Nine Steps Predictive Modeling Guide for Risk Management

Developing and deploying predictive models is not a trivial job. It requires a methodical approach. In this page, nine step quick guides for of predictive model development are described.

1. Data Cleaning and Transformation

The first step to predictive modeling involves data cleaning and transformation. Data may contain bogus values, synonymous values, outliers, etc. Those values need to be standardized and cleaned. In addition, values may need to be transformed into new variables. Note that data is cleaned and transformed from database systems.

2. Import Data into CMSR Data Miner / Machine Learning Studio

Once data is cleaned and transformed, the next step is to import data into CMSR Data Miner / Machine Learning Studio. Imported data will be used for variable analysis and predictive modeling.

3. Variable Relevancy Analysis

Once data is imported into CMSR Data Miner / Machine Learning Studio, variables are analysed to determine whether certain variables contain information that can predict customer tendencies. Categorical bar/histogram charts and correlation analysis tools are used. For more, read Variable Relevancy and Factor Analysis.

4. Predictive Modeling

Once relevant variables are identified, neural network predictive models can be configured and trained using data imported into CMSR Data Miner / Machine Learning Studio. Only those variables identified as relevant are used in modeling. Multiple models with differing configurations may be used. Testing of model accuracy is performed using (historical) data stored in database.

5. Model Integration - Aggregation

Multiple predictive models are integrated as single models using RME-EP (Rule-based Model Evaluation - Event Processing) rule engine. Integration can take various forms such as maximum, minimum, average, etc. Those combined score values are tested using historical data (in database) and analysed.

Histogram of maximum risk scores

6. Model Integration - Classification

Next step model integration may involve classification. Predicted values from neural networks are hard to understand for casual users. Those numbers are translated into more easily understood vocabularies such as “very high risk”, “high risk”, “medium risk”, “low risk”, etc. This classification is done with RME-EP. RME-EP models need to be tested for accuracy with historical data in database.

Proportions of risk classes

7. Preparing Model Documentations

Once models are integrated and tested, end-user model documentations are prepared. Model documentations are HTML files that can include charts relating to models. Model documentation should contain information what end-users will need to know.

8. Deployment on MyDataSay

Models along with model documentations can be deployed on Android devices using MyDataSay Android App. MyDataSay can be served as trial before deploying on Web.

9. Web and Database Integration

Models along with model documentations can be deployed on web using Rosella BI Server on Java/Jakarta EE Application Server. In addition, BI server provides database and other web application integration using JSP programs. For example, predictive information can be integrated into other web-based business applications using HTML IFRAME tags and JSP pages.

Free Test Trial Program

If your organization has data that can be used to develop predictive models, please write us by filling the form CMSR Data Miner / Machine Learning Studio Download Application. We will provide software and email technical support up to a year free. You will also receive "Predictive Modeling Guide to Credit and Insurance Risk Scoring" ebooks. If you are unsure of how predictive models can be used, please try MyDataSay Android App.