Solutions

Quantitiative Analysis

In addition to risk management solutions, RiskAnalytica is active in scientific research within Physics, Mathematics and Statistics. This allows us to be up to date on the current state of available methods and tools and be in the position to use the most appropriate solutions (with respect to the presented problem).

RiskAnalytica can provide their clients with a mathematical representation of their problem, providing a solution and its subsequent mathematical and physical interpretation. RiskAnalytica employs a wide range of mathematical methods to produce flexible and dynamical models which are used as part of its professional service capabilities:

• Markov Chain Monte Carlo - A stochastic simulation method which uses state transition probabilities to represent the evolution of a dynamic system. The subsequent states of the system are generated randomly based on an appropriate probability of their occurrence (tested using Kolmogorov-Smirnov tests). This gives rise to a statistical approximation with respect to the exact solution (within the desired accuracy level).
• Sensitivity Analysis - Understanding how sensitive a possible outcome (an effect) is to input and functional assumptions (causes). Various methods which are important in computational physics and bank risk management are employed;
• Decision Tree Analysis - Process maps are used to reflect the conditional, joint and marginal decision dynamics that the decision maker faces, with the incorporation of inherent variability
• Knowledge networking - Organizations form knowledge using several different networks often not reflected by a formal organizational structure. Mapping such networks allows an understanding of how observations are used to form organizational knowledge and action. Knowledge networks are subject to variability in the way observations are gained. This is an important process to understand for sophisticated risk management that requires a low margin of error.
• Data Analysis - Techniques used for analyzing historical or experimental data, allowing determination of the extent that an observed result is connected and/or correlated to input values.
• Forecasting - Techniques used to guide planned activity and responses over time using observation based simulation techniques.
• Linear and Non-Linear Analysis - Techniques used to understand the causal links between the variables, obtain the algorithmic form and to establish a mathematical network representation.