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PwC Actuarial Services NewsletterIssue 8, December 2019
EditorialThis year marks five years of our European collaboration on our Actuarial Services News-letter. We want to take this opportunity to look back on an interesting year for actuaries, and take a glance at what awaits actuaries in the coming months and years.
With respect to risk- and finance-related topics, the new accounting standard IFRS 17 and its amendments are keeping (re)insurance companies and the actuarial practice moving at a rapid pace at the current stage. Even so, there still seems to be a long way to go with respect to the implementation of all required processes and models.
The use of data analytics in claims handling in the insurance sector is gaining momen-tum. Processing claims is a key activity for any insurer and the use of data analysis techniques can unlock a wide range of benefits for both insurers and insured parties. For these reasons, claim analytics should be a focus of investment from now on.
Cyber risk presents significant growth opportunities for the insurance industry in a gen-erally soft market, but at the same time has become one of the top threats to the indus-try. Insurance professionals draw a distinction between affirmative (stand-alone cyber products) and silent cyber risks (ie, non-cyber policies that do not explicitly exclude cy-ber risks); both exhibit their own challenges with respect to risk modelling, pricing, policy design and policy wording. Inappropriate policy wording might expose insurers to cyber risk even if they do not offer cyber insurance policies per se. Therefore, insurers need to develop a better understanding of (silent) cyber risk management, define their individual risk appetite and strategies to tackle the challenges that come with cyber risks.
We hope you enjoy this newsletter and we look forward to the opportunity to discuss these topics with you in the near future.
Key points in brief
Article #1 IFRS 17
Article #2 Claim analytics
Article #3 An introduction to (silent) cyber risk
IFRS 17
Contacts
Dr Alexander [email protected]
Carsten [email protected]
Wladimir [email protected]
Tilmann [email protected]
Introduction
The International Accounting Standards Board (IASB) published the new international financial reporting standard (IFRS) for insurance contracts, IFRS 17, on May 18th 2017. IFRS 17 will replace the current interim standard for insurance contracts, namely IFRS 4. The aim of IFRS 17 is to establish a principle-based accounting standard for insurance companies around the globe that apply IFRS.
On June 26th 2019, the IASB released proposed targeted amendments to IFRS 17 in an exposure draft. The exposure draft was open for comments for a period of 90 days end-ing on September 25th 2019, in which stakeholders had the opportunity to raise con-cerns to the IASB regarding the proposed amendments. The Board’s focus in issuing the exposure draft has been to assist companies implementing the standard without unduly disrupting implementation or diminishing the usefulness of the improvements introduced by IFRS 17. One of the more significant proposed amendments is a delay of the effec-tive date of the Standard. The IASB initially intended IFRS 17 to become effective for all financial reporting on January 1st 2021, but the Board proposed to amend IFRS 17 to move that date to January 1st 2022 and even deferment for an additional year is under discussion. The amendment to 2022 is highly welcomed by the insurance industry, although some entities still consider the timetable challenging. Other issues identified by stakeholders and addressed by the Board in the ED will have an impact on those involved in preparing IFRS 17 financial statements and help to support the implementa-tion process.
Thus, after the introduction of Solvency II, this new regulatory requirement presents a mammoth new challenge for (re)insurance companies preparing IFRS 17 financials. Methodological complexity of IFRS 17 and its technical implementation is keeping the insurance industry in a state of flux, while the value-added vision through enhanced transparency for investors and its implications on business steering is still not yet clear. The following article will introduce some hot topics for the whole international insurance industry practice, and then focus on issues including the transition approach and the eligibility approach for different measurement models in IFRS 17, as these are important for many (re)insurance companies.
Main amendments to the IFRS 17 standard
In the IASB meeting on April 9th 2019, the Board considered issues in IFRS 17 principles previously identified by stakeholders as a whole and agreed to proceed with drafting an exposure draft on the amendments to IFRS 17.
The exposure draft proposes the expected targeted amendments in eight different areas of IFRS 17, in addition to several clarifications to the Standard: 1. Deferral of the effective date to January 1st 2022 for IFRS 17 and IFRS 92. Attribution of profit to service related to investment activities3. Accounting of prepaid acquisition costs to expected renewals4. Recognition of gains on inception of proportionate reinsurance for loss-making un-
derlying contracts, to reduce accounting mismatches5. Extension of risk mitigation option for contracts falling under the Variable Fee Ap-
proach (VFA)6. Balance sheet presentation at portfolio rather than group level7. Additional transition reliefs8. Scope exclusions for certain loans and credit cards
PwC published a general over-view of the amendments, which can be downloaded from www.pwc.com/gx/en/audit-services/ifrs/publications/ifrs-17/ifrs-17-Insurance-contracts.pdf.
Download
PwC Actuarial Services Newsletter Issue 8, December 2019 PwC | 2
Although the proposed amendments address the very important issues, we believe that further guidance or educational material will be helpful. In particular, we think that pre-parers of IFRS 17 financial statements will benefit from material on how to determine• whether contracts are eligible for the VFA,• profit attribution for contracts that have multiple types of insurance contract services,
and• whether insurance contracts without direct participation features provide investment-
return services.
Level of aggregation
One of the fundamental principles for the accounting and measurement requirements for insurance contracts under IFRS 17 is having a clear picture of the (economic) “insurance contract” as the basis for different levels of granularity. The IFRS 17 “unit of account”, used in all measurement calculations within the Standard, is based on the level of groups of insurance contracts (GIC). This is complex, in particular for reinsurance contracts (is-sued as well as held) and for group insurance business. The determiniation of what con-stitues an (economic) insurance contract, in combination with the decision on the level of aggregation has tremendous impact on the profitability of groups as well as on the complexity of technical implementation. Moreover, the level of aggregation is a primary driver of accounting results.
IFRS 17 requires that insurance contracts in one portfolio have a similar risk profile and are managed together. This is achieved by using a “preallocation” of insurance contracts based on portfolios of insurance contracts (PICs) prior to grouping into GICs. GICs are primarily used to distinguish groups of contracts based on timing of insurance con-tract issuance (annual cohorts, ie, contracts issued within one year) and profitability. In practice, the consideration of both criteria at the same time leaves a considerable level of expert judgement for companies, which can be helpful to support robust and mean-ingful accounting and valuation approaches. Once a portfolio of insurance contracts with “similar risks” that are “managed together” is established, further judgment and steering decisions with respect to cohorts, profitability, and potential other relevant internal steer-ing criteria must be made. In particular, the requirement of annual cohorts for strongly mutualised business, ie, insurance business which is mainly applicable for the VFA, remains an issue for insurance companies having long-term life and health insurance business with profit participation features. In such cases, the interdependence between the decision about the level of aggregation and the corresponding measurement impact on unrealised profit (the so-called “Contractual Service Margin”, CSM) and its release to profit or loss over time.
As a minimum requirement, a portfolio of insurance contracts should be split into annual cohorts and grouped into the following categories:• Contracts that are onerous at initial recognition (ie, loss-making), if any• Contracts that, at initial recognition, have no significant possibility of subsequently
becoming onerous, if any• Remaining contracts in the portfolio, if any
A number of relevant financial reporting metrics are significantly impacted by the level of aggregation. For example, the fulfilment cash flows for a group of contracts may be measured at a higher level than the group or portfolio, but only if the entity is able to appropriately allocate the fulfilment cash flows to a lower level of granularity. Other areas impacted by the decision on the level of aggregation, with respect to portfolio definition, include accounting policy choices on the so-called other comprehensive income (OCI) option, directly attributable costs, contract boundary assessments and balance sheet presentation. Areas impacted by the level of aggregation with respect to grouping in-clude the risk adjustment, directly attributable indirect expenses, and discretionary cash flows with respect to profit participation (often referred to as mutualisation).
A detailed description of the amend-ments, as well as amendments that were proposed but ultimately not accepted by the IASB, can be found in the following PwC publication: www.pwc.com/gx/en/audit-services/ifrs/publications/ifrs-17/iasb-exposure-draft-to-amend-ifrs-17.pdf
Download
PwC Actuarial Services Newsletter Issue 8, December 2019 PwC | 3
IFRS 17
The level of sophistication and number of possibilities to shape IFRS 17 segmentation seems to be unlimited. Defining the appropriate level of granularity to meet all stakeholders’ expectations with a
reasonable level of complexity is a challenging task. It is a trade-off between quality, expenses and time. The granularity of the segmentation is also an important driver of the resulting effort for
the model validation and results at the group of contracts level. Up to a certain point, granu-larity might be an important measure for the level of quality of the results (ie, a very broad aggregation in the model might not adequately reflect all product features).
Legal contracts, separation and unbundling
Prior to grouping contracts, all features and terms of an insurance contract need to be assessed. This includes separation of any distinct investment components or embedded derivatives as well as any distinct non-insurance goods or services provided under an in-
surance contract. These non-insurance components are measured and accounted for under the respective accounting standard, IFRS 9 or IFRS 15. In addition, as Transition Resource
Group (TRG) discussions have clarified, it may also be the case that insurance components could be unbundled to better reflect the economics behind the legal contracts. This may also be
a challenge, in particular for multi-cover insurance contracts, reinsurance contracts, group insurance contracts and complex insurance contracts with different kinds of riders.
Portfolios and groups of insurance contracts
After the relevant IFRS 17 insurance contracts have been identified, they are mapped to different (sub-)portfolios including insurance contracts with similar risks which are managed together. In a final step, these (sub-)portfolios are then further divided into different groups based on the aforementioned criteria of profitability and annual cohorts. In this step, allocation is irrevocable for each insurance contract, ie, once an insurance contract is allocated to a group at initial recognition the allocation to this group shall not be reassessed, which shows the importance of the grouping exercise at initial recognition. The mandatory approach of (at least) annual cohorts increases the number of groups within each IFRS 17 portfolio. For sets of insurance contracts that contain considerable mutualisation features, the current IFRS cohorting approach creates a significant number of concerns and implementation challenges. A number of stakeholders raised these concerns to the IASB; however, this did not result in an amend-ment to IFRS 17. Handling of such mutualised portfolios will therefore remain on the agenda during the implementation process for a number of companies.
Onerous contracts
A significant change between current accounting practice and IFRS 17 is the identification and reporting of so-called onerous contracts at initial recognition. The standard defines a contract as being onerous if “the fulfilment cash flows allocated to the contract, any previously recognised acquisition cash flows and any cash flows arising from the contract at the date of initial recognition in total are a net outflow”. We have observed in the market that this requirement leads to the creation of “onerous contract test-ing”, a thorough process by which companies review their portfolios of insurance contracts at a higher
Qua
lity Expenses
Time
Optimisation
Legal Contract Separation Unbundling Portfolio Grouping
Contract signed with
policy holder
IFRS 17
IFRS 9
IFRS 15
e.g. Insurancecomponents
Similar risks?Managed together? Other metrics?
...
PIC 1
PIC 2
PIC 3
PIC n
Year
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O NO Other
...
PwC Actuarial Services Newsletter Issue 8, December 2019 PwC | 4
IFRS 17
level of granularity to identify onerous contracts and contracts which could potentially become onerous. Such testing is performed on qualitative and quantitative bases and contracts are placed into the ap-propriate profitability cohort based on the results of the tests.
Under IFRS 17, expected losses on onerous contracts require immediate recognition. This is in opposi-tion to expected gains on non-onerous contracts, which are recognised as service is provided over time, leading to accelerated loss recognition if compared to profit recognition. An additional implication is that companies are required to separately disclose the financial impact related to onerous contracts. This will allow users of financial statements to compare the level of unprofitable contracts across differ-ent entities within the industry, which could have implications for certain business decisions.
In practical terms, the expected future losses on onerous contracts are tracked until the coverage period is extinguished. This will lead to additional data and systems requirements, as companies will need to develop a framework for identifying onerous contracts and track those contracts over time. This requirement is particularly cumbersome for companies utilising the Premium Allocation Approach (PAA), since tracking of onerous contracts may partially require the use of the General Measurement Model (GMM) in determining the appropriate carrying amount for the onerous contract liabilities.
Transition approach
One of the other major challenges in the IFRS 17 transformation process, also connected to the decision on the level of aggregation, is the handling of long-term insurance business as it relates to the deter-mination of the transition approach to be applied and the usage of simplifications at transition. IFRS 17 defines the transition date as the beginning of the reporting period immediately preceding the date of initial application. Therefore, if the effective date of IFRS 17 is to be January 1st 2022, the transition date will be January 1st 2021.
IFRS 17 requires that on transition, the insurer should retrospectively apply IFRS 17 in full to all insur-ance contracts in force at transition, ie, as if IFRS 17 had always been in place (the so-called “full retro-spective approach”), unless it is impracticable to do so. In cases where the full retrospective approach is impracticable for certain parts of insurance business, companies can apply either the so-called “modified retrospective approach” or the “fair value approach”.
Using either of the simplified options (modified retrospective or fair value approach) on transition influences shareholders’ equity and CSM differently. Thus, the judgements made at inception will be relevant for steering purposes and stakeholder communication. In addition, the effect on operational complexity and the cost of IFRS 17 implementation will be a consideration in implementation decisions. An extra complication is that some profit or losses from insurance contracts might not be recognised at all in profit or loss (ie, if contracts have not been recognised in profit or loss under IFRS 4, they will be recognised as an adjustment to equity immediately on transition to IFRS 17), whilst other profits might be recognised in profit or loss twice (ie, if contracts are recognised in profit or loss under IFRS 4, they will also be recognised in the contractual service margin after transition to IFRS 17). This is an unavoid-able result of differences in measurement approaches used under IFRS 4 and IFRS 17.
The contractual service margin on transition represents the remaining carrying amount of profit from insurance contracts in force at transition. On transition, a higher contractual service margin yields a smaller profit accumulated from insurance contracts immediately recognised in shareholder’s equity. Consequently, more profit will be recognised in future periods. This might also affect the way in which investors assess the performance of the entity on transition and at future dates until the end of the cov-erage period of the contracts in force on transition.
PAA eligibility
As companies with non-life insurance contracts of longer duration continue their IFRS 17 transformation process, one of the first questions that must be addressed and finalised is which measurement model is applicable for their insurance liabilities. While the GMM is the default measurement approach for all insurance contracts without direct participation features, non-life insurers in particular are exploring whether the use of the simplified approach compared to the GMM, the PAA, is applicable for most or all of their business. This holds true for both directly issued insurance contracts and reinsurance contracts held.
Eligibility for the PAA remains one of the most widely discussed topics in insurance companies with non-life business and has led to significant costs related to impact assessments and materiality analysis
PwC Actuarial Services Newsletter Issue 8, December 2019 PwC | 5
IFRS 17
in the current implementation phase. There are two separate criteria a portfolio of contracts can meet in order to be eligible for measurement under the simplified approach; the second criterion is the specific focus of discussion:1) If all contracts in the group have a coverage period of one year or less, then the group is automati-
cally eligible for measurement under the PAA. If there are any contracts in the group with a coverageperiod greater than one year, then
2) the entity must demonstrate that the liability for remaining coverage (LRC) for the groups of contractsmeasured under the PAA would not differ materially from the LRC measured under the GMM. Thiscriterion also requires that an entity does not expect “significant variability” in the fulfilment cashflows for the group of contracts; otherwise, the group is not eligible for the PAA.
As IFRS 17 is a principle-based standard, there is no concrete guidance on how to assess whether the LRCs “do not differ materially”, nor are there tangible criteria for defining “significant variability”. As such, companies have developed their own frameworks for assessing eligibility for contract groupings that do not automatically qualify under criterion 1) above; this has led to different approaches during the implementation phase. Over time, it is likely that the insurance industry will determine a market prac-tice for the so-called PAA eligibility assessment and testing principles. A parallel could be drawn to the development of a host of comparative financial reporting analytics for evaluating the presence of risk transfer for reinsurance contracts under the US GAAP.
There is a wide range of perceived benefits of implementing the PAA rather than the GMM, including simpler implementation, lower data storage requirements and a more direct link to current IFRS practice. It is likely that this topic will continue to be discussed further as the IFRS 17 effective date comes closer.
VFA eligibility
IFRS 17 draws a distinction between contracts with and without direct participation features. The stand-ard defines contracts with direct participation features as contracts for which a) the contractual terms specify that the policyholder participates in a share of a clearly identified pool
of underlying itemsb) the entity expects to pay to the policyholder an amount equal to a substantial share of the fair value
returns from the underlying items; andc) the entity expects a substantial proportion of any change in the amounts to be paid to the policyhold-
er to vary with the change in fair value of the underlying items.
For contracts fulfilling conditions a)–c), the mandatory measurement model is the VFA, which is a modi-fication of the GMM taking the investment-related nature of these contracts into account. The conditions are assessed at inception; a reassessment shall not be performed unless the contract is substantially modified.
Assessment of the VFA eligibility criteria on contract-by-contract basis or on group level is currently be-ing discussed in the insurance industry.
Regardless of the conditions, reinsurance contracts cannot be measured using the VFA.
Accounting mismatches for reinsurance contracts held
An amendment within the exposure draft lays out circumstances in which an initial gain recognition is possible for proportionate reinsurance contracts held on inception. The purpose of this amendment is to reduce accounting mismatches where an onerous underlying contract is covered by a proportional reinsurance contract held (the exposure draft also includes a new definition of the term “proportional”). Prior to the amendment, an insurer was required to always recognise the gain or cost of such reinsur-ance contracts held over the whole coverage period, while the loss on the underlying onerous contract required immediate recognition. After the amendment, under certain circumstances an insurer can rec-ognise an immediate gain to offset some of the initially recognised direct loss. This benefit is calculated as the loss on the underlying group multiplied by the percentage of corresponding claims covered by the reinsurance treaty. If there is any residual gain or cost, this is recognised over the coverage period.
In this issue of the newsletter, we have highlighted some IFRS 17-related topics which are keeping our industry moving. The implementation process of IFRS 17 is going forward and we still have interesting and challenging times ahead of us.
PwC Actuarial Services Newsletter Issue 8, December 2019 PwC | 6
IFRS 17
Claim analytics
Contacts
Theo [email protected]
Gerard [email protected]
Pieter [email protected]
José [email protected]
Introduction
For an insured person, the moment when insurance matters is when they suffer (non)ma-terial damage. An insurer can play a pivotal role in paying out the claim, and even before such an event by providing information, taking preventative measures, etc. In practice, insurers have focused their efforts in analytics on pricing. Claim analytics is probably equally important, or even more so.
The use of predictive analytics in claims handling in the insurance sector is gaining mo-mentum. With new emerging technologies and customers demanding more personalised service across all sectors, the insider knowledge that data analysis and claims analysis can provide becomes almost essential. These new conditions have brought new data-driven organisations to the market, creating a more competitive environment.
Insurers using claims analysis can benefit in many ways. For example: • Improved customer experience through a smoother claims process and better, more
personalised interaction with claimants• Reduced uncertainty in decision making through a better and more granular under-
standing of claims that is not possible with more traditional techniques• Enhanced claim forecasts by providing faster/automated and more accurate forecasts• Improved claim reserving (processes) by more precise analyses of claims and patterns• Detection of fraud and prevention of wrongful increase of premiums for policyholders• Reduced claim handling costs
All these factors provide a competitive advantage, or even the raison d’être for insurance companies, making a difference to increase sustainability and business performance. Lagging behind reduces the relevance and sustainability of insurance companies.
The following paragraphs provide a view on the current status of claims analytics in Germany and the Netherlands.
Situation in Germany
The insurance sector in Germany is not indifferent to the fast-growing worldwide trend towards digitalisation, and the resulting transition to data-driven digital business models is set to transform the business. Companies are aware of the opportunity offered by new approaches and technologies, and how they can improve decision-making and create competitive advantages. However, most companies are still in the early stages of this transformation. Most companies still use legacy systems, where data is stored in differ-ent databases in structured and unstructured forms. Therefore, the first challenge is to develop a plan that brings together data, analysis, top-quality tools and people to create business value.
The next paragraph provides a case study to illustrate the introduction of claim analytics at an insurance company, focusing on actuarial reserving.
Case study
The topic of claims management in the area of property/casualty insurance, especially for motor portfolios, has been thoroughly examined in recent years by the working group “Interest and Inflation in Property Insurance“ (headed by Dr Daniel John) from the sub-working group on Actuarial Data Analytics in Claims Reserving (ADACR) (DAV-AG non-life insurance committee). Their goal was to develop a suitable model for describing the effects of interests and inflation in a loss portfolio and to identify the main (macro-)
PwC Actuarial Services Newsletter Issue 8, December 2019 PwC | 7
economic loss drivers. It ultimately became clear during the work that analysis on the basis of aggre-gated data is very difficult. In order to approach the issue of claims management, a much more com-prehensive approach with clearer reference to the level of individual claims is necessary. These findings underline the need to further develop actuarial reserving towards individual claims.
The Actuarial Department and the Claims Department often work completely independently and without any coordination. The actuary is interested in the best estimate provision for the entire portfolio, while the claims processor is interested in “cautious” provision for the individual damages. The actuarial reserve process is often far removed from the processing of operational damages. This means that a good interaction between actuarial reserves and the processing and control of operational claims is of-ten almost impossible. This translates into a wide range of unused synergy potentials. The damage case officer’s expert knowledge remains hidden from actuaries, but could be very useful in the estimation of reserves. By using an individual approach, all of this valuable information can be leveraged and made available to the management of the company.
Further development of the traditional reserve should address these points and bring together the differ-ent perspectives. It is evident here that development must move towards individual damage.
Objective
In this case study we wanted to explore extensions to the simplified traditional aggregated approach (triangles) to claims reserving. Claim triangles hide too much information about the portfolio due to strong aggregation of payments or expenses. Claims behaviour is influenced by a variety of different factors (eg, inflation, legal changes, damage composition, medical progress, life expectancy, binding behaviour...), and it is almost impossible to accurately understand how these elements work, how they correlate and how they explain each other when looking at aggregated data.
Approach and steps taken
Our vision for addressing this challenge is to extend the focus of the aggregated models to include in-dividual claims information. We define the modelling of provisions and cash flows for individual losses based on their individual characteristics. The features to use in this process go far beyond reporting pay ments and expenses. For example, characteristics such as the claimant’s age, marital status, geo-graphical location, credit scores, salary, occupation, etc may be included. For analysis and modelling, the complete toolbox can be used, from classic GLMs to modern data analysis and machine learning methods.
So far, we have tested the performance of neural networks and GLMs on synthetic individual claims data. We built a dashboard where their results and the results of aggregated methods can be compared.
Comparison of techniques
Traditional aggregated data rely on the assumption that the claims data is sufficiently homogeneous for a coarse reserving algorithm to be applied. However, this is not always the case; for example, if there is a change in the composition of the portfolio affecting the behaviour of the claims, aggregated methods are not well suited to capture these variations. By contrast, individual approaches build the reserves at a more granular level. This gives the models greater flexibility, which translates into better performance. The individual claims models can also be used to support claim handlers in the estimation of damages, or this process could be fully automated.
Observations and results
As can be seen in the following plot, the individual approaches return results that look very similar to the traditional methods at an aggregated level, but individual methods bring the additional benefits men-tioned above.
The performance of the individual approaches should be further tested with real data, and the algo-rithms fine-tuned and adjusted to integrate the available features.
PwC Actuarial Services Newsletter Issue 8, December 2019 PwC | 8
Claim analytics
Situation in the Netherlands
In recent years, many property and casualty (P&C) insurers offering third-party motor liability products have suffered increasing amounts in bodily injury claims. Case and IBNR reserves have needed to be adjusted upwards over and over again, causing increasing losses which were primarily severity-driven. One of the causes is that cases are increasingly charged with extrajudicial costs, because of the trend that injured persons are more often assisted by an advocate. As in other markets, actuaries in the Neth-erlands still rely on traditional aggregated triangle approaches when estimating reserves. Using this ap-proach, the actuary ignores many factors that influence the amount of bodily injury claims, for example: • Type of injury• Duration of claim• Going to court or not• Whether the injured person has an advocate• Income of injured person• Age of injured person• Social status of injured person
The data features as listed above are often not available to an actuary in a data warehouse, and are only stored in written files like emails. In order to get a better insight in the trends in claim portfolios, insurers should store more details of a claim in a data warehouse. This enables the actuary to extend the analysis on bodily injury claims – not only estimating IBNR, but also assisting the claim handler with questions like:• Which factors are relevant for setting the case reserve?• What is the probability of going to court and how can this be prevented?• Which claims have a high expected amount of subrogation?• Which claims are potentially fraudulent?
Using a large amount of data, the actuary can apply various (machine learning) techniques to answer these kinds of questions. Imagine you have trained a decision tree model on the level of historical bodily injury claim amounts, and that you are using this model to attach a score to the outcomes, ranging from 1 (high claim) to 999 (low claim). New income claims can be scored using this model, also showing the factors relevant to the size of this claim – see the figure below:
0.0M
0.2M
0.4M
0.6M
0.8M
1.0M
1.2M
1.4M
1.6M
1.8M
nnmic_glmglmclreal
200520042003200220012000199919981997199619951994
Observations and results in Germany: Ultimates per method
PwC Actuarial Services Newsletter Issue 8, December 2019 PwC | 9
Claim analytics
Claim 1 has a high score, indicating a relatively low claim amount. This claim is positively influenced by factors that might indicate the person’s characteristics (age, gender etc) and injury characteristics (pri-mary body part, nature of injury etc). On the other hand, claim 2 has a low score, indicating a relatively high claim amount. This claim is negatively influenced by certain factors, which are not necessarily the same factors as for claim 1. If the claim handler had access to this kind of information, he/she would be able to make a judgement about the case reserve in a more data-driven and objective way. Similar mod-els can be trained for other objectives, eg, prediction of likelihood of litigation for bodily injury claims, allowing the claim handler to take action to prevent litigation and significantly reduce associated legal and settlement costs.
Conclusion
A wide range of data analytic techniques can potentially be applied to the claim data of P&C insurers. Due to legacy systems and insufficient data recording, advanced analytics can often not yet be ap-plied on a large scale. Actuaries should take the lead in improving the availability and quality of detailed characteristics of claim data, which involves close collaboration with claim handling departments and IT departments. This will enable actuaries to take centre stage in providing a competitive advantage for their insurance company.
0 20 40 60 80 100 120 140
Factor N
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Factor 2
Factor 1
–60 –50 –40 –30 –20 –10 0 10
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Factor 2
Factor 1Claim 1 – Score 961 and Claim 2 – Score 279
PwC Actuarial Services Newsletter Issue 8, December 2019 PwC | 10
Claim analytics
An introduction to (silent) cyber riskLegacy policy wording might expose you to non-affirmative cyber risk
As described in PwC’s most recent CEO survey, 30% of CEOs see cyber as one of 2019’s top ten threats to their organisation’s growth prospects.1 Specifically for the insur-ance industry, cyber risk emerged as the number one risk of the industry’s risk register because it reflects both the anxiety of underwriting a risk that is constantly changing and the rising threat to insurers themselves.2
Insurance professionals draw a distinction between two types of cyber risk: affirmative and non-affirmative, also known as silent cyber risk. Affirmative cyber risk concerns in-surance policies that explicitly include coverage for cyber risk (ie, standalone products). Silent cyber risk relates to non-cyber insurance policies that do not explicitly include or exclude coverage for cyber risk. As these risks are not explicitly included or excluded in the policy, they are referred to as “silent”.3 As such, ambiguous wording might in practice (as an outcome of litigation) mean these risks are covered.
In this article, we will explore cyber risk and silent cyber risk, and examine first steps insurers can take to tackle silent cyber.
Cyber
Due to recent major cyber-events, organisations are increasingly becoming aware of the potential threats to their business, as well as associated risk mitigation and cyber security techniques. Headline-generating incidents show that cyber threats are becom-ing more sophisticated and aggressive.4
Potential consequences of cyber attacks for organisations include intellectual property theft, data and software loss, cyber extortion, cybercrime, breaches of privacy, network failures, business interruptions, loss of market share, and impact on reputation.5 As such, cyber insurance has become an instrument to (partially) manage these conse-quences. In the past, first-party claims exhausted coverage limits of entire cyber insur-ance programmes, but changes in policy conditions driven by insurance brokers have more recently led to increasing limits and removal of sub-limits. These changes have also led to a shift in cyber underwriters’ exposure, from mainly first-party dominated business more towards more slowly developing third-party claims.
In addition, the cyber insurance market has seen an inflow of new entrants. This has probably been caused by favourable market-wide combined ultimate loss ratios. This has created extra capacity and has led to a softening market. In this soft market environ-ment, overall limits have further increased. For instance, sub-limits like (contingent) busi-ness interruption ([C]BI) limits are increasing or no longer included in contracts.4
Beside the changes in the industry, cyber is not merely seen as a technology risk any more. It is now also a reputational and systemic risk.2 Given its increasingly systemic and extreme nature, it is unclear whether this risk is appropriately priced. For instance, the fear of potentially systemic impacts was indicated in PwC’s 2018 global cyber insurance survey, in which participants stated that they were most worried about a scenario involv-ing cyber risk related to (C)BI.6
1 Cf. PwC, 22nd Annual Global CEO Survey, 2019.2 Cf. PwC, Uncharted waters: Tackling reinsurers’ riskiest exposures, 20173 Cf. Prudential Regulation Authority, Cyber insurance underwriting risk, 2017.4 Cf. PwC, global cyber insurance survey, 2018.5 Cf. London Engineering Group, Cyber Risks – PD/BI Coverage in Industrial Property Cyber
Exposure for Power, Energy and Project risk, 2016.6 Cf. PwC, global cyber insurance survey, 2018.
Contacts
James Norman [email protected]
Jan Wirfs [email protected]
Lars [email protected]
PwC Actuarial Services Newsletter Issue 8, December 2019 PwC | 11
Silent cyber
In addition to cyber risk in affirmative cyber policies, insurers are also exposed towards cyber risk in their (legacy) general liability books. This raises concerns not just in the insurance industry, but also among regulators. The Prudential Regulation Authority (PRA) at the Bank of England indicates that in-surers need to develop a better understanding of silent cyber risk management, risk appetite and strat-egy. Insurers acknowledge the urgency of having formalised risk appetites and a board-agreed strategy for silent cyber risk.7 This then raises the question of how this should be implemented.
The PRA has defined some recommenda tions,8 which are further discussed below: • Modifying policies to reflect the additional risk and offer explicit (limited) cover (affirmative cyber)• Introducing effective wording exclusions
Identify suspect wording, then translate to portfolio impact
As with any issue in life, it is always a good approach to first assess the size of an issue. For silent cyber, we suggest starting by having insurance experts and lawyers scrutinise legacy policies and endorse-ments for suspect wording. Once suspect sections are identified, translating this to the financial impact on portfolio level is key. A possible method might be based on advanced analytics techniques such as data mining applications, which can be trained to go through original scans of signed policies and en-dorsements to identify prevalence of suspect wording in portfolios. Based on a better understanding of their silent cyber exposure, companies then should define the best mode of action. We will explore two solutions: 1) (limited) affirmative cyber coverage and 2) exclusions.
Affirmative cyber
The first option we will explore here is to accept the risk as part of the policy, and then adequately price for it. The key challenge here is how to put a price tag on this additional piece of risk. Traditionally, pric-ing of insurance products relies on historical data. As cyber risk is a relatively new phenomenon, histori-cal data is scarce. Furthermore, cyber risk is a dynamic risk evolving over time due to technical progress and the use of novel systems and devices. This complicates using historical data for the prediction of future cyber losses. Additionally, data that is available may no longer be representative of insured future events, eg, due to increasing (sub-)limits for (C)BI coverage.
The majority of companies in a survey among underwriters of cyber insurance, published by PwC in 2018, indicate they are writing business at or above their desired level of profitability. The majority of survey respondents reported an aggregate company combined ratio of below 90% for their standalone
7 Cf. Prudential Regulation Authority, Cyber underwriting risk follow-up survey results, 2019.8 Cf. Prudential Regulation Authority, Cyber insurance underwriting risk, 2017.
In view of the strong appetite for developing cyber insur-ance business, it is important that cyber risk is reflected in firms’ strategy and risk appetite statements. Firms should exercise caution and introduce robust capacity and uti-lisation controls in cyber underwriting. The overall cyber underwriting strategy and exposure to cyber should be reviewed by the management on at least an annual basis.
PwC insight
Carriers should express caution when relying on any cyber data, whether internal data or data from external vendors, as the data quality and granularity is highly variable and it is almost certain that the cyber threats of the future will be different from those of the past.
Business interruption (BI) cover is the main protection that insured parties are seeking, yet cyber-related BI claims data is extremely sparse. The analysis of claim data is fur-ther complicated by the ever-changing threat profile from the constant evolution of attack methods, rapid technolo-gy changes and changes in policy coverage, meaning that data may be outdated or irrelevant by the time carriers start using them in underwriting and modelling.
PwC insight
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policies, suggesting that cyber, as a business, is more profitable than the average line of business. While the combined ratios reported in the survey suggest healthy profitability of the line, new market entrants and competition are likely to erode profitability.
Furthermore, the industry has yet to experience a major cyber catastrophe loss. As such, it is still not known whether the risk is well-priced for its true profitability and it is uncertain how reserves and claims will develop over time, given the potentially systemic and extreme nature of cyber attacks, which could have a significant impact on profitability.9
The inevitable market-turning event will separate carriers that have sufficient risk management, under-writing processes and capital in place from those that do not.9
Exclusions
The second option we will explore is to change policy wording to exclude coverage for specific events. This is complex, as an important challenge is to find a way to put in words exactly which risks should not be covered. As such, the first issue is whether policy wording adequately describes what is covered under the contract. An example of this is whether state-sponsored cyber events should be covered or not, as it is not always clear whether an event is state-sponsored. In this example, it is difficult to make the distinction. More importantly, even in a case where an insurer has clearly identified wording to exclude coverage, the effectiveness of those exclusions in practice might only be known after they have been tested in court or in arbitrage proceedings.10
Conclusion
The cyber market presents a significant opportunity for insurers to profit in a soft market. However, insurers need to be aware of the significant risks and downside potentials to writing this kind of busi-ness. One of these is silent cyber, which is inherent in insurance policies that do not explicitly include or exclude coverage for cyber risk. Options to deal with (silent) cyber risk are modifying the policies to reflect the additional risk and offer explicit (limited) cover (affirmative cyber), or introducing effective wording to exclude coverage.
As far as affirmative cyber is concerned, it is important to underwrite risks at the right price. This means that proper underwriting guidelines have to be in place, and pricing models need to be as robust and re-liable as possible. Due to the continuously changing nature of cyber threats, a scenario-based modelling approach is crucial. Model validation and robust model risk management processes also are vital for a model to remain appropriate and sustainable in the face of constantly changing threats. Moreover, we expect regulators to require more robust validation of cyber modelling in the future. To cope with these increased requirements, internal validation teams may need training on this new and specialised area.
9 Cf. PwC, global cyber insurance survey, 2018.10 Cf. Swiss Re, cyber reinsurance, 2018.
The majority of cyber underwriters currently keep reserving and pricing activities in-house. However, the limitation in past data and uncertain-ties in accumulation risk lead to a wide range of results using stand-ard actuarial methods. Companies need to understand and address the limitations in the standard actuarial methods in pricing and reserving for both affirmative and silent cyber.
PwC insight
At the current stage of development of cyber modelling, there is high un-certainty surrounding the modelling of cyber, as well as defining a “cyber event”. As such, there is a greater appetite for proportional reinsur-ance, as reinsurers rely on cedants’ underwriting expertise to create an alignment of interest. As quantifica-tion capabilities mature, we expect to see more diversified capacity coming into the market, including insurance-linked securities.
At the current stage of development of cyber modelling, there is high un-certainty surrounding the modelling of cyber, as well as defining a “cyber event”. As such, there is a greater appetite for proportional reinsur-ance, as reinsurers rely on cedants’ underwriting expertise to create an alignment of interest. As quantifica-tion capabilities mature, we expect to see more diversified capacity coming into the market, including insurance-linked securities.
PwC insight
Typical wordings of cyber policies are not yet sufficiently standardised. We note the need for brokers and other insurance bodies to start con-verging on this matter, especially as the current market is still dominated by broker placement. As the market matures and providers build up direct relationships with their clients we expect this reliance to decrease.
PwC insight
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An introduction to (silent) cyber risk
This publication is intended to be a resource for our clients, and the information therein was correct to the best of the authors’ knowl-edge at the time of publication. Before making any decision or taking any action, you should consult the sources or contacts listed here. The opinions reflected are those of the authors. This material may not be reproduced in any form, copied onto microfilm, or saved and edited in any digital medium without the express permission of the publisher.
© 2019 PricewaterhouseCoopers B.V. All rights reserved. PwC refers to the PwC network and/or one ore more of it’s member firms each of which is a separate legal entity. Please see www.pwc.com/structure for further details.
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