Private Credit

Deep Generative Models for Private Credit SPV Analytics

A hierarchical framework for modeling Special Purpose Vehicles (SPVs) that securitize loan portfolios across multiple asset classes.

Key Features

Component Architecture Purpose
Macro VAE Conditional LSTM-VAE Generate correlated macro scenarios
Transition Transformer Transformer Encoder Cohort-level state dynamics
Loan Trajectory Model AR Decoder + Diffusion Individual loan path generation
Portfolio Aggregator Differentiable Waterfall Loss distribution & tranche returns

Quick Start

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pip install privatecredit

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from privatecredit.data import LoanTapeGenerator, MacroScenarioGenerator
from privatecredit.models import MacroVAE, PortfolioAggregator

# Generate synthetic portfolio
generator = LoanTapeGenerator(n_loans=10000)
loans, panel = generator.generate()

# Generate macro scenarios
macro_gen = MacroScenarioGenerator()
baseline = macro_gen.generate_scenario('baseline')
adverse = macro_gen.generate_scenario('adverse')

Architecture Overview

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Level 1: MACRO SCENARIO GENERATOR (VAE)
    |-- GDP, Unemployment, Credit Spreads
    v
Level 2: TRANSITION TRANSFORMER
    |-- Cohort-level transition matrices
    v
Level 3: LOAN TRAJECTORY MODEL
    |-- Individual loan state & payment sequences
    v
Level 4: PORTFOLIO AGGREGATOR
    |-- Waterfall, VaR, CVaR, Tranche Returns

Asset Classes Supported

  • Corporate Loans: SME, mid-market, large corporate
  • Consumer Credit: Personal loans, auto loans
  • Real Estate: Commercial and residential mortgages
  • Trade Receivables: Invoice financing, factoring

Use Cases

Application Output
Pricing Fair value of SPV tranches
Risk Management VaR, CVaR, capital allocation
Regulatory IFRS 9 ECL, Basel IRB, Solvency II
Investment Risk-adjusted returns by tranche

Documentation

Citation

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@software{privatecredit2026,
  title = {Private Credit: Deep Generative Models for SPV Analytics},
  author = {Digital Finance Research},
  year = {2026},
  url = {https://github.com/Digital-AI-Finance/private-credit}
}

License

MIT License - see LICENSE

View on GitHub