regulatory-analytics

PostgreSQL Metro 2-style remediation sandbox for synthetic longitudinal tradelines, credit-impact windows, cure logic, treatment assignment, QA, and audit-ready before/after reporting.

High-scale SAS TVM engine using 1-year CMT Treasury rates, PROC FCMP arrays, chunked processing, annual compounding, and final remainder interest for remediation liability calculations.

Metadata-driven SAS engine for insurance coverage reconciliation: merges overlapping proof periods, applies lapse-threshold rules, and calculates policy adjustment factors and executive liability signals.

SAS reconciliation and audit-governance engine for tolerance-aware A/B dataset comparison, schema drift, key-only/value differences, run metadata, UAT validation, and CSV outputs.

Python forensic data-integrity engine that scores raw datasets, detects hidden nulls, bias, format defects, and zero-variance fields, and generates audit ledgers and executive scorecards.

GenAI-powered regulatory risk analysis engine using Isolation Forest and OpenAI API for automated compliance insights.

Python data analysis project exploring SLA breaches, processing delays, workload patterns, and early-warning risk prediction using a mock regulatory applications dataset.

Public portfolio of enterprise data strategy and governed decision systems spanning credit strategy, survival modeling, forensic data quality, reconciliation, regulatory remediation, model/data validation, and executive decision support—built with SAS, SQL/PostgreSQL, Python, and Power BI.

Python fund risk analytics for AIFM / ManCo workflows, covering leverage, VaR, stress testing, derivatives and liquidity methodology.