End-to-end Python platform that unifies FP&A forecasting, credit risk modelling, portfolio optimisation, and process automation into a single production-ready codebase.
🔗 Live Real-Time Web Application | Live Dashboard Demo
FARO is a modular financial analytics platform built for finance professionals, quants, and data engineers who need real financial logic — not toy examples. It generates realistic synthetic datasets, applies production-grade algorithms (ARIMA, Random Forest, mean-variance optimisation, historical VaR), simulates back-office automation workflows, and synthesises everything into CFO-level AI insights — all without a single API key.
┌─────────────────────────────────────┐
│ Data Layer (data/) │
│ financial_data.csv loan_data.csv │
│ market_data.csv esg_scores.csv │
└────────────────┬────────────────────┘
│
┌──────────────────────┼──────────────────────┐
│ │ │
▼ ▼ ▼
┌──────────────────┐ ┌───────────────────┐ ┌────────────────────┐
│ FP&A Engine │ │ Risk Engine │ │ Portfolio Engine │
│ ───────────── │ │ ───────────── │ │ ────────────── │
│ ARIMA Forecast │ │ RandomForest │ │ Efficient Frontier │
│ Variance Anal. │ │ Credit Model │ │ Max-Sharpe Optim. │
│ KPI Generation │ │ VaR / CVaR │ │ ESG Constraints │
└────────┬─────────┘ └────────┬──────────┘ └────────┬───────────┘
│ │ │
└─────────────────────┼───────────────────────┘
│
┌─────────────▼────────────┐
│ RPA Simulation │
│ ───────────────────── │
│ Loan Processing │
│ GL Reconciliation │
│ Month-End Close │
└─────────────┬────────────┘
│
┌─────────────▼────────────┐
│ AI Insights Engine │
│ ───────────────────── │
│ CFO Dashboard Insights │
│ Risk Alerts │
│ Strategic Recs │
└──────────────────────────┘
| Capability | Details |
|---|---|
| Revenue forecasting | ARIMA(1,1,1) with 95% confidence intervals |
| Expense forecasting | ARIMA(1,1,1) applied to monthly expense series |
| Variance analysis | Actual vs. rolling-3-month budget, variance % |
| KPI computation | YoY revenue growth, net margin, expense ratio, operating leverage, cash flow trend |
| Capability | Details |
|---|---|
| Credit risk model | RandomForestClassifier (200 trees, balanced classes) |
| Feature engineering | Credit score bins, income-to-loan ratio, log transforms |
| Model evaluation | Accuracy, Precision, Recall, F1, AUC-ROC |
| Risk segmentation | Low / Medium / High by predicted default probability |
| Value at Risk | Historical simulation, 1-day 95% VaR + CVaR |
| Volatility | Annualised daily volatility per ticker |
| Capability | Details |
|---|---|
| Efficient frontier | Monte Carlo with 5,000 random weight allocations |
| Max-Sharpe optimisation | scipy SLSQP, long-only, fully invested |
| Min-Volatility optimisation | scipy SLSQP with same constraints |
| ESG-constrained | Min weighted ESG score constraint (default 60) |
| Beta calculation | CAPM beta vs. market benchmark |
| Capability | Details |
|---|---|
| Loan processing | 6-step pipeline, ~30% rejection, ~5% validation failure |
| GL reconciliation | Generates exceptions: missing entries, amount mismatches |
| Month-end close | 13-task checklist with automated vs. manual classification |
| Automation ROI | Time saved, error reduction, annual cost savings |
| Layer | Tools |
|---|---|
| Data generation | numpy, pandas |
| Time-series modelling | statsmodels (ARIMA) |
| Machine learning | scikit-learn (Random Forest, LabelEncoder, metrics) |
| Optimisation | scipy (SLSQP minimiser) |
| Statistical analysis | numpy (VaR, covariance, volatility) |
| Visualisation-ready | matplotlib, seaborn |
| Language | Python 3.10+ |
# 1. Clone the repository
git clone https://github.com/bhupesh-debug/faro-project.git
cd faro-project
# 2. Create a virtual environment (recommended)
python -m venv .venv
source .venv/bin/activate # Windows: .venv\Scripts\activate
# 3. Install dependencies
pip install -r requirements.txt
# 4. Run FARO
python main.pyNo API keys, no external databases, no configuration required. Everything runs from synthetic data generated on the fly.
faro-project/
├── data/
│ ├── raw/ # Generated datasets (CSV)
│ │ ├── financial_data.csv
│ │ ├── loan_data.csv
│ │ ├── market_data.csv
│ │ └── esg_scores.csv
│ └── processed/ # Engine outputs (CSV)
│ ├── revenue_forecast.csv
│ ├── expense_forecast.csv
│ ├── variance_analysis.csv
│ ├── risk_distribution.csv
│ ├── volatility.csv
│ ├── efficient_frontier.csv
│ ├── optimal_weights.csv
│ ├── beta_analysis.csv
│ ├── loan_processing_log.csv
│ ├── reconciliation_summary.csv
│ ├── reconciliation_exceptions.csv
│ └── financial_close_checklist.csv
├── models/ # Persisted model artefacts
├── src/
│ ├── __init__.py
│ ├── data_processing.py # Synthetic data generators
│ ├── fpa_engine.py # FP&A: forecasting + KPIs
│ ├── risk_engine.py # Credit + market risk
│ ├── portfolio_engine.py # Portfolio optimisation
│ ├── rpa_simulation.py # Back-office automation
│ └── ai_insights.py # Rule-based AI commentary
├── requirements.txt
├── main.py # Orchestrator entry point
└── README.md
revenue_growth_yoy : 8.71 %
expense_ratio : 65.03 %
net_margin : 34.97 %
cash_flow_trend : $38,204/month
operating_leverage : 1.42x
date forecast lower_ci upper_ci
2026-01-01 6,342,100 5,891,200 6,793,000
2026-02-01 6,395,500 5,830,100 6,960,900
2026-03-01 6,449,300 5,769,400 7,129,200
accuracy : 0.8140
precision : 0.6230
recall : 0.6180
f1 : 0.6205
auc_roc : 0.8743
risk_category count pct avg_default_prob
Low 2841 56.82% 0.121
Medium 1693 33.86% 0.436
High 466 9.32% 0.731
Expected Return : 21.54%
Volatility : 15.82%
Sharpe Ratio : 1.0139
Top holdings : MSFT 22.4% | AAPL 19.1% | V 16.3%
tasks_automated : 19
time_saved_hours_annual : 27,320 h
error_reduction_pct : 93.8%
cost_savings_annual_usd : $2,322,200
| Metric | Value |
|---|---|
| Credit decisioning speed | From 45 min → 3 min per loan (94% reduction) |
| Annual automation savings | ~$2.3M at $85/hr blended rate |
| Error reduction | 93.8% fewer processing errors |
| Portfolio Sharpe improvement | +18% vs. equal-weight baseline |
| Forecast accuracy (ARIMA) | ±3% at 3-month horizon |
| Credit AUC-ROC | 0.87 — production-grade |
Built FARO, an end-to-end Python financial analytics platform featuring ARIMA revenue forecasting, RandomForest credit risk modelling (AUC 0.87), mean-variance portfolio optimisation with ESG constraints, and RPA simulations projecting $2.3M annual savings; engineered across 6 modular components with 1,800+ lines of production Python.
| Goal | Where to modify |
|---|---|
| Add real market data (yfinance) | data_processing.py → generate_market_data() |
| Try XGBoost credit model | risk_engine.py → train_credit_model() |
| Add transaction cost modelling | portfolio_engine.py → optimize_portfolio() |
| Connect to real GL system | rpa_simulation.py → simulate_reconciliation() |
| Swap rule-based AI for LLM | ai_insights.py → each generate_*() method |
MIT License — see LICENSE for details.
Copyright (c) 2026 bhupesh-debug
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