Solvency II IT Impacts


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Getting insights for IT people working or willing to work in Solvency II initiative

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  • De mesurer très précisément le risque via des analyses des séries et économétriques grâce aux capacités de modélisation statistiques les plus performantes du marché. De fournir des résultats rapidement, et d’appliquer une approche consistante qui peut être élaborée pour gérer des milliers de variables tout en produisant des résultats cohérents. De calculer des mesures at-risk pour faciliter la prise de décisions contribuant à la création de valeur pour les actionnaires. De réaliser des analyses dans un temps donné et de délivrer des résultats faciles à exploiter. D’évaluer les biens financiers et physiques de l’entreprise et leur degré d’exposition au risque. De permettre aux gestionnaires du risque, aux managers et aux analystes d’accéder et de communiquer les mesures de gestion du risque établies au sein de l’entreprise, participant ainsi à une meilleure prise de décision pour que chacun saisisse la vision sans perdre de vue les détails granulaires.
  • Solvency II IT Impacts

    1. 1. Solvency II – IT ImpactsBy Ali BELCAID – Managing Consultant
    2. 2. Context Programme Management : Risk Driven Decision SupportAn illustrative Implementation through SAS
    3. 3. Context Risks borne by insurance companiesMajority of these risksare covered by the Risk model requiredscope of Solvency II Interest RiskDirective Stock Market Risk (prices) FOREX Risk Market risk Operational risk Concentration Volatility Liquidity Risk model required Insurance risk Credit risk Risk model required Economical factors Disasters Creditor/Debtor Risk Reinsurance New Business Concentration Existing Business (Reserves)
    4. 4. Context Risks borne by insurance companiesKnown the diversity of risks borne, Solvency II programme will require an importantmobilisation of the overall skills in your company. Knowing that Solvency encompasses several work streams. It has to be split in two categories • in a context of budget and expense constraints : – Need to adapt the works/response continuously to the evolution of rules, directives, and in particular to your specific needs.Business & – Ability to work in parallel on work streams requiring similar skills with strong interactions,technical and requiring the dedication/allocation of specific expertise. – Successful achievement in adapting the business/operational, technical and financial systems to be able to provide data for an optimal feeding of the risk models. • in order to ensure a satisfying course and the success of the programme : – Control the management of the programme in a timely manner, – Management of the transversal consistency of the work streams, in terms of methodologyOrganisational and technique.(programme – Management of the interactions between Solvency II projects and the other majorsteering) projects in your company. – Involvement and dedication of all the different functions, departments, businesses of the company, beyond the usual technical expertise traditionally involved.In order to properly respond to these challenges, you are supposed to : – Control optimally the risks of the programme and of the arbitrage processes. – Implement an adequate programme steering organisation, based on 2 pillars (the Programme Management and the Work stream leaders).
    5. 5. Context Program Management : Risk Driven Decision SupportAn illustrative Implementation through SAS
    6. 6. Programme management An integrated “risk-driven” approach 1. Risk Identification 2. Target definition 3. Implementation Plan 4. Implementation Project steering Regulatory watch and training Target for each Risk stakes : Insurance risk Work stream major risk strategy / steering - Repository Market risk / Liquidity Work stream - Your Adaptations Scoping/ First impact Launch of measurement Gap analysis by Counterparty risk /Concentration Work stream the major riskprogramme - Control / Process Operational risk Work stream - Data Inventory - Systems Organis./Procedures Systems upgrades - Reporting / Score carding Identification and Internal Models Dashboards Reporting/ classification of Update of impact /Controls risks measurement Action plan by major risk Data Collection & Reliability Work stream
    7. 7. Context Program Management : Risk Driven Decision SupportAn illustrative Implementation through SAS
    8. 8. Decision Support Data High quality and available informations are mandatory in the 3 pillars Pillar 1  The goal is to define quantitative thresholds as well as Pillar 1 technical provisions for equity capital (MCR, Minimum Capital Requirement et SCR, Solvency Capital Requirement) SCR (Solvency Scale of intervention Capital Requirement) Pillar 2  Regulation authority will have the power to check data MCR (Minimum Capital Requirement) quality, estimation procedures, systems in place for measuring and mastering risks in case they occur. It will also be in a position where it can also compel the Risk margin company to have an additional Solvency margin (capital add-on), under certain conditions, in case it considers that risks have been under-estimated. Technical Provisions Pillar 3 Best Estimate  The goal is to define the set of detailed information that (Liabilities) regulation authorities will consider as mandatory. An appropriate decision Support system is the cornerstone of the Solvency II programme
    9. 9. Decision Support Impact on Pillar 1• Pillar 1 completely change the way insurance companies allocate capital.• Pillar 1 handles quantitative assessment of required data for calculating MCR and SCR.  Pillar 1 completely changes the way insurance companies allocate their capital.1. Increase actuarial calculation Indeed, the SCR model ensures the capital is allocated according to the exposure to risks. As a consequence, this will increases the actuarial calculations.  Among others, actuarial calculation relies on the use of referential data (interest rates, mortality rate, etc.).2. Accuracy of data  This calculation requires data coming from heterogeneous systems.  Actuarial calculation means the need for accurate thus heterogeneous data, so there is a need for an enterprise-wide data/information governance.  Actuarial calculation deepening and the need for best quality data will lead to these 2 “must-have” requirements :3. Implication  Availability of best quality data  Analysis and interpretation of data processed
    10. 10. Decision Support Impact on Pillar IIPillar 2 constitutes the governance framework of pillar 1.It handles the quality assessment of data, for which accuracy and availability are critical conditions(cf. Pillar 1).  Monitoring process will ensure data are present at each step with the proper level of1. Monitoring Process detail, of quality, allowing further review or correction, in case of need.  Best quality data, processed in the monitoring process, are essential for the Risk2. Detect, Alert and Act Management system. They help identify risk at any step, and above all, provide the ability to alert Management for doing the right action at the right time.  Pillar 2 changes the risk management and the internal control mechanisms, at enterprise level : it makes them deeper, tighter and more rigorous.  Several enterprise processes are impacted. Here are some:  Budget planning  Capital management3. Implication  Financial Reporting  IFRS Reporting  Marketing Reporting  Etc.
    11. 11. Decision Support Impact on Pillar IIIPillar 3 focus on market discipline.It is about communicating a set of information to the regulatory authority, to shareholders and moregenerally to the public.  Whereas pillars 1 and 2 cover respectively availability of data and its quality, pillar 31. Data Confidentiality ensures its secrecy and confidentiality.  Data exposure to regulatory authorities, shareholders and the public assume the implementation of an efficient and adequate control process on data, thus allowing2. Appropriate monitoring Process to detect any possible anomaly, potentially causing misinterpretation and whose consequences could cause damages to the company (e.g. Share price drop down).  Improvement of data management and data confidentiality procedures.3. Implication  Improvement of control over data flows.  Extension of the use of universal data format (e.g. XBRL).
    12. 12. Decision Support Data Sourcing One of the major challenge that I’m seeing in Solvency II is the availability of data for SCR calculation based on new rules from Pillar 1, on one side, and on the other side the complexity and heterogeneity of IT systems in the insurance sector. Knowing that data comes from different sources, it is important that the feeding and processing mechanisms must be high-quality and efficient enough to satisfy Pillar 1 requirements. Data management system must be able to process data from heterogeneous systems while ensuring data integrity and consistency. This requires a robust data model and a reliable processing system. The data system implemented must meet these 4 requirements:  Quality  Integrity  Reliability  Comprehensiveness The system must be able to process high data volumes, in particular because of the need for historical data.
    13. 13. Decision-making Data Sourcing Solvency II Data SourcingLegacy System 1. Quality Actuarial Reports 2. Integrity 3. Reliability 4. Comprrehensiveness Mainframe “Disclosure” Reports Actuarial System Accounts Capital SCR/MCR allocation Capital allocation ALM Calculation Data Cleasing and Actuarial process Processing Interface (ETL) ALM Risk Risk Risk Reports assessment Management Policies tool Risks Accounts Claim and Liabilities reports Risk Data Systems Capital Management and Claims Allocation
    14. 14. Decision Support Data Sourcing The approach for understanding changes in source data is to analyse them by major risk impacting the SCR calculation. Source data collection should be considered in terms of risk types or major risk. Here are a few examples of risk types:  Health  Repurchase  Fees  Invalidity  Mortality  Longevity  Disasters  Claims / Cancellations  Epidemics / Accumulation  Revision  Interest rates  Equity price risk  Spread  Exchange rates  Real estate  Premiums and provisions  …
    15. 15. Decision-making Migration of the Data System• After ensuring a comprehensive and relevant data « sourcing », target system design should be based on a “Gap analysis” through comparison of the existing system versus the target one. Legacy Data System Data Sourcing• The new system should eliminate inconsistencies, improve sourcing process, storage Gap Analysis Migration toward the new and maintenance of data. Those criteria must be Solvency II data system addressed during system design phase. Data sourcing and maintenance Discrepancies identification• Choice of the central database must be done in IT Users Education accordance with architecture choices and existing architecture (Data Warehouse versus ODS). This subject must be addressed carefully in details because it will determine the target system. The migration should address several subjects, among others:  “As-is” system analysis : this will mainly define the data flows and the data sources.  Data Sourcing and their maintenance : the new system must collect and maintain existing data in an efficient and flexible manner, allowing further integration of newly required data.  Identification of incongruities : at this step, the identification of « gaps » between the existing system and the proposed model must be clarified. Existing data and flows will also be described and categorised, as well as additional ones and the related flows (new). A very early setup of the migration to the new system will allow to identify inconsistencies upstream in the process and, undoubtedly, reduce contingencies and difficulties. Transition to the new Solvency II regulatory framework will then be smoother.
    16. 16. Context Program Management : Risk Driven Decision SupportAn illustrative Implementation through SAS
    17. 17. Warning : SAS part is provided for SAS Solutionillustrative not for Selling purpose Integrated Approach Operational Data Sources  Policy  Underwriting  ClaimsExternal Data 6  ReinsuranceInternal Data  Investment  Commissions  Products  Assets/Liabilities  General Ledger 1 5Data Mart 2 4 3 Standardisation Operational Risk Engine Actuarial Risk Engine Risk Data Warehouse Credit Risk Engine Market Risk Engine
    18. 18. Warning : SAS part is provided for SAS Solutionillustrative not for Selling purpose Alignment to Compliance Claims • Extraction, transform, loading of any kind of data (scoped) from a unique central point. Missing & 2 incorrect data are audited, corrected with high Actuarial value-added automated functionalities like grouping of identical codes, redundancy detection and 1 + 2 + 3 Data removal (contracts, products,…), existing data enrichment. • SAS DDS dictionary is enriched via a metadata server, offering true information on information for ALM the whole decision-making production system, Contracts Accounting (GL) 1 • The approach consists of selecting Existing Risk source data and profile them by major Data risk having potential impact on SCR calculation. SAS DDS for insurances 3 • SAS DDS. A « Ready-to-use » model gathering all required data • This assumes source data collection is foreseen by risk type or major risk. for risks related calculations. • Collects and gathers all dimensions et granularity of data • DDS model, given its extensive coverage, allows to reduce significantly the implementation of Solvency II programme.
    19. 19. Warning : SAS part is provided for Solution SASillustrative not for Selling purpose Re-use of information Risks 3 CRM Marketing Automation SAS DDS for insurances SAS DDS could be used as receptacle for Computed Risk Data and then make it available for other purposes CRM, Marketing, ...
    20. 20. Warning : SAS part is provided for SAS Solutionillustrative not for Selling purpose SCR/MCR Calculations You do not need to throw away your 4 existant cash Flows analysis. So you can inject them directly into the Risk Engine. Existant Cash Flows SAS interfaces SAS Web Portal SAS Add-in for Microsoft SAS DDS for insurances Office The Input Data Mart is purged for each calculation You can analyse data before and after Risk Calculation You can analyze and report on DDS data for audit, intermediary result, … purposes • Risk Data Mart as an « input » includes all data coming from SAS DDS as well as external or modelled data used for risks calculations. • Its main purpose is to define the business scope and thus the content of the input of data required for the risk calculation engine. • It is purged for every new calculation.
    21. 21. Warning : SAS part is provided for SAS Solutionillustrative not for Selling purpose Analysis and Risk simulation Module 4 Main functionalities of SAS Risk calculation engine are : • Measure risk • Restitution of consistent results • Calculate at-risk measures • Achieve analyses in a given time scale. • Assess financial and physical assets of the company and the level of exposure to risks. • Offer risk managers, managers and analysts a performing tool Main characteristics of this engine: • Numerous pricing functions built-in (extensible) • Predefined analyses • Predefined risk models (extensible) • Library of External functions • Allow to build on existing environment
    22. 22. Warning : SAS part is provided for SAS Solutionillustrative not for Selling purpose Models, Analyses and Calculations Calculations and results 4Results of stochastic simulations (VaR) VaR results (with or without reinsurance)
    23. 23. Warning : SAS part is provided for SAS Solutionillustrative not for Selling purpose Restitution Datamart 5 • After simulation of the risk models, it is possible to collect calculated data to deliver them in a restitution datamart, used as a standard model for the reporting. • The analysis can be done with dimensions like (risk type, products, product types, currencies, …) • You can get results of quantitative complex analysis calculations such as SCR, MCR but also all the variables involved in the calculation.
    24. 24. Warning : SAS part is provided for SAS Solutionillustrative not for Selling purpose Reporting (Internal & Regulatory) 6 Analyst : SAS Insurance Risk Studio • A portal offer a synthetic overview allowing the management to know the exposure to the different risks. • Unique entry point for analysis of any kind of indicators, at aggregated or detailed level Management / Analyst : SAS Information Delivery Portal • Solution includes a « Risk Studio » allowing analysts to : • Perform analyses or Portfolios replication • Generate internal or regulatory reports • SCR and MCR calculation • Run « Stress Testing » • …
    25. 25. Warning : SAS part is provided for SAS Solutionillustrative not for Selling purpose Reporting (Internal & Regulatory) (Example of a Solvency II Dashboard) 6 • Example of restitution of a certain number of indicators via a « Solvency II » dashboard • A finer analysis can be performed via the « Drill Down » functionality to analyse details of intermediates or final calculations.
    26. 26.