INSURANCE RISK SOLUTIONS

Insurance Risk Solutions

1)Risk Factor Data Architecture  Definitions:  Uncertainty : Limited knowledge of future states of the world  Uncertainties can impact my company posivitely or negatively  Uncertainties genereate risk exposures  Risk expsosures can generate losses or gains.  Risk is both the upside – value – and the downside – loss  Risk:  

Mean loss/gain Unexpected variability of results compared to expected results: loss/gain.

 Expected results are covered by provisions, unexpected are

covered by capital and other financial ressources.

Risk Factor Data Architecture  Goal: Convert those uncertainties – external and internal

to the firm – into decisions based on a systematic risk identification, risk assessment and quantification and risk management.  How:  Expsoure Data Identification: Get the data at the source.  Set up approprate risk models based on your goals 

Ex. Regulatory or business needs call for different types of models.

 Report and communicate impact of risk on your strategic

goals: financial measures and non financial impact like social responsibility indices.

Insurance Modeling Evolution ERM

DFA

ALM/Reserve

Duration Matching

Risk Factor Data Architecture

Exposure Data

Risk Models

Risk Results

Exposure Data Components

External Internal

• Financial Market Based Information Information: Int rates, currencies, credit markets, equity prices, commodity prices • Insurance Based Information: Industry data on mortatility, morbidity, trend indicators, health claims, fraud losses, hurricane forecasts, reputational indices.

• Liability Legacy systems on policyholders’ underwriting data like age, sex, health conditions, policy characteristics, claims, amount at risk.

• Assets Legacy systems on asset components by types of securities, duration, no of positions by type, liquidity….

• Strategic and business information: new business goals, new products, audit data on controls : DFA +ERM

Risk Models:

Capital Focused

• Models based on projected CF of EXISTING assets and liabilites • Projections based on stochastic generators for both financial variables and in some cases liability variables • Stress tests are perfomed on some specific variables.

• -Used for reserves, ALM, Economic capital models

Value Focused

• Models based on projected Value of EXISTING and NEW BUSINESS on an Portfolio approach.

• Projections based on stochastic generators usually for financial variables and include strategic plans and management actions as well • Scenarios are performed.

• Used for DFA – existing risks – and ERM – broader set of risks.

Risk Results: DashBoard

Capital Focused

• KRI Risk metrics: Duration, Convexity, defaults, liquidity ratios, expected claims • Distribution of results: VAR, CVAR, Capital AT Risk, Earnings at Risk, Probability of ruin • Capital Measures: Economic Balance Sheet over time.

• Risk Adjusted Performance Measures: RAROC type

Value Focused

• KPI Metrics like Embedded Value, Earnings at Risk, Value at Risk, projected share price, • Measures by type of risk: financial, operational, strategic, • Measures based on portfolios and correlations.

• Measures that integrate new business, their marginal contribution.

• Measures that integrate non financial elements

Improvements to Ins. Architecture: Efficiency  Usually focused on existing assets and liabilities: Lack the

forward looking element. DFA and ERM are more forward looking  Non financial risks are not well integrated into risk models:  Operational risk losses and business plans.  Liquidity risk – assets and liabilities – not included

 Actuarial risk models are usually not reactive rapidly enough to

new trends:

 Credit trends - KMV vs rating agencies –  Health/casualty claims: Don’t integrate external trends.

 Actuarial models done in isolation of operations.  Actuarial models can’t be used to control risk exposures. Not

reactive enough, too long to produce numbers.

2) Analytics Architecture and insurance organizational structure •Enterprise Wide view of Risks •Profitability •Capital Management

•Control of risk exposures •Compliance work for regulatory purposes

Corporate Actuarial

Centralized

• Pricing of new products • Profitability of existing products • Reseve calculations, claims management

Top Management

Liability Mgt

Asset Mgt

By BUs

Centralized • Pricing of securities • Portfolio management • Trading • Valuation of assets and derivatives

Analytics Architecture Actual Situation: Decentralized  Asset Management: own portfolio systems – Bloomberg,

Murex, Excel.. – and their own database of investment portfolios. Not an enterprise view of their counterparties and many manual processes due to private placements.  Liability Management: own systems or models – Excel & AtRisk, CatModels, data mining models - to price and value insurance liabilities using their insurance legacy, administration and claims systems.  Corporate actuarial: own systems – TAS, ALFA, PTS – using both downloads from the asset database and the company’s own administration, claims systems and using their own forecasts for financial values.  Top Management: Receive diverse reports and try to understand what is going on!

Analytics Architecture: EDW & Models  Centralized Enterprise Data Warehouse:  Would establish links to existing internal legacy systems of asset, liability, administration, claims, losses, and establish links to external common financial and non financial data.  Centralized warehouse of valuation and pricing

models used to value both existing assets and liabilities on a consistent basis by the asset managers, corporate actuarial and liability managers .  Decentralized models accessible by the business divisions to test new products and new services.

Analytics Architecture: Benefits of Centralized EDW and Models  Would allow the Enterprise Wide View of risks: gross

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and net of hedging/insurance activities Would allow the integration of risks: on and off balance sheet on both sides. Ex. Equity risks found on both sides for variable annuities.

A common platform decreases operational risk: model risks Would allow a company to manage portfolios of risks and financial ressources – capital – and measure value.

Would satisfy emerging regulatory views like Solvency II, in particular ORSA of Pillar II, economic balance sheet, risk management requirements – limits systems.

Analytics Architecture: Helps Innovation and Performance  A common EDW allows a more refined pricing and

integration of recent trends into pricing.  A common EDW allows the implementation of data mining to see trends, identify new opportunities, test new product features and assess impact on total company portfolios. Bunits can build their own models.  Would allow measurement of performance and profitability by

business lines.

 Would allow a quick response time based on a timely and

reliable dashboard and measurement and control of risk appetite.

3) Value Proposition  Value Statement: The SAS suite of solutions enable the

insurance industry improve its performance, anticipate emerging tr ends, create new products and assess and monitor its enterprise risk profile.  SAS offers a wide range of solutions to satisfy the needs of C-level executives, external parties like reporting to rating agencies and regulators.  SAS solutions can assist an insurance company implement major components of frameworks like COSO ERM, Solvency II, rating agencies ERM requirements. -Would need to provide mapping

Value Proposition  SAS also provides a broad array of solutions that can

satisfy the diverse needs of operational and marketing goals of insurance companies:  Health Insurers:  Disease management  Fraud management  Life Sciences expertise: Could establish links between that sector and the health insurance sector.  Casualty Insurers:  Data Mining and Predictive modeling  Cat modeling solutions?

Value Proposition  Life Insurers: 

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SAS offers a complete suite of investment management tools for the utility industry – Risk Advisory – that could be easily adapted to the asset portfolios of life insurers SAS offers many credit risk solutions – credit underwriting, credit modeling developed for the banking sector – that would be very useful to the life industry as it is weak point.

Improvement of customer profitability with SAS Marketing Solutions.

 All insurers: 

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Operational Risk is the weak point of the industry: SAS OP Risk Solutions, its association with BIA, other software like neural network, supply chain, baysian analysis Data mining to see trends affecting the field of reputational risk- brand Simulation tools: DFA and Value Based ERM, Economic Capital with SAS Business Analytics, SAS Business Intelligence, SAS Performance Management, dashBoards and balance scorecards solutions.

Value Proposition vs Competition  SAS is competing against dedicated systems like TP TAS,

Milliman MG-ALFA, Axis, Sungard Prophet , EMB, Oliver Wyman, the insurance arms of the BIG consulting firms, dedicated IT systems in the operational risk field, credit fields….  These systems are usually used by the insurance quants – actuaries – who perform with them specific tasks like reserve, pricing, EC,DFA calculations. Their work is usually done in isolation.  I would envision SAS to position itself as an enterprise risk solution that would give management actionable business information to drive value and satisfy regulations as well as perform detailed analytics.

4) Case Study  Summary of the business issues:  Both a PC and Life company with issues related to Balance Sheet management – recent credit losses on structured products for the Life portfolio – and data mining for the PC.  Actuaries use a set of analytics softwares to perform their duties: loss forecasting, cash flow testing, Asset Adequacy testing, Balance Sheet Management(ALM) and pricing.  Softwares used are actuarial softwares like Moses and Prophet – actuarial work – and SAS for data mining and advanced statistical analysis.  Based on the COO’s assertions – looking for a GRC solution and the next generation of IT platforms –

Best Practices  GRC:  Usually based on COSO: Implies risk identification, risk assessment, modeling, control and reporting.  GRC is a component of ERM.  Regulatory:  US Principles Based for regulatory reserve and capital management, particularly for the Life Business and the PC: Implies an enhanced modelling of assets and liabilities, both for the Life and PC business individually and in combination.  Insurance Industry can expect an increased demand of transparency: Implies an ability to gather reliable, timely and detailed financial and risk information for each business and in total for the company.  Solvency II Pillar I: Economic Capital for a broad set of risks  Solvency II Pillar II: Risk Management and control practices: ORSA  Solvency II Pillar III: Enhanced disclosure on risk exposure

Best Practices  Rating agencies:  SP, Moody’s, AmBest have indicated that ERM practices will influence their financial strength and claims paying ability ratings.

ERM implies risk identification, risk modeling, risk control, risk reporting and risk adjusted performance evaluation.  Accouting standards:  Proposed changes – IFRS and its implication on US GAAP – will move insurance accounting towards a fair-value approach: Implies more variability of performance and consequently, more risk management by firms to reduced that variability.  Ex. Widening Credit Spreads will influence the value of the Assets even though there are not defaults per se.  Portfolio replicating implies a Balance Sheet measured at fair value – no arbitrage assumption - , both the assets and the liabilities including hedging programs.

Best Practices: Next Generation  Integrated IT platforms and systems :  Actuarial systems are traditionally geared towards specific actuarial tasks, regulatory stress tests.  GRC and ERM imply a continuous analysis and monitoring of a broader set of risks – financial, operational, business, reputational , which an integrated IT Platform solution can deliver.  Internal Models Approval for Economic Capital will require, among other elements, a common data ware house of credit exposures, operational loss data, KRIs based on trends observed and analysis  Scenario modeling is becoming a big trend and requirement in the financial industry: an IT platform can perform those  Enhanced financial and risk reporting – not only SOX – demand that the IT platforms be integrated  Model risk due to implementation is reduced: A big component of op risk.

Priorities  Long-term: A GRC/ERM approach that will allow the

company implement a coordinated framework to manage and control its many risks from financial – based on IFRS -, to operational and compliance.  Operationalize the GRC/ERM approach with an integrated IT Platform.  Short-term: Implement an integrated IT platform on the existing Life and PC portfolios, starting with the assets and then integrate the liabilities.

Potential Steps  Set a common EDWarehouse, which would regroup major components        

of both assets and liabilities.

Implement a set of models/systems to measure both assets and liabilities according to proposed fair-value – IFRS – standards.

Implement risk drivers to measure the financial riskiness of the portfolios – assets and liabilities -. Can be stress tests initially, stochastic afterwards.

Integrate existing hedging strategies.

Generate risk reports - risk metrics - to measure risk exposures, both gross and net and required EC. Set up an operational risk system to assess operational risk exposure.

Expand the platform to integrate business risks.

Move some actuarial compliance work onto the common IT platform.

Move to Balance Sheet management: Modeling from NOW to the FUTURE

Agenda  Presentation of SAS: History, culture, how we work with clients.  Discussion of our understanding of your situation  Your company: verification of our initial research  Actual state and Desired state in terms of analytical and risk management needs:    

What are your drivers?

Ideal situation vs realizable considering company’s constraints, budget, timeline, commitment, people.

What are your quick wins? Full ERM vs specific risk management?

What are your IT platforms and their uses?

 Presentation of what we see as emerging best practices and trends as it relates

to the insurance industry.  Discussion on your priorities: Long-term and short-term in terms of data, analytics, Balance Sheet management  High-level presentation of potential SAS IT solutions including demo if time permits.  Next steps.

Sales Strategy  Start with an understanding of the company.  Identify their needs and get them to articulate what they want to   

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accomplish: sometimes it is fuzzy.

Determine how and if SAS can be of any help: full solution from EDW to analytics and reporting or just a component of it.

Show SAS advantages in terms of intellectual capital, breath of expertise, reliability.

Determine their commitment in terms of time, budget, top management approval.

Make a proposal – RFP - with both short-term and long-term objectives based on their priorities.

Involve relevant SAS people from the account manager to technical assistance when necessary.

Develop a detailed implementation plan.

Sales Objections  Price will be a consideration as insurance company’s

profitability is usually low.  Company may say that they already have existing systems and people – actuaries mostly – to perform these tasks. Operational risk can be reduced by having an integrated IT solution.  Company may say that SAS’ insurance experience is limited and products not directly adapted to the insurance operations and financial and regulatory needs. However , GRC and ERM are broad and standard frameworks.

Questions  Other questions?  Vous avez des questions?  Tienen preguntas?  Thanks  Merci beaucoup  Muchas Gracias