FRAUD & RISK ANALYTICS · MS Business Analytics
I turn complex data into clear decisions across fraud, payments, insurance risk, and operations.
01 — DECISION MODELING
An imbalanced classification study comparing models and sampling strategies, with interpretable drivers and a documented evaluation process.
90% recall on the held-out test set · Python / scikit-learn / XGBoost
02 — BUSINESS INTELLIGENCE
An interactive Excel dashboard exploring sales across 1,000 order lines and three countries, with clear comparisons for partial-year data.
Excel / XLOOKUP / PivotTables / slicers
03 — CUSTOMER ANALYTICS
A decision-tree classification project for identifying customers likely to accept a personal loan. See the repository for the current evaluation details.
Python / scikit-learn / Decision Trees
04 — FORECASTING
A time-series case study focused on turning historical patterns into planning insights.
Python / time-series analysis
I bring an actuarial science foundation and an MS in Business Analytics to problems involving fraud monitoring, payment operations, insurance risk, and business performance. I work across the analysis cycle—from framing the question and evaluating evidence to communicating practical next steps.
Analytics Python · SQL · statistical analysis
Modeling Classification · forecasting · regression
Decision support Excel · Tableau · Power BI · stakeholder reporting
Open to roles in fraud analytics, risk strategy, and business analytics.