PE Org AI R Platform – Financial Grade AI Scoring Engine Case Study 3

Описание: We demonstrate the design, implementation, and validation of the PE Org-AI-R Financial-Grade AI Scoring Engine — a configuration-driven, sector-calibrated AI Readiness scoring system built for Private Equity portfolio evaluation.
This case study transforms structured evidence (SEC filings, job signals, innovation signals, digital presence, leadership data) into a reproducible, auditable, and risk-adjusted AI Readiness score.

🔹 Project Highlights:
• Configuration-driven architecture (no hardcoded weights)

• Unified schema supporting 7 sectors × 7 dimensions

• Financial-grade Decimal precision (no floating-point risk)

• Non-compensatory statistical penalty using Coefficient of Variation

• Talent concentration risk adjustment (HHI-style penalty)

• Redis-backed configuration caching (1ms retrieval)

• Temporal weight versioning with rollback capability

• Property-based testing (Hypothesis) for invariants

• Sensitivity analysis and monotonicity validation

• Structured JSON logging (audit-ready replayability)

• Confidence Intervals using Spearman-Brown reliability modeling

• Strict clamping and bounded outputs [0–100]
🔹 Scoring Framework Overview:
The VR (Idiosyncratic Readiness) Engine:
VR = Dw × (1 − λ · cvD) × TalentRiskAdj
Where:

• Dw = Weighted Mean of 7 dimensions

• cvD = Coefficient of Variation (dispersion penalty)

• TalentRiskAdj = Key-person risk multiplier
Org-AI-R Aggregation:
Org-AI-R = (1 − β)[αVR + (1 − α)HR] + β × Synergy
Includes:

• Position-based HR adjustment

• Alignment and timing multipliers

• Dynamic Confidence Intervals

• Reliability-adjusted uncertainty modeling
🔹 Architecture Stack:
• FastAPI (API layer)

• Pydantic (data validation)

• Snowflake (production data warehouse)

• PostgreSQL (dev environment)

• SQLAlchemy 2.0 (async ORM)

• Redis (configuration caching)

• Alembic (schema migrations)

• Decimal-based financial arithmetic

• Hypothesis (property-based testing)

• structlog (audit logging)
🔹 Case Study Scope:
This submission covers:
• Dimension scoring engine implementation

• Configuration loading and weight calibration

• Redis caching layer integration

• Financial-grade precision controls

• Statistical penalty modeling

• Reliability-based uncertainty quantification

• Robust testing strategy (unit + property testing)

• Sensitivity analysis

• End-to-end scoring validation
🔹 Course Information:
Course: DAMG 7245 – Big Data Systems & Intelligent Analytics

Instructor: Prof. Sri Krishnamurthy

Term: Spring 2026

Group: 1