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.

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.

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.

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.

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.

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.

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.