Upload
others
View
4
Download
0
Embed Size (px)
Citation preview
INSURANCEJULY 2014
Automating the Solvency Capital Requirement Calculation ProcessOverview
This Whitepaper explores how the Solvency II Solvency Capital Requirement (SCR) calculation process can be automated to facilitate efficient and timely regulatory reporting. The SCR calculation process is complex, requiring significant data consolidation, cleansing and transformation to produce accurate and consistent results. Insurers must calculate the SCR both on a quarterly and annual basis; however their current manual, spreadsheet-based processes are often unable to manage the increased scale and scope of the reporting requirements. Consequently many insurers are exploring how best to automate their SCR calculation process.
WHITEPAPER
Author
Brian Heale,Senior Director, Business Development Officer
Alexis Bailly, Director, Sales
Contact Us
Asia (Excluding Japan) [email protected]
2 JULY 2014 WHITEPAPER: AUTOMATING THE SOLVENCY CAPITAL REQUIREMENT CALCULATION PROCESS
Introduction
The Solvency II regime requires insurers to submit more reports to their regulators, in greater detail, than ever before. These enhanced requirements place significant pressure on constrained internal resources from the actuarial, finance, investment and risk departments.
This paper examines the different streams of the SCR calculation process that are resource-intensive, and suggests technology that can help insurers reduce the calculation of the SCR from months to days. It also highlights how an efficient, automated solution can provide wider benefits to the business. These benefits include improvements to overall actuarial, asset and liability modeling, dynamic financial analysis and wider enterprise risk management processes.
The Challenge: Consolidating Data and Calculating Results
The calculation of the SCR is highly complex. Insurers must evaluate the main risks they carry, determining the level of capital that must be held to meet their liabilities over the following 12 months with a probability of at least 99.5%. The following diagram shows a simplified view of the components of the SCR calculation. The capital for each individual risk factor must be calculated for every type of product, correlated, and then aggregated.
Mortality
Correlation Matrices
BSCR* Operational
Risk Adjustment
Loss Absorbing Capability of
Technical Provisions SCR Deferred
Taxes
SCR Market
SCR Health
SCR Counterparty
SCR Life
SCR Non-life
Correlation Matrix
Correlation Matrix
Correlation Matrix
Correlation Matrix
Expense
CAT
Revision
Morbidity
Longevity
Lapse CAT
Premium & Reserve
Lapse
Longevity
SLT** Health
Mortality
Interest Rate
Equity
Property
Spread
Currency
Concentration
CCP
EIOPA Capital Components &
Shocks
Morbidity
Revision
Expense
Intangible
Lapse
Non- SLT Health
Premium & ` Reserve
CAT
* BSCR – Basic Solvency Capital Requirement
** SLT – Similar to Life Techniques
Many insurers use spreadsheets and manual processes to calculate their SCR numbers for QIS 5 or for internal testing purposes. This approach is effective for calculating results on a one-off basis, however in the longer term, it creates issues around repeatability, auditability and accuracy. Manual processes are inherently error-prone and lacking effective management and audit control. There is also the issue of whether mundane, repetitive tasks are the best use of valuable internal resources.
INSURANCEMOODY’S ANALYTICS
3 JULY 2014 WHITEPAPER: AUTOMATING THE SOLVENCY CAPITAL REQUIREMENT CALCULATION PROCESS
Each of the European Insurance and Occupation Insurance Authority (EIOPA) capital and shock calculations requires a significant amount of risk, finance, actuarial and asset data. The data is extracted from many back-end systems, and then transformed into a specific format, ready for use in the calculation process. While this process appears straightforward, there are several complexities that insurers need to consider:
» Group and Solo ResultsThe SCR calculation applies at solo entity and group levels. Each solo entity has to calculate its SCR individually. The group entity then aggregates all its solo SCR results, and incorporates them with its own, if it also transacts insurance business. Each solo entity will report to its local regulator, as well as providing data to its group headquarters, which must in turn report its consolidated results to its own regulator. The enhanced reporting requirement leads to a greater volume of reports that must be prepared and submitted. These reports need to be accurate, consistent and submitted in the correct format for each national regulator. The group entity must also address issues such as equity elimination and intra-group transactions. Ensuring the accuracy and consistency of results across the business to the levels demanded by Solvency II, with largely manual processes and resources would challenge the best organized insurer. The enhanced scale and granularity of reporting demanded by regulators is a significant driver for automation.
» Different Data ModelsThe sheer scale and scope of modern insurers means that data is widely dispersed across a range of source systems – actuarial, finance, investment and risk systems, for example. These systems will encompass multiple databases, with multiple formats, and spreadsheets, as well as other systems. Extracting, transforming and validating this data manually is no longer sustainable even at the small scale. Automation, in one form, or another is essential.
» Credit RatingsCredit Ratings are an integral part in the calculations of the spread and default risk capital charges in relation to the SCR. Under the Standard Formula, the spread risk capital charge calculation depends on Credit Quality Steps (CQS). EIOPA has defined a mapping table to associate the ratings of external Credit Rating Agencies to Credit Quality Steps and their respective capital charges:
Credit Quality Step 0 1 2 3 4 5 6
Moody’s Rating Aaa Aa A Baa Ba Lower than Ba, unrated
Other Rating Providers AAA AA A BBB BB Lower than BB, unrated
Capital Charge 0.9% 1.1% 1.4% 2.5% 4.5% 7.5%
In cases where several ratings are available for a given credit exposure, the second-best rating should be applied. To avoid mechanistic reliance on external ratings EIOPA proposes that “if an item is part of the larger or more complex exposures of the insurance or reinsurance undertaking, the undertaking shall have its own internal credit assessment of the item and allocate it to one of the seven steps in a credit quality assessment scale (‘reassessment’)”.
In order to automate the SCR process credit risk ratings can be accessed via secure electronic data feeds, which can be fed systematically as an input into internal models. This reduces both manual intervention and operational risk.
» Actuarial DataThe SCR calculation is heavily dependent on the output of actuarial modeling engines for the SCR calculation. For life risks, the technical provisions are typically generated from an insurer’s Asset and Liability models (ALM). For non-life risks, Dynamic Financial Analysis (DFA) models are typically used.
INSURANCEMOODY’S ANALYTICS
4 JULY 2014 WHITEPAPER: AUTOMATING THE SOLVENCY CAPITAL REQUIREMENT CALCULATION PROCESS
This approach presents two key challenges;
» Generating the ALM/DFA results requires inputs such as policy data, claims data, assumption tables, and run parameters as illustrated in the following diagram. If these data sets are flawed, the SCR results will necessarily be inaccurate.
» Many insurers will have multiple ALM/DFA models, covering their various product lines, countries and commercial entities. These models will leverage many actuarial processes, assumptions, systems and outputs. Combining these complex models into a single SCR calculation engine, without losing any accuracy, consistency and relevance is a non-trivial challenge for any insurer.
The following diagram illustrates the various types of input data required to be fed into an actuarial model at run time:
Actuarial Engines
ALM & DFA Models
1. Policy/Claims Data
3. Assumption Sets
4. Run Parameters
5. ESG/CAT Model Files
2. Granular Asset Data
Administration Engines/ General Ledgers
Spreadsheets from Actuaries – potentially
hundreds
Spreadsheets/ManualInput from Actuaries
From Scenario Providers –
e.g. Moody’s Analytics, AIR
From External/Fund Managers Internal Investment Systems
Direct ETL, CSV files Spreadsheets
Direct ETL, CSV files Spreadsheets
Spreadsheets
Spreadsheets
SpreadsheetsCSV Files etc
» Managing External Data SourcesThe SCR results will likely leverage data sourced from external fund managers. Capturing, consolidating, cleansing and formatting this data is a significant challenge, best suited to automation. The need to assess investment concentration risk is an additional driver for automation.
» The Ending of Solvency II Transition ArrangementsAs the deadline for Solvency II approaches, EIOPA is removing many transitional features available in the quantitative reporting templates (QRT), such as the helper tabs. Many insurers currently use these tabs as a short- cut to calculate the results, as they become familiar with the requirements. Replacing this functionality with manual processes will prove exceptionally difficult, again driving insurers towards greater automation of the SCR calculation process to deliver accurate and consistent results.
» Quarterly Reporting CyclesTo date, the QIS exercises and dry runs have been based on producing annual SCR numbers, but the move into quarterly reporting creates issues. Clearly, producing the calculations four times a year is an issue. More importantly, using annual data for a quarterly purpose may create anomalies such as contract boundaries. Thus care must be taken in translating annual data to quarterly data
INSURANCEMOODY’S ANALYTICS
5 JULY 2014 WHITEPAPER: AUTOMATING THE SOLVENCY CAPITAL REQUIREMENT CALCULATION PROCESS
The Solution - Leveraging Automation to Deliver Accurate and Consistent Results
The scale and scope of change in Solvency II drives insurers to automate Solvency II compliance, which in turn enables insurers to deliver the SCR results accurately and cost-effectively. Many aspects of automated Solvency II compliance also drive clear business value, including enhanced management insight that supports better-informed management decision making.
Automation in the context of the SCR calculation encompasses several aspects:
Automating the ALM/DFA Production Process
It is critical to automate the ALM or DFA model run to produce the technical provisions aspects of the SCR process, like cash flow or loss triangle results. This process is part of the SCR process workflow, which, in turn, is based on an automated Solvency II reporting calendar.
Feeding the Data into the SCR Calculation Process
The SCR calculation process requires data from multiple sources to be loaded into the SCR calculation engine in a defined format. The quality of this data must be checked and validated to ensure accurate and consistent results. In addition, there must be full audit trails for compliance and governance purposes.
Driving the SCR Calculation Process
The SCR calculation process is a series of steps, executed in a specific order. The combination of data, algorithms and shocks must be fully maintained to reflect the reality of the business and regulatory developments.
Report Generation A significant challenge of Solvency II and SCR reporting is the complex and diverse reporting requirements of national regulators. While EIOPA drives the requirements of national regulators using the QRTs, each regulator is free to interpret the implementation of the EIOPA requirements. As a result, insurers often face complex reporting regimes incorporating a variety of national requirements. Automating the process that populates data into reporting templates per each regulator’s requirements will ensure a high level of accuracy and consistency. Automating the reporting process allows insurers to leverage workflow approval processes. Managers are able to review, amend, approve and audit changes to the SCR results before they are submitted to the regulator. This process improves managers’ confidence, allowing them to apply expert judgment to their reports.
Solvency II Reporting Calendar
In order to drive the SCR calculation and Solvency II reporting processes, an insurer must develop a Solvency II calendar which breaks down the process into tasks, time steps and responsibilities. These elements, in turn, must be embedded in workflow technology to drive and monitor the process.
INSURANCEMOODY’S ANALYTICS
6 JULY 2014 WHITEPAPER: AUTOMATING THE SOLVENCY CAPITAL REQUIREMENT CALCULATION PROCESS
Leverage Technology to deliver SCR Automation.
Different firms will select different technology solutions to achieve Solvency II compliance. However there are some key components that must be implemented and integrated into the existing infrastructure to help deliver SCR results and Solvency II compliance.
ALM
/DFA
Models
Actuarial Engines
Model Points
ESG Files
Assumption Sets
Policy Claims/Data
SCR Reporting
Workflow Definition & Task Automation
Market Risk
Life
Non-Life
Health
Credit Risk
Aggregation
SCR
Calculation
Analytical Repository
Analytical Repository
Investment Data
Finance Data
Actuarial Data
Risk Data
Data Q
uality and Validation Checks
Data M
anagement Suite (ETL tools)
Claims Data
Risk Data
Market Data
Financial Data
Policy Data
Investment Data
1
2
3
4 5
6
Access Rights
Security
Performance
Technology Platform
7
An optimal model for SCR results generation and Solvency II compliance
A solution for SCR results calculation and Solvency II compliance must include these key components:
1. Powerful Data Management ToolsThese tools automate the data extraction process; transform data into a standardized format, and then load it into the actuarial engines and the SCR calculation engine. Data quality and validation rules should be applied to identify and correct data quality issues that can compromise results later in the process.
2. A Centralized Data Repository A single, centralized data source is key to accurate and consistent SCR results and regulatory reports. It ensures that all the data required to calculate the results can be cleansed, validated and audited. As well as enhancing accuracy, this approach is the least disruptive to the business, as it leverages data from existing back-end systems. Developing a compliance system that runs in parallel to the existing environment reduces the impact and risk of introducing new systems and processes. This consolidated data approach also enables enhanced audit and change control functionality that assures the integrity of the final results.
3. Automated Workflow ProcessesCentral to automation are effective workflow capabilities that drive and monitor the end-to-end data consolidation, calculation and reporting processes. This workflow process can drive a Solvency II calendar for all calculations and reporting with defined tasks, timelines and dependencies. This approach can help streamline the complexities of SCR calculations and Solvency II reporting.
INSURANCEMOODY’S ANALYTICS
7 JULY 2014 WHITEPAPER: AUTOMATING THE SOLVENCY CAPITAL REQUIREMENT CALCULATION PROCESS
The following diagram is an example of a simplified workflow. It covers production of technical provisions QRT, starting six weeks before submission. Similar processes can be developed for all aspects of QRTs including group consolidation.
Technical Provisions (TP) QRT Process Week
1 Week
2
Week 3
Week 4
Week 5
Week 6
Run EIOPA Validation
Checks
Sign-Off
Generate XBRL
Submit to Regulator
Approve
Load Data & Assumption
Files
ALM/DFA Models
Approve Input Data
Review Results
Map Data For TP
QRTs
Approve Results
Run Models
Results
Run Quality Checks
Load to QRT Engine
Load to SII Data Mart
Generate TP QRT
Templates
SCR Calculation
Process
Technical Provisions
Initiate Process
Process Complete
Integrate with other QRT Templates
Correct Errors
4. Actuarial Calculation EnginesThese engines are used to provide the liability elements of the SCR calculation. To maximize the value of full SCR automation, the actuarial modeling process itself should be automated as shown in the following example.
Sample - Actuarial Modeling Process
Step 5 Results
Step 4 Model Run
Load to Repository
Step 3 Modelling Data
Sets
Load /Upload to Repository
Step 2 Policy Data
Load/Upload to Repository
Step 6 Reports
Step 1 Model
Construction
Load to Repository
Load to Repository
Load to Repository
Model Template
Model Build/ Customization
Model Test
Model Sign-Off
ModelDocumentation
ETL direct from Policy/Claims
Systems
Manual extracts from Policy/Claims
Systems
Enhancement of Data
Sign-off of Product Data Set/ Model
Points
Model Points
Specify policydata sub-set
Mortality Tables
Assumption Files
Yield Tables
Model RunParameters
Analysis & Aggregation
Collate Model Runs
Results Files
Load Model, Assumptions
Policy, Modelling Data Sets
Schedule Run
Run Model(s)
Model Output
Run Report
Specify Report
Report
Model Outputs
Input to Economic Capital Aggregator
Model Production Version
ESG Files
Sign-off of Assumption Sets
Model Template
Model Build/ Customization
Model Test
Model Sign-Off
ModelDocumentation
Model Production Version
Step 3 Modelling Data
Sets
Step 2 Policy Data
Load/Upload to Repository
Step 1 Model
Construction
Load to Repository
ETL direct from Policy/Claims
Systems
Manual extracts from Policy/Claims
Systems
Enhancement of Data
Sign-off of Product Data Set/ Model
Points
Model Points
Specify policy data sub-set
Mortality Tables
Assumption Files
Yield Tables
Model Run Parameters Run Models
Model Output
ESG Files
Sign-off of Assumption Sets
Model Template
Model Build/ Customization
Model Test
Model Sign-Off
Model Documentation
Model Production Version
INSURANCEMOODY’S ANALYTICS
8 JULY 2014 WHITEPAPER: AUTOMATING THE SOLVENCY CAPITAL REQUIREMENT CALCULATION PROCESS
5. The SCR Calculation EngineAt the core of the process is the SCR calculation engine, which leverages the data in the repository to calculate the SCR results. The engine should contain all the formulas necessary to calculate the results. These formulas are best built-in the application to reduce the time and risks of implementing a Solvency II compliance solution. This built-in approach has the added advantage that the vendor maintains the formulas, which can be costly and time-consuming for an insurer to maintain. Furthermore, accurately maintained formulas ensure that the business and the regulations are always fully aligned.
6. Regulatory Reporting EnginesThe final stage of the SCR calculation process is reporting the results to regulators. Populating the QRT templates is complex, due to the large number of reporting points required. The use of the extensible business reporting language (XBRL) as the reporting format is also a complexity for many insurers. Automating this complex area will drive greater accuracy and consistency. Consolidating the reporting data by leveraging a dedicated reporting system, as shown in the following diagram, will enable managers to review the results. This can enhance their insight into the business, providing opportunities to enhance business performance, as well as regulatory compliance.
Risk type
Nat Cat risk
GI pricing risk
Market risk
Credit
DB1
Total
Risk profile as at ...
BU1 BU3 BUnBU2 (DB1) Firm
1 Diversification benefit
180 250%
200%
150%
100%
50%
0
140
160
120
100
60
40
20
0
Economic capital and solvency forecast
2009 2010 20122011
80
20142013
£bn
Own funds Economic capital Solvency coverage
7. End-to-end CapabilitiesHowever insurers approach the task of calculating their SCR results and submitting their reports, their infrastructure must be seamlessly integrated end-to-end. This structure ensures that the data consolidation, calculation and reporting process is accurate, consistent, streamlined and cost effective.
INSURANCEMOODY’S ANALYTICS
9 JULY 2014 WHITEPAPER: AUTOMATING THE SOLVENCY CAPITAL REQUIREMENT CALCULATION PROCESS
The Benefits of Automation
There are significant benefits to automating the SCR calculation and regulatory reporting process:
1. Automated Data Management
» Provide an accurate and reliable process for integrating data from multiple systems, in different formats, and transforming it into a unified data set. These data can then be used in the calculation and reporting process.
» Enhance accuracy and consistency by applying data quality checks that identify and eliminate poor quality data. These checks can improve the overall quality of the results.
» Leverage automated workflow processes to allow management to audit and approve changes to regulatory reports. Apply user access controls to control the users’ ability to change to core regulatory data.
2. Analytical Data Repository
» Provide a unified, consolidated source of data for data cleansing, calculation and regulatory reporting. Ensure consistency and accuracy, and deliver a coherent view of the business.
3. Workflow Engine » All actuarial, SCR and reporting processes can be defined and broken down into a number of tasks with associated time steps, approved users and rules. Processing can be automated or semi-automated, depending on requirements.
» Automation streamlines SCR calculations, delivering a structured, robust and fully auditable process. Task monitoring, reporting and exception handling are all effectively enabled.
4. SCR Calculation Engines
» There are various options as to where the SCR calculation can be performed – in the actuarial engines or in a dedicated SCR engine. Either way, automating the SCR calculation process improves efficiency and aids compliance.
5. Reporting Engine » Raw data stored in the repository can be transformed into meaningful business information for both regulatory and business purposes.
» Users can define their own reports.
» Reports can be scheduled or run automatically on a set date, time or occurrence.
6. Technology Platform
» By using industry standard components, an insurer can reuse existing skills and underlying technology components. This approach helps reduce both initial and ongoing costs of the project.
INSURANCEMOODY’S ANALYTICS
Regulatory Capital Management and Reporting for Insurance Companies
Moody’s Analytics offers a comprehensive solution for managing insurance regulatory capital calculation and reporting. The solution encompasses data consolidation, ratio calculations, as well as regulatory reporting. It helps insurance companies comply with all facets of the Solvency II directive while providing the foundation for improved strategy and performance.
To learn more visit moodysanalytics.com/riskintegrity.
INSURANCEMOODY’S ANALYTICS
10 JULY 2014 SP29733/101215/IND-104
© 2014 Moody’s Analytics, Inc. and/or its licensors and affiliates (collectively, ‘MOODY’S’). All rights reserved.
ALL INFORMATION CONTAINED HEREIN IS PROTECTED BY LAW, INCLUDING BUT NOT LIMITED TO, COPYRIGHT LAW, AND NONE OF SUCH INFORMATION MAY BE COPIED OR OTHERWISE REPRODUCED, REPACKAGED, FURTHER TRANSMITTED, TRANSFERRED, DISSEMINATED, REDISTRIBUTED OR RESOLD, OR STORED FOR SUBSEQUENT USE FOR ANY SUCH PURPOSE, IN WHOLE OR IN PART, IN ANY FORM OR MANNER OR BY ANY MEANS WHATSOEVER, BY ANY PERSON WITHOUT MOODY’S PRIOR WRITTEN CONSENT.
All information contained herein is obtained by MOODY’S from sources believed by it to be accurate and reliable. Because of the possibility of human or mechanical error as well as other factors, however, all information contained herein is provided ‘AS IS’ without warranty of any kind. Under no circumstances shall MOODY’S have any liability to any person or entity for (a) any loss or damage in whole or in part caused by, resulting from, or relating to, any error (negligent or otherwise) or other circumstance or contingency within or outside the control of MOODY’S or any of its directors, officers, employees, or agents in connection with the procurement, collection, compilation, analysis, interpretation, communication, publication, or delivery of any such information, or (b) any direct, indirect, special, consequential, compensatory, or incidental damages whatsoever (including without limitation, lost profits), even if MOODY’S is advised in advance of the possibility of such damages, resulting from the use of or inability to use, any such information. The ratings, financial reporting analysis, projections, and other observations, if any, constituting part of the information contained herein are, and must be construed solely as, statements of opinion and, not statements of fact or recommendations to purchase, sell, or hold any securities.
NO WARRANTY, EXPRESS OR IMPLIED, AS TO THE ACCURACY, TIMELINESS, COMPLETENESS, MERCHANTABILITY OR FITNESS FOR ANY PARTICULAR PURPOSE OF ANY SUCH RATING OR OTHER OPINION OR INFORMATION IS GIVEN OR MADE BY MOODY’S IN ANY FORM OR MANNER WHATSOEVER.
Each rating or other opinion must be weighed solely as one factor in any investment decision made by or on behalf of any user of the information contained herein, and each such user must accordingly make its own study and evaluation of each security and of each issuer and guarantor of, and each provider of credit support for, each security that it may consider purchasing, holding, or selling.
Any publication into Australia of this document is pursuant to the Australian Financial Services License of Moody’s Analytics Australia Pty Ltd ABN 94 105 136 972 AFSL 383569. This document is intended to be provided only to ‘wholesale clients’ within the meaning of section 761G of the Corporations Act 2001. By continuing to access this document from within Australia, you represent to MOODY’S that you are, or are accessing the document as a representative of, a ‘wholesale client’ and that neither you nor the entity you represent will directly or indirectly disseminate this document or its contents to retail clients within the meaning of section 761G of the Corporations Act 2001.