Business Analytics | Corporate & Commercial Banking | Credit & Risk Analytics | Financial Analysis | Power BI | SQL | Python | Excel
I am a banking and analytics professional with 14+ years of experience across corporate banking, relationship management, credit assessment, financial analysis, portfolio management and business development.
I have complemented this experience with postgraduate study in Business Analytics at the University of Waikato, New Zealand, building hands-on capability in SQL, Power BI, Python, Excel, Tableau and R.
My portfolio focuses on practical business problems across banking, credit risk, fraud, customer retention, segmentation and predictive analytics.
Python | PostgreSQL | Machine Learning | Power BI
End-to-end fraud analytics solution using 6.36M synthetic financial transactions, including data profiling, feature engineering, temporal model validation, risk-tier design and a ranked investigator alert queue.
- Champion HistGradientBoosting model
- 89.73% precision
- 84.76% recall
- 3,609 fraud-labelled transactions detected
- Five-page Power BI dashboard
PostgreSQL | SQL | Power BI
Credit-risk and portfolio analytics project covering 2.26M loan records and 145 source fields.
- Portfolio exposure analysis
- Credit-grade and DTI risk assessment
- Loan-performance segmentation
- Vintage and watchlist analysis
- Five-page Power BI dashboard
PostgreSQL | SQL | Power BI | DAX
Analysed 7,043 customer records to identify churn drivers, retention priorities, CLTV patterns and revenue-at-risk segments.
- Churn-driver analysis
- Customer retention prioritisation
- High-value at-risk segmentation
- CLTV analysis
- Five-page Power BI dashboard
Python | scikit-learn | Random Forest | Power BI
End-to-end campaign analytics and predictive targeting project using 45,211 bank marketing records.
- Logistic Regression and Random Forest comparison
- Champion Random Forest model
- 79.13% ROC-AUC
- Optimised targeting threshold
- Propensity scoring and priority targeting
- Five-page Power BI dashboard
PostgreSQL | SQL | Power BI | RFM
Customer segmentation project analysing approximately 397.88K transactions, 4.34K customers and $8.91M revenue.
- Recency, Frequency and Monetary modelling
NTILE(5)RFM scoring- Customer segmentation
- Revenue concentration analysis
- Five-page Power BI dashboard
Data & Analytics
- SQL
- PostgreSQL
- Power BI
- DAX
- Python
- pandas
- scikit-learn
- Excel
- Tableau
- R
Analytics & Modelling
- Data profiling
- Data cleaning
- Feature engineering
- Business intelligence
- Customer segmentation
- RFM analysis
- Credit-risk analytics
- Fraud analytics
- Churn analytics
- Predictive modelling
- Model evaluation
- Threshold optimisation
Banking & Commercial
- Corporate Banking
- Relationship Management
- Credit Analysis
- Financial Analysis
- Portfolio Management
- Risk & Compliance
- Trade Finance
- Foreign Exchange
- Stakeholder Management
I am particularly interested in opportunities across:
Business Analytics | Business Intelligence | Banking & Financial Analytics | Credit & Risk Analytics | Fraud & AML Analytics | Commercial & Operations Analytics | Financial Analysis | Portfolio Analytics | Corporate & Commercial Banking | Relationship Management
- LinkedIn: www.linkedin.com/in/jamiul-islam-01mji
- Location: New Zealand
I use analytics to connect data, financial understanding and commercial decision-making - with particular interest in solving problems across banking, risk, customer behaviour and business performance.