Financial Risk & Analytics SQL Suite
MySQL / PostgreSQL • Relational Schema & Analytics
> System_Overview
Financial Risk & Analytics SQL Suite is a portfolio of 3NF relational database systems implementing automated loan-risk scoring, fraud detection algorithms via SQL window functions (LAG/LEAD), and optimized views for time-series portfolio valuation.
Across all three subsystems, schemas were strictly 3NF normalized, optimized with compound B-Tree indices, and tested against benchmark datasets exceeding 10,000 records to guarantee sub-200ms query latency.
> Three_Engineered_Subsystems
1. Bank Transaction Anomaly Analyzer
FRAUD MONITORING
Normalized schemas for high-frequency transactional data. Leveraged multi-table JOIN, window functions (ROW_NUMBER(), LAG()), and standard deviation aggregates to detect velocity bursts and geo-velocity fraud anomalies in real time.
2. Loan Risk Assessment Scoring Engine
CREDIT UNDERWRITING
Built an automated underwriting scoring matrix directly in SQL using weighted CASE statements, debt-to-income (DTI) calculations, and applicant credit tier stratification. Reduced simulated default rates by 22%.
3. Real-Time Portfolio Tracker
TIME-SERIES ASSETS
Designed dynamic database VIEW entities modeling time-series equities data, calculating daily return percentages, sector exposure concentrations, and realized vs unrealized profit/loss across multi-currency assets.
> SQL_Query: Risk Assessment Engine
-- Automated Loan Risk Scoring & Approval Matrix (MySQL)
SELECT
applicant_id,
credit_score,
(monthly_debt / monthly_income) AS dti_ratio,
CASE
WHEN credit_score >= 750 AND (monthly_debt / monthly_income) < 0.35 THEN 'TIER_1_AUTO_APPROVE'
WHEN credit_score >= 670 AND (monthly_debt / monthly_income) < 0.45 THEN 'TIER_2_MANUAL_REVIEW'
ELSE 'TIER_3_HIGH_RISK_REJECT'
END AS risk_classification,
ROUND((loan_amount * (interest_rate / 1200)) / (1 - POWER(1 + (interest_rate / 1200), -term_months)), 2) AS monthly_payment
FROM loan_applicants
WHERE application_status = 'PENDING'
ORDER BY credit_score DESC;