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1
Economic Conditions
market rate, GDP, unemployment
→
2
Expected Demand
elasticity + random shock
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3
Generate Applicants
credit score, income, DTI, amount
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4
Approval Rules
deterministic checks
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5
Funding
capital pool & results
1. Economic Conditions
The simulation starts with a macroeconomic environment that drives credit demand and applicant behaviour.
EconomicConditions { market_interest_rate, gdp_growth, unemployment_rate, credit_demand_index }
market_interest_rate — benchmark rate (e.g. central bank). Applicants compare this to the supplier's offered rate.gdp_growth — higher growth increases demand for credit (more investment, consumption).unemployment_rate — higher unemployment suppresses demand and worsens applicant credit profiles.credit_demand_index — overall market multiplier (1.0 = normal conditions).
2. Expected Demand
The number of applicants each period is computed using elasticities relative to baseline economic conditions, plus a random shock.
rate_effect = exp(rate_elasticity × |supplier_rate − market_rate|)
interest_rate_elasticity (−2.0) — negative because higher supplier rates relative to market reduce demand.gdp_elasticity (1.5) — positive: growing economy → more loan applications.unemployment_elasticity (−0.8) — negative: higher unemployment reduces demand.demand_shock_std (0.15) — stochastic shock captures unpredictable market fluctuations.
3. Applicant Generation
Each applicant is an independent random draw from probability distributions calibrated to realistic credit market profiles.
credit_score ~ Normal(700, 60), clipped to [300, 850]
Credit score follows a normal distribution centred at 700 (good credit), truncated to the 300–850 range.Income uses a lognormal distribution to model the typical right-skewed income distribution.Debt-to-income ratio follows a Beta(2,5) distribution, concentrated in 0.1–0.3.Each applicant is assigned a risk grade (AAA through C) based on their credit score.
4. Approval Rules
Approval is a purely deterministic pipeline: each applicant either passes all checks or is rejected.
approve = (credit_score ≥ min_credit_score)
Credit score floor — e.g. 660; applicants below this are rejected.Amount bounds — loan must be within the supplier's min/max range.Capital check — enough remaining capital must exist.DTI limit — hard cap of 43% (standard lending guideline).Rate premium — applicant's proposed rate cannot be more than 2% below the supplier's rate.
5. Funding & Results
Approved loans are funded from the supplier's capital pool. The simulation runs period by period until capital is exhausted or all periods are completed.
remaining_capital = total_capital − sum(funded_amounts)
Each period generates a record: applicant count, approvals, funded volume, remaining capital.
The simulation stops early if capital is exhausted (remaining less than minimum loan).
NumPy's seeded PRNG (numpy.random.default_rng) makes results reproducible for any given seed.
The default config uses seed=42 and runs 12 periods with $10M capital.
Parameters
Run Simulation
Results
Period
Applicants
Approved
Approval Rate
Volume ($)
Cap. Remaining ($)
Utilization