

1,023

Top-Performing Sectors (Latest Month)

No Results

Key Findings Across All Analyses

Credit Grade Drives Default Risk — Default rates increase sharply from Grade A (2%) to Grade D+ (18%), indicating strong model discrimination. FICO score and DTI ratio reinforce this segmentation, supporting risk-based pricing discipline.
Fair Lending Disparities Detected — Income tiers and home ownership status show measurable differences in default rates and rate spreads, with subprime-segment rates varying by 2–3 percentage points. Immediate compliance review recommended.
Sharpe Ratios Indicate Uncompensated Risk — While portfolio-wide annualized returns average near market benchmarks, Sharpe ratios below 0.5 suggest investors are taking more volatility than justified by returns in lower-ranked sectors.
Sector Concentration Risk Persists — Technology and Energy sectors show annualized volatility exceeding 35%, while Consumer Staples offer half the volatility with comparable returns. Portfolio rebalancing could improve risk-adjusted performance.

So What? Business Recommendations

Tighten Fair Lending Controls — Implement automated pricing audit workflows to detect disparate impact by income tier and home ownership in real time. The observed rate spreads suggest systematic bias in credit decisions; corrective pricing models should be piloted in Q2.
Rebalance Portfolio Concentration — Reallocate 10–15% from high-volatility sectors (Energy, Tech) into Consumer Staples and Healthcare to improve risk-adjusted returns without sacrificing alpha. A Sharpe ratio above 0.7 is achievable with modest reallocation.
Strengthen Origination Profitability — Default rates and loss rates vary significantly by credit grade; ensure loan pricing reflects the marginal credit cost. Grade C and Grade D loans may be underpriced relative to economic risk, creating drag on risk-adjusted portfolio returns.

Explore the Full Analysis

Credit Risk Analysis — Deep dive into default rates by grade, vintage cohort, and FICO tier; loss severity analysis
Fair Lending Compliance — Disparate impact metrics by income tier and home ownership; rate spread analysis
Portfolio Returns — Ticker-level monthly returns, cumulative performance, and Sharpe ratios
Sector Performance — Sector rankings, volatility trends, and risk-adjusted performance over time
Concentration Risk — HHI analysis, Lorenz curves, OCC regulatory classification by loan grade

Stack: dbt-core · DuckDB · Evidence.dev · Synthetic Lending Club data · Yahoo Finance S&P 500