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
  • Realtime expert risk advisors

For more detailed discussions, please read;

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We have consulting and implementation firms/teams in your neighborhood, including authors of well-known books, with proven track records from major financial institutions. We provide end-to-end solutions from anaysis to enterprise-wide implementation. For more information, please write to us.

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.

risk factor correlation analysis.

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;

insurance risk profiling.

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;

If you are interested in developing credit/insuarnce scoring models by yourself, please write to us. We will send you step-by-step "Modeling Guide to Credit and Insurance Scoring". It details the procedures described here: data preparation, variable relevancy analysis, hotspot and exception analysis, predictive decision tree probability modeling, neural network modeling, model validation, and rule-based model integration.

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;

  • 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 CMSR. Network visualization tools will guide users from configuration, training, testing, and more importantly direct application to databases.

  • Decision Tree Classifier
    CMSR 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. CMSR provides powerful tree visualization and analytic tools.

  • 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.

Decision tree classification predictive modeling. Neural network predictive modeling.

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;

  • Government regulations.
  • Internal business policies.
  • Common sense rules.
  • Industry professional heuristics.

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.