Risk Management

Risk management can mean many different things. The objectives of risk management is to control factors that may lead to risk being materialized. Risk management is especially important for financial institutions providing loans, mortgages, and finances, and insurance companies providing insurance coverage. For finance and insurance, analyzing past historical data may reveal factors that may constitute risk.

Risk Analysis and Risk Modeling Methods

In this page, the following risk analysis and risk modeling techniques are described. More details are discussed in the subsequent sections;

• Risk factor correlation analysis
• Risk factor analysis and risk factor profiling
• Risk modeling and risk predictive modeling

For more detailed discussions, please read;

• Credit Risk Analysis, Scoring and Modeling
• Insurance Risk Analysis, Scoring and Modeling

Risk Factor Relevancy Analysis

How do you know that data you collected are any indicator for predicting risk? In another words, how can you determine that a variable can be a factor for future risk, e.g., credit defaults or insurance claims? The answer is correlation analysis. Correlation is measured between -1 and 1. It indicates the degree of association between two variables. If coefficient is 1, two have perfectly positive correlation. If it’s -1, two have perfectly negative correlation. It it’s 0, two have no association at all. StarProbe correlation link analysis, shown below, is a visualization tool for many-to-many variable correlation analysis. Positive correlations are shown in bright red color, while negative correlations are shown in bight blue color.

Risky Segment Profiling

Profiling risky customer segments is very useful. Customers at risky customer groups may be identified using StarProbe hotspot drill-down tools. It can identify risky customer segments through systematic search using artificial intelligence techniques. It can search profiles of risky segment in terms of risk probability, average risk amount, and so on. For example, assume an insurance company keeps records on motor vehicle insurance information in its database containing driver and vehicle information: Gender, age, license experience, education, occupation, drinking, smoking, mobile phone use; vehicle manufacturer, type, model, year make, and so on. The company wishes to know which motor vehicle insurance is at the highest risk groups or highest average insurance payouts. The following is a possible output of hotspot profiling analysis;

Risk Modeling

Risk modeling refers to the use of predictive modeling techniques to determine the risk level of financial portfolios. Different modeling tasks require different risk modeling techniques. When abundant past historical data is available, predictive modeling techniques, such as neural network and decision tree, can be applied. For modeling techniques, please read;

• Credit and Finance Risk Modeling Methods
• Insurance Risk Modeling Methods

Risk Predictive Modeling and Tools

StarProbe data miner supports powerful easy-to-use risk predictive modeling tools. Users can build models with the help of intuitive model visualization tools. Application of models is very easy. Users can apply models directly to user data using built-in database interface tools. StarProbe data miner comes with the following predictive modeling software programs;

• Neural Network
  Neural network is a predictive model which is based on the architecture of the, say, our brains. It can be used for classification as well as for value prediction. It generally offers most accurate and versatile models. It's very easy to develop neural network predictive models with StarProbe. Network visualization tools will guide users from configuration, training, testing, and more importantly direct application to databases.

• Decision Tree Classifier
  StarProbe supports not only all major Decision Tree methods, but also Cramer Decision Tree exclusively. In general, Cramer tree produces best trees. It tends to produce decision trees with smaller numbers of nodes. Note that such trees tend to use more general descriptors and work better with actual data. StarProbe provides powerful tree visualization and analytic tools.

• Rule Induction classification
  Rule induction is based on hotspot analysis and profiling techniques. It builds predictive models based on profiles of hotspots. This is a technique that you will be very interested! It determines risk levels based on risk factor profiles. Note that rule induction is exclusively supported in StarProbe.

• Regression
  Compared to above methods, regression can be very limiting and inflexible, since all categorical information should be encoded into numerical variables. With Rule-based Modeling Environment, however, regression can be useful in combining influential key factors and modeling for special segments and cases.

New way of deploying risk models for risk managers

Conventionally, risk models are made available to risk managers using purposely-written programs, normally desktop programs with graphical user interfaces (GUI). However, developing fully-featured GUI programs is very costly and time consuming. In addition, it is very difficult to manage distribution. Risk models should be revised to reflex changing business environment periodically. Model upgrade is another problematic area.

StarProbe Rule-based Modeling Environment and Web Service Kit provides a robust environment for deploying sophisticated risk management models over the web in a simple manner without any programming effort. Risk managers can access models using web browsers, as shown in the following figure. Deploying models over StarProbe RME Webkit is very simple and easy. More importantly, in a secure manner, centrally controlled. Model upgrade can happen instantaneously to all users.

Harness your risk management products with Embedded intelligence!

StarProbe Data Miner and Rule-based Modeling Environment provides ideal end-to-end solutions for developing and deploying intelligent systems. It provides robust modeling power incorporating rules, formulas, and predictive models. It is based on component technology and can be easily integrated to your risk management software using program API calls, SOA, SOAP, Web Services, Java/J2EE, Servlets, JSPs, XML, etc. Essential technology for systems integrators, outsourcing companies, and service providers! For more, please Contact Us.