|Rosella Predictive Knowledge & Data Mining|
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;
For more detailed discussions, please read;
Software downloads: Evaluation copy of CMSR modeling and drill-down segmentation analysis is available from CMSR Downloads.
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 itís -1, two have perfectly negative correlation. It itís 0, two have no association at all. CMSR 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 CMSR 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 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;
Risk Predictive Modeling and Tools
CMSR 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. CMSR comes with the following predictive modeling software programs;
Rule-based Expert Risk Advisors
Predictive modeling is based on past statistical evidences. If there is not enough evidence, predictive modeling can fail to predict reliably. In general, most of high risk applications are filtered manually by various regulations and policies. Statistical evidence for high risk applications are not available in historical data. Therefore predictive modeling cannot be used as the sole solutions. Risk management systems must deal with the followings as well;
Rule-based modeling is a very powerful method that can combine the bests of the knowledge of experienced human experts and the power of predictive modeling. It is ideally suited for risk management tasks. Rosella BI Platform provides two rule-based modeling engines: RME and RME-EP. Both are based on SQL-like rule specification languages. They are very powerful languages incorporating predictive models along with logical expressions and mathematical formulas. RME is a procedural language. RME-EP is for rule-based expert systems. Together they serve as very powerful engines for risk modeling. For more, please read Expert System Rule Engines.
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.
Rosella BI Server provides a robust environment for deploying sophisticated risk management models over the web in a simple manner without serious programming effort. Risk managers can access models using web browsers. Deploying models over Rosella BI Server 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!
CMSR 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.