Solutions

Risk management includes the identification, assessment, and application of measures, in order to minimise, monitor, and control the probability and/or impact of loss events. Eurorisk Systems'risk management solution estimates and manages the main financial risk types: Market Risk, Credit Risk and Operational Risk, which have the following risk distribution and the corresponding risk capital allocation:

• Market Risk - 15%
• Credit Risk - 50%
• Operational Risk - 35%

After the economic crisis in 2008, additional risk types include Liquidity Risks of Assets as well as Spread Risks of Assets and Derivates.

Architecture

Eurorisk Systems' solution for risk management uses technological, human and organizational resources and follow international principles and standards (ISO 31000). Further, it can estimate risks in non-financial areas, since these risks can be expressed in terms of financial risks. For example, the risk of price changes (e.g. in materials, services, etc.) is actually market risk, similarly to the risk of changes in share prices. The solution consists of the following main modules for Value at Risk (VaR) calculation:

• Calculation of Market VaR via Monte Carlo Simulation;
• Calculation of Credit VaR via Monte Carlo Simulation;
• Calculation of Operational VaR via Monte Carlo Simulation.

A set of supporting modules is used to produce calculation data from primary market data:

• Rating tool box produces ratings.
• PD and matrix calculator calculates rating-based default and migration probabilities.
• Portfolio management constructs portfolios and calculate portfolio positions.
• Loss Given Default (LGD) simulator produces data in case of exposure defaults.
• Operation risk module simulates operational risk losses.
• Market risk module simulates and aggregates market risk along an instrument specific list of market factors.
• Reporting modules produces OLAP & Standard reports for screen, printer and export files.
• Model settings configure scoring and rating models.
• Models for validations (GINI Diagram) and analysis of scoring and rating models using neuronal networks.

Risk Evaluation Data Flow

Data flow ensures the calculation and simulation of various risks, as well as the preparation of primary data, that comes from evaluation environments outside the system:

• Rating levels are calculated for every issuer, based on hard facts (balance sheet data) and on soft facts (a set of parameters and classifiers). In addition, rating levels can also be imported from external sources. Produced ratings are assigned to issuers and are used in subsequent risk evaluations.
• The scoring and rating models that are used are validated and analysed by neural networks in order to reduce the relevant factors and to optimise the rating structure.
• Probabilities of default (PDs), migration probabilities (MPs) and migration matrix are generated based on historical time series of rating migrations or from historical statistics of cumulative PDs. PDs, MPs and migration matrixes can also be imported from external agencies. PDs and MPs are used to simulate credit risk loss distribution.
• The Portfolio Management Module displays the risk evaluation of a portfolio subject and rolls out its cash flows. Exposure at default (EAD) is calculated based on market data.
• The Loss Given Default (LGD) simulator models the specific LGD exposure, based on historical loss data, as well as on collaterals assigned to the exposure. In the default case, LGD is then used in the credit risk evaluation process, to account for recovery and for risk absorption by collaterals.
• The Operation Risk Module receives key factor distributions from the Monte Carlo simulation and simulates operational risk losses within an institution’s hierarchical structure. Key Factor distributions are mapped to theoretical distributions, using primary information from the historical loss data base and the self-assessment module.
• The Market Risk Module operates on fast simulation trees of portfolio positions. Market factors for the Monte Carlo simulation are obtained from historical time series and their statistic features are held in market risk correlation matrixes and volatility vectors. This module simulates and aggregates market risk along instrument specific lists of market factors, on position and portfolio level.
• All risk results and figures are stored in standard report database tables, that are then reported via reporting modules, producing OLAP and Standard reports and exports.

Functionality

Solvency II Coverage

The current Solvency II Module includes Market, Default, and Life Underwriting Risk as well as the calculation of BSCR (Basic Solvency Capital Requirements) as well as BCR (Basic Solvency Capital Requirements). Тhe calculated BSCR is adjusted for loss absorbency, using the equivalent scenario or the modular approach for each risk category. Solvency Capital for Operational Risk is considered in the calculation of Overall Solvency Capital Requirement (SCR) and Minimum Solvency Capital Requirement (MCR).

Rating and Scoring Evaluation

In the credit risk calculation, rating estimations act as measurements for the ability to serve loans and other debt transactions. Risk Framework' solution for estimation of private and corporate ratings is based on balance sheet data and on soft factors (see above). Country specific factors and criteria can be defined to consider particular economical environment conditions. Scoring results can be mapped to rating levels, using unified master rating scales. Certain rating levels are determined by early warning signals and corresponding K.O.-criteria. Official external information about counterparties can be included. Scoring models evaluate the quality of the applied collateral, as the relation between planed debt redemption and debtor incomes. The quality of the produced rating and scoring is enhanced by adjusting rating and scoring models according to historical losses of a debtor pool, or produced ratings and PDs of the rating and scoring modules.

Advanced Measurement Approach (AMA) for Operational Risk

It is calculated via the Monte Carlo Simulation, using the Copula approach on non-normal distributions. Simulation distributions for severity and frequency event type are adjusted for every loss, based on the economic capital. This capital is calculated either according to those loss event types or according to a historical loss database that accumulates internal or external loss data for the previous 5 years.

Interfaces and Connectors

The Risk Management Module inherits the features of Risk Framework Interfaces and Connectors and Risk Engine Interfaces and Connectors.