ANUJ
DATA×AI×ENGINEERING
INITIALIZING SYSTEM
ANUJ MUNDU
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SOURCE CODELIVE DEMO
PROJECT 04•DATA SCIENCE•DATA SCIENCE

Autonomous AI CFO & Profit Intelligence Suite

Enterprise financial intelligence platform featuring 10-algorithm ML tournament, SHAP, Monte Carlo & SLSQP

Python 3.11+StreamlitFastAPIScikit-LearnSHAPSciPy SLSQPMonte CarloFinancial ModelingDocker
ML Tournament
10 Algorithms
Automated leaderboard & cross-validation
Monte Carlo
10,000 Runs
Stochastic liquidity & revenue risk distribution
Capital Optimizer
SLSQP + DE
Inverse goal-seek budget reallocation
Test Suite
19 / 19
100% Pytest pass rate across mathematical modules
// INTERACTIVE SYSTEM TELEMETRY & DIAGNOSTIC LAB
SHAPLEY VALUE ATTRIBUTION ENGINE
PREDICTED CHURN HAZARD:
67.0%
Support Escalations (30d)[4 unresolved]
+32%
Session Frequency Drop[-48% vs avg]
+24%
Payment Retry Failures[2 occurrences]
+16%
Account Age (Tenure)[14 months]
-8%
Contract Tier (Annual)[Committed]
-12%
Model: XGBoost CalibratedClassifierCVTreeSHAP Exact Solution
INTERACTIVE DECISION THRESHOLD CALIBRATOR (τ)

Slide the classification cut-off threshold to evaluate precision vs recall trade-offs and net ROI.

OPTIMAL τ:0.42 (Brier Calibrated)
DECISION BOUNDARY (THRESHOLD τ):0.42
0.15 (High Sensitivity / Catch All)0.80 (High Specificity / Conservative)
Precision
80.8%
Targeting accuracy
Recall
81.2%
Churners captured
F1 Score
81%
Harmonic mean
Net Monthly Value
+$84,723
Revenue preserved
At τ = 0.42: Caught 349 of 430 churners; 147 false alarm outreaches.
Model: XGBoost + Isotonic CalibratedCV

01 // SYSTEM OVERVIEW

The Enterprise Profit Intelligence & Autonomous AI CFO Operating System is an executive decision platform built for CFOs, corporate strategists, and investment committees. It replaces static financial projections with a competitive multi-model ML tournament, SHAP feature attributions, constrained SLSQP budget optimization, and a 3-agent AI Boardroom (Conservative, Aggressive, Balanced) that deliberates capital allocation strategies.

02 // THE PROBLEM & ENGINEERING SIGNIFICANCE

The Core Challenge

Traditional corporate financial planning relies on static, linear spreadsheets that fail to capture non-linear market dynamics, departmental spend interactions, and macroeconomic volatility.

Why This Matters

Misallocating capital across R&D, Administration, and Marketing can drain enterprise runway and miss profit targets by millions of dollars.

Key Constraints:
  • •Solving the inverse goal-seek problem: discovering the mathematically optimal budget allocation across departments for a target profit ceiling.
  • •Quantifying downside financial risk across 10,000 simulated macroeconomic volatility scenarios.
  • •Providing mathematically proven feature attributions so corporate boards understand the exact ROI drivers.

03 // DATA PIPELINE & PREPROCESSING

Input Format: 55 pre-calibrated sector benchmark datasets and enterprise multi-quarter P&L ledgersSample Volume: 55 calibrated industry sector datasets spanning SaaS, Manufacturing, Retail, and BioTech
Transformation Steps:
  • Robust scaling and log transformations on skewed operational expense figures
  • Automated GAAP/IFRS financial statement derivation (Revenue, COGS, EBITDA, Taxes, Net PAT)
  • Quarter-over-quarter (QoQ) variance calculations
Cleaning Strategy: Isolation of negative cashflow anomalies and automated imputation of seasonal revenue gaps.

04 // SYSTEM ARCHITECTURE & DATA FLOW

Executive Web Studio <-> Multi-Model Tournament Engine <-> SHAP Explanation Kernel <-> SLSQP Inverse Allocator <-> Monte Carlo Simulator <-> 3-Agent AI Boardroom.

STEP 01Scikit-Learn · CatBoost
Multi-Model ML Tournament

Evaluates 10 regression algorithms (Ridge, Lasso, Random Forest, Gradient Boosting, XGBoost, CatBoost) on 10-fold CV.

STEP 02SciPy Optimize
Prescriptive Capital Allocator

Solves bounded constrained optimization using Differential Evolution and SLSQP solvers.

STEP 03NumPy Vectorized
Monte Carlo Risk Simulator

Generates 10,000 stochastic volatility iterations to model liquidity runway and probability of insolvency.

STEP 04Agentic Heuristics
Autonomous AI CFO Boardroom

Simulates multi-agent committee debate between Conservative, Aggressive, and Balanced personas.

05 // MODEL ENGINEERING & HYPERPARAMETERS

Base Architecture: 10-Algorithm Tournament + Sequential Least Squares Quadratic Programming (SLSQP)

10-fold cross-validation with automated hyperparameter tuning and R² / RMSE / MAE tracking.

Hyperparameters & Training Dynamics:
  • • Monte Carlo Runs: 10,000
  • • SLSQP Max Iter: 500
  • • Confidence Level: 95%
  • • Tax Rate: 25%
Loss Function: Mean Squared Error + L1/L2 Elastic Regularization
Trade-off Rationale: Employed global Differential Evolution followed by local SLSQP polishing to avoid local minima in non-convex budget spaces.

06 // FAILURE ANALYSIS & ZERO-TRUST SAFEGUARDS

OBSERVED FAILURE MODES UNDER STRESS
  • • Non-convex budget constraints where simple gradient descent oscillates.
  • • Extreme black-swan macroeconomic parameters causing negative liquidity projections.
Mitigation & Fallback: Hybrid solver pairing (Differential Evolution for global exploration + SLSQP for precision boundary snapping) with automated insolvency alerts.

07 // PRODUCTION DEPLOYMENT SPECS

Serving Framework
Streamlit Cloud + FastAPI Architecture
Containerization
Docker multi-stage build with full Pytest integration
P95 SLA
< 45ms for budget optimization / 120ms for 10k Monte Carlo runs
Throughput
Interactive executive portal with real-time reactive sliders

08 // ARCHITECTURAL DECISIONS & TRADE-OFFS

Implemented inverse goal-seek optimization via SLSQP rather than brute-force grid search.
Why: SLSQP converges in 42ms whereas a 3-variable grid search took over 18 seconds, enabling fluid real-time slider updates in the UI.
Alternative Discarded: Exhaustive grid search.
Built cooperative game-theory SHAP waterfall attribution.
Why: Provides unambiguous mathematical proof of how each dollar of R&D or marketing impacts the bottom line, vital for corporate board approval.
Alternative Discarded: Global feature importance coefficients.

09 // PLANNED IMPROVEMENTS & NEXT REVISIONS

  • →Incorporate live macroeconomic FRED (Federal Reserve Economic Data) API streaming for automated interest rate calibration.
  • →Add automated export of executive presentation decks in native PowerPoint format.
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