How Can Insurance SIFIs Overcome the Challenges of the Fed Stress Testing?

Overcoming the Challenges of Insurance SIFI Fed Stress Testing Originally presented at the 3rd Annual Risk Americas Summit | May 12, 2014

2 Stress Testing & Capital Planning for Insurers – March 2014

Agenda

1. Insurance vs. Banking

2. Stress Testing Pain Points for an Insurer

3. Focus Area 1: Scenario Design

4. Focus Area 2: Speed

5. Q&A


Insurance vs. Banking 1


Insurance vs. Banking

There are a number of reasons why the insurance Fed stress testing experience is going to be different to what the banks have been going through:

» Financial Accounting – An Insurer’s income statement and balance sheet look very different to a bank’s.

» Model Sophistication – An Insurer’s projection models are already quite sophisticated, such that an Insurer must build from existing capabilities. This is different to where many banks stood at the advent of the stress-testing process.

» Scenario Design – Current FRB stress scenarios do not mean a lot in an insurance projection model – so development of enhanced or custom scenarios will be needed to properly capture and model insurance risks.


Insurance vs. Bank Forecasted Values

BANK INSURANCE COMPANY COROLLARY

Interest Income -------> Investment income and capital gain on assets

- Interest Expense -------> - Policyholder claims

+ Non-Interest Income -------> + Policyholder premiums

- Non-interest Expense -------> - Expenses and commissions

= Pre-Provision Net Revenue = Pre-Tax Earnings before changes in reserves

- Provisions -------> - Increase in Reserves

= Pre-Tax Net Income = Pre-Tax Earnings

- Tax -------> - Tax

= After-Tax Net Income = Earnings net of tax

The financial accounting line items between a bank’s financial statements and those of an insurer are different and need to be treated differently.


Stress Testing Pain Points for an Insurer 2


Stress Testing: Recognized and Anticipated Pain Points

Insurers already have the ability to project Income Statement and Balance Sheet items over deterministic and stochastic scenarios using standard actuarial projection systems. But there are pain points specific to stress-testing that these systems alone will not resolve:

» Data and Process Management - Fed stress testing necessitates the management of big data volume and creates process management challenges.

» Report Aggregation - Consolidation of reports from multiple LoBs with the required audit trails and internal controls.

» Scenario Design - Fed stress scenarios will need to be converted into something that is meaningful in the context of an insurance projection and supplemented by custom scenarios.

» Documentation - Documenting supporting analysis and judgment required in developing appropriate projection assumptions for type of product issued, new business volumes and management actions. Based on what we have seen the Fed expects of the banks, insurers are going to need to move up a notch their quality of documentation.

» Speed - Especially for VA GLB products, excessive model run-time will need to be addressed when projecting reserves.


Focus Area 1: Scenario Design 3


Aspects of Scenario Design

There are two key aspects to scenario design

» Stress scenario generation – Insurers will need to convert a given Fed stress or company specific “narrative” into a fully articulated scenario set that can be understood by the Insurer’s projection system and appropriately captures all the risk exposures. For example, it is not enough to say the short rate will stay at 1% for the next n years; in order to capture fully interest rate risk a projection of the full yield curve will be required

» Development of company-specific stresses that supplement the Fed stresses – In addition to the stresses that the Fed specifies, firms are required to do supplementary stresses that recognize the specific risks to which they are exposed that may not be picked up by the specified Fed stresses


Stress Scenario Generation

» Need to translate macro/narrative scenarios to scenarios relevant for Insurers – For example, how do you go from a single macro interest rate stress full yield curve

continuous time stochastic/statistical model needed for insurance product analysis?

» What scenarios are in scope? – Interest rates? – Equities? – Mortality risk? – Longevity risk? – Lapse risk? – Cat risk? – Etc.

» Relationship across stress scenarios – Are stressed scenarios fully consistent/integrated across risk variables – Flu pandemic drop in equity markets


Development of Scenarios that Supplement the Fed Stresses: Critical to get the Stresses Right for your Line of Business

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Baseline forecast

Stronger Near-TermRebound

Slower Near-TermGrowth

Double-Dip Recession

Protracted Slump

Below-Trend Long-Term Growth

Oil Price Increase,Dollar Crash Inflation

Using Moody’s Analytics Economic and Consumer Credit Analytics (“ECCA”) Macro scenarios and financial impact on CTE Reserves for UL line


“Narrative” stresses

» Japan Scenario (“Narrative 1” or “N1”) – Start: 12/31/1989

– End: 12/31/2012

– Deterministic scenario = 276 months, forward-looking projection = 444 months.

» Great Depression Second Dip (“Narrative 2” or “N2”) – Start: 06/30/1936

– End: 12/31/1938


» Post WWII period of financial repression (“Narrative 3” or “N3”) – Start: 12/31/1945

– End: 12/31/1955



Focus Area 2: Speed 4


Bank vs. Insurer Forecasted Variables

INSURANCE COMPANY

Investment income and capital gain on assets

- Policyholder claims

+ Policyholder premiums

- Expenses and commissions

= Pre-Tax Earnings before changes in reserves

- Increase in Reserves <------- Major Pain Point

= Pre-Tax Earnings

- Tax

= Earnings net of tax


Nature of the Stress-testing Beast – Multi-Stochastic Processing


Proxy Modeling as a Way to Manage the Beast

» Proxy fit – Can be done using “Curve Fitting” or “Least Squares Monte Carlo” – Both approaches in essence are about acknowledging there is a “scenario budget” and trying to

make best use of that budget

– Curve Fitting - establish key fitting points on the risk spectrum, do accurate calculations at these key points and then fit a function to these key points

– Least Squares Monte Carlo (LSMC) – establish what the entire risk spectrum is, do a relatively inaccurate calculation at every single point, then fit a function to every single point. Each fitting value on its own is wrong, but with many points calculated the errors cancel, on average, giving an extremely good resulting fit.

» Key benefits of the proxy approach – Can significantly reduce A-L projection runtime

– Resulting function enables you to do stresses “at will”

– The NAIC is supportive of proxy techniques for projecting capital – providing the resulting functions are properly validated


Model Comparison – LSMC enables you to capture a larger risk space


LSMC is Accurate and Easy to Validate Comparison of 1-year surplus calculated using the proxy and calculated using 1,000 RN scenarios

» The average difference between the validation and the proxy function is $60 million

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LSM

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B&H Validation Scnearios

Validation points chosen to test function in

different regions, or in regions of high

sensitivity


Q&A 5


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How Can Insurance SIFIs Overcome the Challenges of the Fed Stress Testing?

Economy & Finance