CPA Finalist working at the intersection of finance and data science, building the credit-risk and fraud models that sit downstream of development-finance operations.
| Project | What it does | Result |
|---|---|---|
| Credit Risk Scorecard (live) | From-scratch WoE/IV and logistic regression validated line-for-line against scikit-learn on 307,511 real Home Credit applicants, plus a LightGBM benchmark | 0.7774 AUC (LightGBM), 0.9985 correlation vs sklearn |
| Fraud Detection System | XGBoost fraud classifier on 6.3M PaySim mobile-money transactions: balance-discrepancy feature engineering, isotonic calibration, walk-forward validated across 4 folds | 99.85% precision / 99.56% recall |
| Financial-Analyst | Three-statement models and DCF valuations built from primary-source SEC filings, with a validation tab tying every historical line back to source | Fully source-linked |
| Stock-Portfolio-Tracker-Analytics-Engine | Portfolio risk/performance analytics engine in Excel: VaR/CVaR, CAPM, Black-Litterman optimisation, tax-aware rebalancing | 23-test validation suite |
Credit risk: WoE/IV, scorecard development, GINI/KS/PSI, IFRS 9 ECL. Fraud: imbalanced classification, cost-sensitive thresholding, PR-AUC-first evaluation. Finance: GAAP/IFRS, 3-statement modelling, DCF valuation.
Open to Credit Risk Analyst, Data Analyst, and Financial Data Scientist roles.
📫 alvenyuka2@gmail.com · 💼 LinkedIn