Data defines the model by dint of genetic programming, producing the best decile table.
GenIQ Articles: Solutions
Bruce Ratner, Ph.D.


    1. Subprime Lender Short Term Loan Models for Credit Default and Exposure
    2. Credit Risk Modeling – A Machine Learning Approach
    3. Finding Tax Cheaters Easily
    4. CRM Success with Data Mining
    5. Retail Revenue Optimization: Accounting for Profit-eating Markdowns
    6. Nonprofit Modeling: Remaining Competitive and Successful
    7. Detecting Fraudulent Insurance Claims: A Machine Learning Approach
    8. Demand Forecasting for Retail: A Genetic Approach
    9. CRM: Cross-Sell and Up-Sell to Improve Response Rates and Increase Revenue
    10. Performance Management: Improve It via Machine Learning
    11. Risk Management for the Insurance Industry: A Machine Learning Approach
    12. Credit Scoring: A New Approach to Control Risk
    13. Customer-Value Based Segmentation: An Overview
    14. Trigger Marketing: Predicting the Next Best Offer to Give Customers
    15. Marketing Mix Model: Right Offer, Right Time, and Right Channel
    16. Building a CRM Model for Identifying Profitable Leads: The Genetic Contact-Profit Model
    17. A Machine Learning Approach to Conjoint Analysis
    18. Subprime Borrower Market: Building a Subprime Lender Scoring Model for a Homogeneous Segment
    19. The Financial Services Problem-Solution: Reduce Costs, Increase Profits by Data Mining and Modeling
    20. Retail Revenue Optimization: A Model-free Approach
    21. Fraud Detection: Beyond the Rules-Based Approach
    22. Product Positioning: Predicting the Next Best Offer to Give Customers
    23. Marketing Mix Model: A Genetic Approach
    24. Optimizing Customer Loyalty
    25. Telecommunication Fraud Reduction: Analytical Approaches
    26. The Banking Industry Problem-Solution: Reduce Costs, Increase Profits by Data Mining and Modeling
    27. Fundraising Modeling: Competitive and Successful

For more information about this article, call Bruce Ratner at 516.791.3544 or 1 800 DM STAT-1; or e-mail at br@dmstat1.com.
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