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Risk Management and Risk Predictive Modeling

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 coverages. For finance and insurance, analyzing past historical data may reveal factors that may constitute risk. In addition, predictive models using machine learning algorithms can be developed.

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 detailed discussions, please read;

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? An 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.

Factor Analysis

Risky Segments 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 segments 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 Predictive Modeling

Risk preditive 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 based on machine learning, such as neural network (as shown in the following figure) and decision tree, can be applied.

Neural network predictive model.

For risk modeling techniques, please read;