Solvency II Group Models Capital Allocation and Diversification Thomas C. Wilson Chief Risk Officer, Allianz SE

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1 Solvency II Group Models Capital Allocation and Diversification Thomas C. Wilson Chief Risk Officer, Allianz SE GDV Solvency II Conference Berlin,

AZ supports the direction and progress of Solvency II, besides some pending issues: 1 Group Key Issue Supervision Group Supervision Allianz Position One single point of contact for Group issues;

valuation MCR and SCR should be risk-based; MCR as percentage 3 Consistent MCR & ~ SCR framework of SCR; allow for risk mitigation.

2 AZ supports the direction and progress of Solvency II, besides some pending issues: 1 Group Key Issue Supervision Group Supervision Allianz Position One single point of contact for Group issues; Local Supervisors have to support the Group Supervisor; Need for cooperation and coordination. Status ~ Market consistent Total balance sheet approach, market value where 2 possible, BE + MVM. valuation MCR and SCR should be risk-based; MCR as percentage 3 Consistent MCR & ~ SCR framework of SCR; allow for risk mitigation. Right incentive for using internal models; Efficient validation process via Group Supervisor; Use test full buy-in from management needed. 4 Internal Models ~ Harmonized requirements throughout Europe 5 Harmonization (reporting / application of tools). Starting point for disclosure consolidated account; 6 Disclosure No disclosure of sensitive information. 2

3 Why is Allianz committed to meeting Solvency II? It s Expected As the largest European insurance group, we have to fulfill SII internal model requirements regulators and rating agencies expect it; High external expectations regarding well functioning enterprise-wide risk management. We Want To! Significant decision support benefits: What-if and movement analysis: market movements, positions; Asset management support; Risk transparency, etc.; 15-25% reduction in minimum solvency capital requirements relative to the standard model, potentially increasing our ROE. 3

4 AZ internal framework: Best Practice models within / across risks Best-in-class aggregation model Conservative correlation assumptions across risk types Monte Carlo simulation based Risk Aggregation ( Gaussian Copula based on conservative correlations) Financial risk Credit & transfer risk Insurance risk Operational risk Business risk For each risk: Best-in-class models Integrated asset models / replicated liabilities MKMV port manager, submitted for Basel II advanced Actuarial models for each risk, loss functions / Loss scenario files Internal and external data Loss functions calibrated to historical data Best estimate correlations within risk class Historical vol. & correlations, 5 yr weekly data MKMV correlations Management correlation assumptions Management correlation assumptions Management correlation assumptions 4

5 Overview of Aggregation of Risk (for 2 different risk types) 1. Random Numbers 2. Linking of risks 3. Aggregating of risks Generate two independent random numbers between 0 and 1: Identify inverse cumulative distributions for the two risk types: To get joined scenarios: Result of linking a standard normal distribution and a lognormal distribution (0,1) via the Gauss copula Uniform independent [0,1] distributed random variables Inverse cumulative standard normal distribution Inverse cumulative Lognormal (0,1) Distribution , , , , , , ,0 0,2 0,4 0,6 0,8 1,0 1,2 6, ,6-1 4, ,4 0, ,2 0,4 0,6 0,8 1 1,2 Determine the correlation between the two risk types: e.g. 0.9 Generate out of these information two correlated normal distributed samples: 1,2 1 Quantiles of random variables after Gauss Copula transformation , ,000 0,200 0,400 0,600 0,800 1,000 1,200 e.g. market risk e.g. credit risk Link the risks via the correlation information (scenario by scenario): Inverse cumulative standard normal distribution 0,0 0,2 0,4 0,6 0,8 1,0 1,2 1,2 1 0,8 0,6 0,4 0,2 0 2,00000 Quantiles of random variables after Gauss Copula transformation 0 0,2 0,4 0,6 0,8 1 1, (e.g. value-value distribution) Aggregate these scenarios to a multi-point-representation of loss distributions: 0,8 Inverse cumulative Lognormal (0,1) Distribution 0,6 14, , ,4 10, ,2 8, , ,2 0,4 0,6 0,8 1 1,2 4, , , ,000 0,200 0,400 0,600 0,800 1,000 1,200 5

Meeting Solvency II Internal model requirements Objectives: Internal Model qualification in the first year, at both the Group level as well as for all European OEs (with the possible exception of

6 Meeting Solvency II Internal model requirements Objectives: Internal Model qualification in the first year, at both the Group level as well as for all European OEs (with the possible exception of operational risk). Translates into 100% OE coverage for European Economic Area and approx. 95% for Group Make fundamental improvements to our reporting processes, increasing efficiency, flexibility and controls, leading to increased frequency within financial closing. Approach: Leverage off-the-shelf, vended IT solutions, selecting the best possible application for each specific role, and integrating them into a coherent Business Application Architecture. Operating Entities to take more responsibility for decentralized modeling, especially for insurance life and non-life stochastic cash-flow modeling, to better match local product and market characteristics, accompanied by increased responsibility effective local model validation and process controls to ensure the quality and integrity of the numbers. 6

7 Solvency II Projects Risk Aggregation Infrastructure Individual risk systems: -PRISM (P&C) -MKMV (Credit) -ALIM (Life) -ORM Process & Control Project Market data: valuation rates, vols, correlations; Scenario generators: real-world, risk-neutral; Monte Carlo risk-aggregation; On-line reporting and ad-hoc scenario analysis allowing OE use; Well defined interface for feeder systems, e.g. ALIM, MKMV, RUN/RIO, etc., and reconciliation interface to SAP in the future; Including replicating portfolios for life insurance market risk. P&C insurance risk model: extensions of currently used tools for modeling reserve / premium risk; Move all credit portfolio modeling to MKMV platform; improve data quality; Extend ALIM* to generic UL** / UL + guarantees; Ensure that ALIM can meet Risk Infrastructure interface requirements; Ensure loss data collection quality for ORM; Implement platform for RCSA***, KRI**** and capital modeling. Define process and control guidelines (and later Minimum Standards) at OE level for input into internal models; End User Computing (EUC) Change controls for parameters, models; Model validation, etc. *ALIM Asset Liability Interaction Model, **UL - Unit linked insurance * ***RCSA Risk and Control Self Assessment, ****KRI Key Risk Indicators 7

8 Overview of Risk Modeling Landscape Central Risk Platform Algorithmics Market data MinD (DreBa) Valuation, Risk Capital, Scenarios, Sensitivities Risk Engine: MC-Simulation - Inter / Intra Risk-Aggregation Model - Market Value - Balance Sheet - Available Capital - Limits - Hierarchies - Tax calculation - Minorities Market Risk Replicating portfolio tool/ Greeks Web-based User Interface Insur. Risk Marginal dist. /Parameters Credit Risk Marginal dist. /Parameters Oper. Risk Marginal dist. /Parameters Cost Risk Marginal dist. /Parameters Local Systems Feeder Systems Replicating Scenarios ALIM CF Models Life/P&C Asset Input PRISM incl. Nat Cat MKMV Investment Data System OR System Business Risk Partially locally developed and parameterized models Centrally developed, parameterized and controlled models Centrally developed, locally parameterized models 8

9 Allianz SE Example Diversification effect 53% 54% 53% 54% 9

Allocation and management process Input Input Board OE Board limits OE divisional / channel plans Group Strategic Planning Regular risk reports Ad hoc reports / analysis OE product data InCo Output

10 Allocation and management process Input Input Board OE Board limits OE divisional / channel plans Group Strategic Planning Regular risk reports Ad hoc reports / analysis OE product data InCo Output Limit setting Limit monitoring Output FiCo Overall limits Sub-limit authorities RiCo Requests for further investigation Input Reporting (through risk function) Limit revision Limit change requests Output Group Risk OE Board reports Committee Output Group reports Other reports Limit amendments / mitigation actions 10

11 Balance Sheet Management Challenges ahead: Capture diversification benefit which might impact P/L and EVA performance measurement and reward have to be aligned; Optimize regulatory capital leads to impact on: -Tax; - Accounting; - Local regulatory and rating agency metrics; Combined effort for setting out a new performance paradigm, calculate diversification benefit and manage legal entity balance sheet structure is necessary. Meet related PII requirements: Supervisory Review Process (SRP); Own Risk & Solvency Assessment (ORSA); Use Test: Economic Capital - Linked to strategic planning; - Used in EVA performance measurement, incentive compensation; - Linked to MCEV and reserve setting processes; - Influences asset allocation, directly and through replicating portfolios; Calibration, validation, documentation and model change policies. 11

12 Questions

Solvency Management in Life Insurance The company s perspective

Group Risk IAA Seminar 19 April 2007, Mexico City Uncertainty Exposure Solvency Management in Life Insurance The company s perspective Agenda 1. Key elements of Allianz Risk Management framework 2. Drawbacks