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

1. Solvency II – IT Impacts
By Ali BELCAID – Managing Consultant

2. Context
Programme Management : Risk Driven
Decision Support
An illustrative Implementation through SAS

3. Context
Risks borne by insurance companies
Majority of these risks
are covered by the Risk model required
scope of Solvency II Interest Risk
Directive 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. Context
Risks borne by insurance companies
Known the diversity of risks borne, Solvency II programme will require an important
mobilisation 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 methodology
Organisational and technique.
(programme – Management of the interactions between Solvency II projects and the other major
steering) 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. Context
Program Management : Risk Driven
Decision Support
An illustrative Implementation through SAS

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 risk
programme
- 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. Context
Program Management : Risk Driven
Decision Support
An illustrative Implementation through SAS

8. Decision Support
Data
High quality and available information's 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. 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. Decision Support
Impact on Pillar II
Pillar 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 of
1. 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 Risk
2. 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 management
3. Implication  Financial Reporting
 IFRS Reporting
 Marketing Reporting
 Etc.

11. Decision Support
Impact on Pillar III
Pillar 3 focus on market discipline.
It is about communicating a set of information to the regulatory authority, to shareholders and more
generally to the public.
 Whereas pillars 1 and 2 cover respectively availability of data and its quality, pillar 3
1. 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 allowing
2. 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. 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. Decision-making
Data Sourcing
Solvency II Data Sourcing
Legacy 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. 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. 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. Context
Program Management : Risk Driven
Decision Support
An illustrative Implementation through SAS

17. Warning : SAS part is provided for SAS Solution
illustrative not for Selling purpose Integrated Approach
Operational Data Sources
 Policy
 Underwriting
 Claims
External Data 6  Reinsurance
Internal Data  Investment
 Commissions
 Products
 Assets/Liabilities
 General Ledger
1
5
Data Mart
2
4
3 Standardisation
 Operational Risk Engine
 Actuarial Risk Engine Risk Data Warehouse
 Credit Risk Engine
 Market Risk Engine

18. Warning : SAS part is provided for SAS Solution
illustrative 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. Warning : SAS part is provided for Solution SAS
illustrative 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. Warning : SAS part is provided for SAS Solution
illustrative 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. Warning : SAS part is provided for SAS Solution
illustrative 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. Warning : SAS part is provided for SAS Solution
illustrative not for Selling purpose Models, Analyses and Calculations
Calculations and results 4
Results of stochastic simulations (VaR)
VaR results
(with or
without
reinsurance)

23. Warning : SAS part is provided for SAS Solution
illustrative 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. Warning : SAS part is provided for SAS Solution
illustrative 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. Warning : SAS part is provided for SAS Solution
illustrative 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. http://intelligenteenterprise.blogspot.com/
http://www.linkedin.com/in/albel