EIOPA : Sound Regulation in an Evolving Landscape

Regulation is only effective for as long as it remains relevant. While EIOPA is evolving into a supervisory-focused organisation, it pays close attention to how regulation is applied and how effective it remains, with a view to reinforcing cross-sectoral consistency and improving fairness and transparency and with a focus on better and smart regulation.

INSURANCE

  • SOLVENCY II REVIEW

Since the successful implementation of Solvency II Directive in 2016, EIOPA played an important role in monitoring its consistent implementation and during 2018 was able to provide valuable input into preparations for its review.

EIOPA provided advice to the European Commission on the review of the Solvency Capital Requirement based on an in-depth analysis of 29 different elements of the Standard Formula. The advice focused on increasing proportionality, removing unjustified constraints to financing the economy and removing technical inconsistencies.

EIOPA proposed further simplifications and reduced the burden to insurers by:

  • Further simplifying calculations for a number of sub-modules of the Solvency Capital Requirement (SCR) such as natural, man-made and health catastrophes, in particular fire risk and mass accident;
  • Simplifying the use of external credit ratings in the calculation of the SCR (an issue especially relevant for small insurers);
  • Reducing the burden of the treatment of lookthrough to underlying investments;
  • Developing simplifications in the assessment of lapse and counterparty default risks;
  • Recommending the use of undertaking specific parameters for reinsurance stop-loss treaties.

Furthermore, one of the major technical inconsistencies found related to the calculation of interest rate risk, which did not capture very low or even negative interest rates. EIOPA recommended to adjust the methodology using a method already adopted by internal model users and, given the material impact on capital requirements, suggested to implement it gradually over three years.

EIOPA also carried out an analysis of the loss-absorbing capacity of deferred taxes practices. In face of the evidence of wide diversity, especially concerning the projection of future profits, EIOPA proposed a set of key principles that will ensure greater convergence and level playing field, while maintaining a certain degree of flexibility.

Finally, EIOPA analysed the treatment and the evidence available on unrated debt and unlisted equity and proposed criteria for a more granular treatment, namely with the use of financial ratios.

In some areas, the analysis of recent developments did not provide for sufficient reasons to change. This is, for example, the case of mortality and longevity risks and the cost of capital in the calculation of the risk margin. The evolution of financial markets does not justify a change in the cost of capital: the decrease in interest rates has not lead to a decrease in the cost of raising equity.

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  • REPORTING ON THE IMPLEMENTATION OF SOLVENCY II

In 2018, EIOPA published a number of reports related to different aspects of Solvency II.

  • Report on group supervision and capital management

In response to a European Commission’s request for information, EIOPA submitted its Report on Group Supervision and Capital Management of (Re)Insurance Undertakings and specific topics related to Freedom to Provide Services (FoS) and Freedom of Establishment (FoE) under the Solvency II Directive. The report concluded that overall the Solvency II Group supervision regime was operating satisfactorily. The tools developed by EIOPA to further strengthen group supervision and supervision of cross-border issues contributed to further convergence of practices of NCAs’ supervisory practices.

The report also highlighted a number of gaps in the regulatory framework, including issues related to the application of Solvency II requirements for determining scope of insurance groups subject to Solvency II group supervision, the application of certain of these provisions governing the calculation of group solvency in particular where several methods are used, the definition and supervision of intra-group transactions, or the application of governance requirements at group level.

Further, EIOPA’s report emphasised that effective supervision of insurance groups will benefit from a harmonised approach on a number of areas, for example, early intervention, recovery and resolution and the assessment of group own funds.

  • Second annual report on the use of capital addons under Solvency II

In December 2018, EIOPA published its second annual report on the use of capital add-ons by NCAs according to Article 52 of Solvency II. The objective was to contribute to a higher degree of supervisory convergence in the use of capital add-ons between supervisory authorities and to highlight any concerns regarding the capital add-ons framework. In general, the capital add-on appears to be a good and positive measure to adjust the Solvency Capital Requirement to the risks of the undertaking, when the application of other measures, for example the development of an internal model, is not adequate.

  • Third annual report on the use of limitations and exemptions from reporting under Solvency II

This report, published in December 2018, addresses the proportionality principle on the reporting requirements, from which the limitations and exemptions on reporting – as foreseen in Article 35 of the Solvency II Directive – are just one of the existing proportionality tools. Reporting requirements also reflect a natural embedded proportionality and in addition, risk-based thresholds were included in the reporting Implementing Technical Standard (ITS).

  • Third annual report on the use and impact of long-term guarantee measures and measures on equity risk

This is a regular report published in accordance with Article 77f(1) of the Solvency II Directive. This year’s report also included an analysis on risk management aspects in view of the specific requirements for LTG measures set out in Article 44 and 45 of the Directive as well as an analysis of detailed features and types of guarantees of products with long-term guarantees.

This report shows that – as in previous years – most of the measures, in particular the volatility adjustment and the transitional measures on technical provisions are widely used. The average Solvency Capital Requirement (SCR) ratio of undertakings using the voluntary measures is 231 % and would drop to 172 % if the measures were not applied. This confirms the importance of the measures for the financial position of (re)insurance undertakings.

  • INVESTIGATING ILLIQUID LIABILITIES

The treatment of long-term insurance business remains a hotly debated issue. In particular, it has been discussed whether the risks of long-term insurance business and the associated investments backing those long-term insurance business are adequately reflected. The illiquidity characteristics of liabilities may contribute to the ability of insurers to mitigate short-term volatility by holding assets throughout the duration of the commitments, even in times of market stress.

To explore any new evidence on the features of liabilities, especially concerning their illiquidity characteristics, a dedicated EIOPA Project Group on illiquid liabilities was set up with the following main goals:

  1. To identify criteria of liquidity characteristics for the liabilities and measures for insurers’ ability to invest over the long term;
  2. To explore the link between the characteristics of liabilities and the management of insurers’ assets;
  3. To analyse whether the current treatment in the regulatory regime appropriately addresses the risks associated with the long-term nature of the insurance business.

Following a request for information from the European Commission on asset and liability management, EIOPA launched a request for feedback on illiquid liabilities in autumn and held a roundtable with interested stakeholders in December to discuss the submitted responses on illiquidity measurements and asset liability management practices.

  • ANALYSIS OF THE INTERNATIONAL FINANCIAL REPORTING STANDARDS (IFRS) 17 INSURANCE CONTRACTS

Following the publication of International Financial Reporting Standards (IFRS) 17 Insurance Contracts by the International Accounting Standards Board (IASB), EIOPA assessed its potential effects on financial stability and the European public good, on product design, supply and demand of insurance contracts, and the practical implementation in light of the applicable inputs and processes for Solvency II.

EIOPA concluded that the introduction of IFRS 17 can be described as positive paradigm shift compared to its predecessor IFRS 4 Insurance Contracts, bringing increased transparency, comparability and additional insights on insures’ business models. EIOPA, however, noted a few reservations regarding concepts that may affect comparability and relevance of IFRS 17 financial statements.

PENSIONS

EIOPA promotes greater transparency in the European pensions sector. In support of this aim, EIOPA is working to enhance the information available to consumers and supporting pension providers by making clear the expectations, justifications and decisions linked with the information they provide, in particular to prospective members, members and beneficiaries as laid out in Articles 38 – 44 of the EU Directive on the activities and supervision of institutions for occupational retirement provision (IORP II).

  • REPORT ON THE PENSION BENEFIT STATEMENT: GUIDANCE AND PRINCIPLESBASED PRACTICES IMPLEMENTING IORP II

The report presents the outcomes of NCA exchanges of views and assessments of current practices for the implementation of the IORP II Pensions Benefit Statement (PBS) requirement. Based on this investigation, several principles have been identified that will facilitate clear understanding and comparability of statements.

Two proposals are now in further development: a basic PBS and an advanced PBS (containing more detailed information) to meet the PBS goals. These proposals will, as far as possible, take account of the behavioral approach principle be subject to further consumer testing.

  • DECISION ON THE CROSS-BORDER COLLABORATION OF NCAS WITH RESPECT TO IORP II DIRECTIVE

This Decision, published in November 2018, replaces the former Budapest Protocol which had to be revised as a result of the new IORP II Directive. The Decision introduces new rules to improve the way occupational pension funds are governed, to enhance information transparency to pension savers and to clarify the procedures for carrying out cross-border transfers and activities.

The Decision also describes different situations and possibilities for NCAs to exchange information about cross-border activities in relation to the ‘fit and proper’ assessment and the outsourcing of key functions, both new provisions of the IORP II Directive in addition to the cross-border transfer.

PRESERVING FINANCIAL STABILITY

As part of EIOPA’s mandate to safeguard financial stability, EIOPA works to identify trends, potential risks and vulnerabilities that could have a negative effect on the pension and insurance sectors across Europe.

  • 2018 INSURANCE STRESS TEST

EIOPA published the results of its stress test of the European insurance sector in December 2018. This exercise assessed the participating insurers’ resilience to the three severe but plausible scenarios: a yield curve up shock combined with lapse and provisions deficiency shocks; a yield curve down shock combined with longevity stress; and a series of natural catastrophes.

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In total, 42 European (re)insurance groups participated representing a market coverage of around 75 % based on total consolidate assets. EIOPA published for the first time the post-stress estimation of the capital position (Solvency Capital Requirement ratio) of major EU (re)insurance groups.

Overall, the stress test confirmed the significant sensitivity to market shocks combined with specific shocks relevant for the European insurance sector. On aggregate, the sector is adequately capitalised to absorb the prescribed shocks. Participating groups demonstrated a high resilience to the series of natural catastrophes tested, showing the importance of the risk transfer mechanisms, namely reinsurance, in place.

An additional objective of this exercise, stemming from recommendations from the European Court of Auditors, was to increase transparency in order to reinforce market discipline by requesting the voluntary disclosure of a list of individual stress test indicators by the participating groups. Since EIOPA does not have the power to impose the disclosure of individual results, participating groups were asked for their voluntary consent to the publication of a list of individual stress test indicators. Only four of the 42 participating groups provided such consent.

  • RISK DASHBOARD

EIOPA publishes a risk dashboard on a quarterly basis and a financial stability report twice a year. In the December 2018 report, EIOPA concluded:

  1. the persistent low yield environment remains challenging for insurers and pension funds;
  2. the risk of a sudden reassessment of risk premia has become more pronounced over recent months amid rising political and policy uncertainty;
  3. interconnectedness with banks and domestic sovereigns remains high for European insurers, making them susceptible to potential spillovers;
  4. some European insurers are significantly exposed in their investment portfolios to climate-related risks and real estate.
  • FINANCIAL STABILITY REPORT

EIOPA published two reports on the financial stability of the insurance and occupational pensions sector in 2018.

In general the persistent low yield environment remains challenging for both the insurance and pension fund sector, which continues to put pressure on profitability and solvency. However, towards the end of the year, as noted in the December report, the risk of a sudden reassessment of risk premia became more pronounced. This is largely due to rising political uncertainty and trade tensions, concerns over debt sustainability and the gradual normalisation of monetary policy. In the short run a sudden increase in yields driven by rising risk premia could significantly affect the financial and solvency position of insurers and pension funds as the investment portfolios could suffer large losses only partly offset by lower liabilities. In this regard, the high degree of interconnectedness with banks and domestic sovereigns of insurers could lead to potential spillovers in case a sudden reassessment of risk premia materialise.

While overall the insurance sector remains adequately capitalised, profitability is under increased pressure in the current low yield environment. The Solvency Capital Requirement ratio for the median company is 225 % for life and 206 % for non-life insurance sector, although significant disparities remain across undertakings and countries.

In the European occupational pension fund sector, total assets increased for the euro area and cover ratios slightly improved. However, the current macroeconomic environment and ongoing low interest rates continue to pose significant challenges to the sector, with the weighted return on assets considerably down in 2017.

  • ENHANCED INFORMATION AND STATISTICS

EIOPA continuously works to improve the availability and quality of available information and statistics on insurance and pensions.

  • Solvency II information

For the insurance sector, EIOPA publishes high-quality insurance statistics at both solo and group level. The statistics are based on Solvency II information from regulatory reporting and their regular publication demonstrates EIOPA’s commitment to transparency. Over the past year, through the increased availability of Solvency II data EIOPA has been able to increase the coverage of its statistics. In June 2018, for the first time, the Authority published further insight into the assets of solo (re)insurance undertakings at country level.

  • Decision on EIOPA’s regular information requests towards NCAs regarding provision of occupational pensions information

In April 2018, the Authority published its decision regarding the submission of occupational pension information. The decision defined a single framework for the reporting of occupational pension information that facilitates reporting processes. As a result, EIOPA will receive the information required to carry out appropriate monitoring and assessment of market developments, as well as in-depth economic analyses of the occupational pension market. The requirements were developed in close cooperation with the European Central Bank in order to minimise the burden on the industry and will apply as of 2019.

  • Pensions information taxonomy

In November 2018, EIOPA published the eXtensible Business Reporting Language (XBRL) Taxonomy applicable for reporting of information on IORPs. It provides NCAs with the technical means for the submission to EIOPA of harmonised information of all pension funds in the European Economic Area. Developed in close collaboration with the European Central Bank (ECB), it allows for integrated technical templates and means to report via a single submission both the information required by EIOPA and the ECB.

CRISIS PREVENTION

In addition to regular financial stability tools, EIOPA undertooka number of additional activities in 2018 related to crisis prevention.

  • Development of a macroprudential framework for insurance

With the aim of contributing to the overall debate on systemic risk and macroprudential policy, over the last year, EIOPA has published a series of reports that extend the debate to the insurance sector and, more specifically, the characteristics of that sector. These reports cover the following:

  1. Systemic risk and macroprudential policy in insurance;
  2. Solvency II tools with macroprudential impact; and
  3. Other potential macroprudential tools and measures to enhance the current framework.

As a next step, EIOPA will consult on concrete proposals to include macroprudential elements in the upcoming review of Solvency II.

  • Analysis of the causes and early identification of failures and near misses in insurance

In July 2018, EIOPA published ‘Failures and near misses in insurance: Overview of the causes and early identification’ as the first in a series aimed at enhancing supervisory knowledge of the prevention and management of insurance failures. The report’s findings are based on information contained in EIOPA’s database of failures and near misses, covering the period from 1999 to 2016, including sample data of 180 affected insurance undertakings in 31 European countries.

The report focuses on an examination of the causes of failure in insurance, as well as the assessment of the reported early identification signals. It also examines the underlying concepts ‘failure’ and ‘near miss’ as well as providing further information on EIOPA’s database, established in 2014.

Click here to access EIOPA’s 2018 Annual Report

Can Data and Technology Support the Insurance Industry to Regain Lost Relevance?

Since the start of the Third Industrial Revolution in the 1980s, the world has changed in many different ways:

  • rapid introduction and adoption of technological innovation (global internet; social networks; mobile technologies; evolving payment solutions; data availability);
  • new economic realities (volatile and shorter economic cycles; interconnected financial climate; under utilisation of assets);
  • structural shifts in society’s values (desire for community; generational altruism; active citizenship);
  • and demographic readjustment (increasing population; urbanization; longer life expectancy; millennials in the work force).

While these changes have been happening, the Insurance industry has seemingly preferred to operate in a closed environment oblivious to much of the impact these changes could bring:

  • Resistance to change,
  • Failure to meet changing customer demands
  • Decrease in the importance of attritional risks

has led the Insurance industry to reduce its relevance.

However

  • the availability of data,
  • the introduction of new capital providers,
  • the impact of new business models emerging from the sharing economy
  • and the challenge of InsurTechs

are affecting the industry complacency. Collectively, these factors are creating the perfect storm for the incumbents allowing them to re-evaluate their preference for maintaining the status quo. There is an ever increasing expectation from the industry to be more innovative and deliver a vastly improved customer experience.

As data and emerging technology are accelerating the need for change, they are also opening doors. The industry is at cross roads where it can either choose to regain relevance by adapting to the new world order or it can continue to decline. Should it choose the latter, it could expose the US$ 5 trillion market to approaches from large technology firms and manufacturers who have the access to customers, transformational capabilities and more than enough capital to fill the void left by the traditional players.

Insurance industry is slow to evolve

The Insurance industry has historically lacked an appetite to evolve and has shown reluctance in adopting industry-wide changes. A number of key elements, have created high barriers to entry. New entrants have found it difficult to challenge the status quo and lack appetite to win market share from incumbents with significantly large balance sheets. Such high barriers have kept the impact of disruption to minimal, allowing the industry to stay complacent even when most other industries have undergone significant structural shifts. In many ways ‘Darwin’ has not been at work.

  • A complex value chain

The Insurance industry started with a simple value chain involving four roles – the insured, a broker who advices the insured, an underwriter who prices the risk and an investor who provides the capital to secure the risk. Over centuries, the chain has expanded to include multiple other roles essential in helping the spreading of large risks across a broad investor community, as shown below.

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These new parties have benefitted the chain by providing expertise, access to customers, secure handling of transactions, arbitration in case of disputes and spreading of risk coverage across multiple partners. However, this has also resulted in added complexities and inefficiencies as each risk now undergoes multiple handovers.

While a longer value chain offers opportunities to new entrants to attack at multiple points, the added complexities and the importance of scale reduces opportunities to cause real disruption.

  • Stringent regulations

Insurance is one of the highest regulated industries in the world. And since the global financial crisis of last decade, when governments across the globe bailed out several financial service providers including insurers, the focus on capital adequacy and customer safety has increased manifold.

While a proactive regulatory regime ensures a healthy operating standard with potential measures in place to avoid another financial meltdown, multiple surveys have highlighted the implications of increased regulatory burden, leading to increased costs and limited product innovation.

  • Scale and volatility of losses

The true value of any insurance product is realised when the customer receives payments for incurred losses. This means that insurers must maintain enough reserves at any time to meet these claims.

Over the years volatility in high severity losses have made it difficult for insurers to accurately predict the required capital levels.

In addition, regulators now require insurers to be adequately capitalised with enough buffer to sustain extreme losses for even the lowest probability of occurrence (for example 1-in-100 years event or 1-in-200 years event). This puts additional pressure on the insurers to maintain bulky balance sheets.

On the other hand, a large capital base gives established insurers advantage of scale and limits growth opportunities for smaller industry players/new entrants.

  • Need for proprietary and historical data

Accurate pricing of the risk is key to survival in the industry. The insurers (specifically underwriters supported by actuaries) rely excessively on experience and statistical analysis to determine the premiums that they would be willing to take to cover the risk.

Access to correct and historical data is of chief importance and has been a key differentiating factor amongst insurers. Since the dawn of Third Industrial Revolution in the 1980s, insurers have been involved in a race to acquire, store and develop proprietary databases that allow them to price risks better than the competitors.

The collection of these extensive databases by incumbent insurers have given them immense benefits over new entrants that do not typically have similar datasets. Additionally, the incumbents have continued to add on to these databases through an unchallenged continuation of underwriting– which has further widened the gap for new entrants.

Struggling to meet customer needs

Despite years of existence, the Insurance industry has failed to keep up with the demand for risk coverage. For example the economic value of losses from all natural disasters has consistently been more than the insured value of losses by an average multiple of 3x-4x.

The gap is not limited to natural disasters. As highlighted by Aon’s Global Risk Management Survey 2019, multiple top risks sighted by customers are either uninsurable or partially insurable leading to significant supply gap.

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Six of the top 10 risks, including Damage to reputation/brand and Cyber, require better data and analytical insights to achieve fully effective risk transfer. However, current capabilities are primarily applied to drive better pricing and claims certainty across existing risk pools, and have not yet reached their full potential for emerging risks.

This inability to meet customer need has been driven by both an expensive model (for most risks only 60% of premiums paid are actually returned to the insured) and a lack of innovation. Historically, the need for long data trends meant insurance products always trailed emerging risks.

Status Quo is being challenged

While the industry has been losing relevance, it is now facing new challenges which are creating pressure for change. While these challenges are impacting the incumbents they also provide the potential for insurance to regain its key role in supporting innovation. Creating opportunity for lower costs and new innovations.

The insurance customer landscape has changed considerably: traditional property and casualty losses are no longer the only main risks that corporations are focused on mitigating. The importance of intellectual property and brand/reputation in value creation is leading to a realignment in the customer risk profile.

Value in the corporate world is no longer driven by physical/ tangible assets. As technology has advanced, it has led to the growth of intangibles assets in the form of intellectual property. The graph below shows that 84% of market capitalization in 2018 was driven by intangible assets. While the five largest corporations in 1975 were manufacturing companies (IBM; Exxon Mobil; P&G; GE; 3M), that has completely changed in 2018 as the first five positions were occupied by Tech companies (Apple; Alphabet; Microsoft; Amazon; Facebook). Yet, organizations are only able to secure coverage to insure a relatively small portion of their intangible assets (15%) compared to insurance coverage for legacy tangible assets (59%).

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This shift represents both a challenge and an opportunity for the Insurance industry. The ability to provide coverage for intangible assets would enable insurance to regain relevance and support innovation and investment. Until it can, its importance is likely to remain muted.

InsurTech

The Insurance industry has had traditionally manual processes, and has been a paper driven industry with huge inefficiencies. While customers´ needs are evolving at an unprecedented quick pace, the incumbents´ large legacy systems and naturally conservative approach, make them slow to reach the market with new products and an improved customer experience.

InsurTechs are companies that use technology to make the traditional insurance value chain more efficient. They are beginning to reshape the Insurance industry by targeting particular value pools or services in the sector, rather than seek to provide end-to-end solutions.

InsurTechs have seen more than US$ 11 billion of funding since 2015, and the volume in 2018 is expected to reach US$ 3,8 billion (FT PARTNERS). While Insurtechs were originally viewed as a disruptive force competing with traditional insurers to gain market share, there is a growing collaboration and partnership with the incumbent players. Most of them are launched to help solve legacy insurer problems across the organization, from general inefficiency in operations to enhancing underwriting, distribution, and claims functions, especially in consumer facing insurance. More recently they are also moving into the commercial segment focusing on loss prevention and efficiency. (CATLIN, T. et al. 2017). Incumbent insurers have managed to leverage InsurTechs to speed up innovation (DELOITTE, 2018: 11). From a funding perspective most of the US$ 2.6 billion that went into the InsurTechs in the first nine months of 2018 came from incumbent Insurers. (MOODY`S, 2018: 6).

The accelerated use of technology and digital capabilities again represents both a challenge for the industry but also an opportunity to innovate and develop more efficient products and services.

Data and technology with potential to transform

Traditionally, the Insurance industry has used proprietary historic data to match the demand from risk owners with the supply from capital providers. Focusing on relative simplistic regression analysis as the main approach.

While robust, this approach is reliant on a long data history and limits insurers ability to move into new areas. Increasingly the transformative power of data and technology is changing this relationship, as shown in the graph below. While underwriting data used to be in the hands of the incumbents only, emerging technologies, new analytical techniques and huge increases in sensors are enabling usage of new forms of data that are much more freely accessible. In addition, these technologies are supporting instant delivery of in-depth analytics that can potentially lead to significant efficiency gains and new types of products.

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  • Artificial Intelligence

Artificial Intelligence – Robotic Process Automation (RPA) and Cognitive Intelligence (CI) – is know as any system that can perceive the world around it, analyse and understand the information it receives, take actions based on that understanding and improve its own performance by learning from what happended.

Artificial Intelligence not only gives the opportunity to reduce costs (process automation; reduction of cycle times; free up of thousands of people hours) but improves accuracy that results in better data quality. For insurers this offers significant potential to both enable new ways of interpreting data and understanding risks. As well as reducing the costs of many critical processes such as claims assessment.

This dual impact of better understanding and lower costs is highly valuable. Insurers’ spend on cognitive/artificial intelligence technologies is expected to rise 48% globally on an annual basis over five years, reaching US$ 1.4 billion by 2021. (DELOITTE, 2017: 15).

  • Internet of Things

The Internet of Things refers to the digitization of objects around us. It works by embedding advanced hardware (e.g. sensors, cameras and meters) into everyday objects and even people themselves, linking those objects further to online networks. (MOODY`S, 2018: 11).

For example, connected devices in the homes such as water leakage detectors, smoke alarms, C02 readers and sophisticated home security systems will support prevention and reduction in losses from water damage, fire and burglary, respectively.

The Internet of Things has the potential to significantly change the way that risks are underwritten. The ability to have access to data in ‘real time’ will provide greater precision in the pricing of risk and also help insurers to respond better to the evolving customer needs. Consider the example of home insurance; customers will be forced to resconsider the decision to buy home insurance as packaged currently when their house is already monitored 24/7 for break-ins and the sensors are constantly monitoring the appliances to prevent fires. The insurers could utilise the same data to develop customised insurance policies depending on usage and scope of sensors.

The Internet of Things applies equally to wearable devices with embedded sensors for tracking vital statistics to improve the health, safety and productivity of individuals at work. It is predicted that the connected health market will be worth US$ 61 billion by 2026.

The Internet of Things offers the Insurance industry an opportunity to reinvent itself and to move from simply insuring against risk to helping customers protect the properties / health. This integration of insurance with products through live sensor data can revolutionise how insurance is embedded into our every day lives.

  • Blockchain

All disruptive technologies have a “tipping point” – the exact moment when it moves from early adopters to widespread acceptance. Just as it was for Google in the late 1990s and smartphones in the 2000s, could we be approaching the tipping point for the next big disruptive technology – blockchain?

Essentially, blockchain is a shared digital ledger technology that allows a continuously growing number of transactions to be recorded and verified electronically over a network of computers. It holds an immutable record of data, stored locally by each party to remove the barrier of trust. Through smart contacts, blockchain can enable automation of tasks for more efficient processing. It made its debut in 2009 as the system used to track dealing in the first cryptocurrency, Bitcoin, and, since then, organisations around the world have spotted blockchain’s potential to transform operations.

Most industries are currently experimenting with blockchain to identify and prove successful use cases to embrace the technology in business as usual. IDC, a leading market intelligence firm, expects the spend on blockchain to increase from US$ 1.8 billion in 2018 to US$ 11.7 billion in 2022 at a growth rate of 60%.

With all the aforementioned benefits, blockchain also has potential to impact the Insurance industry. It can help Insurers reduce operational and administrative costs through automated verification of policyholders, auditable registration of claims and data from third parties, underwriting of small contracts and automation of claims procedures. Equally, it can help reduce the fraud which would contribute to reduce total cost.

In an industry where ‘trust’ is critical, the ability to have guaranteed contracts, with claims certainty will help the take-up of insurance in new areas. BCG estimates that blockchain could drastically improve the end-to-end processing of a motor insurance policy and any claims arising thereof as shown in the graph below.

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Conclusion

The relevance of insurance, which has declined over the last few decades, after peaking in the early 1980s, is set to increase again:

  • Big shifts in insurance needs, both in the commercial and consumer segments,
  • New sources of cheap capital,
  • Prevelance of cheap and accessible data and the technology to automate and analyse

will transform the Insurance industry.

Not only is this important for insurers, it is also important for all of us. Insurance is the grease behind investment and innovation. The long term decline in the Insurance´s industry ability to reduce risk could be a significant impediment on future growth.

However we believe that the reversal of this trend will mean that insurance can once again grow in its importance of protecting our key investments and activities.

Click here to access Aon’s White Paper

 

Marketing’s Role in Employee & Customer Experience Journeys

Is your Marketing department aligned with customer experience and employee experience? The necessity and logic of doing this was highlighted in a recent presentation by Hootsuite’s Vice President of Customer, Kirsty Traill. She pointed out that Marketing Communications is unfortunately the typical focus of customer journey maps and customer-centric marketing.

Her observations are in accordance with the first half of this six-part series which also pointed out that MarCom-focus for customer-centric marketing is extremely short-sighted in what’s needed by your company. It short-changes marketing’s impact.

Hootsuite takes a holistic view of “brand experience” by applying customer-centric research and thinking to each phase of the end-to-end customer experience and employee experience journey maps — for use by all groups within Marketing and beyond. Brand integrity relies upon both employees’ and customers’ perceptions. It also relies on the company’s fulfillment of their needs. Marketing plays a significant role in understanding, communicating and assuring these needs.

“We recognize the importance of employee engagement in driving the customer experience,” said Kirsty. “Marketing touches every part of the employee journey and is a key part of driving a truly customer-centric culture, starting with recruiting and whether the public’s image of our employer brand is likely to attract high-caliber talent.”

The journey team at Hootsuite includes Marketing, Sales and Customer Success representatives. This allows them to look through different lenses. Their work has developed an overarching messaging hierarchy informed by customer journey mapping, and grounded in customer needs. “It’s an overall guide of how customers talk about the category,” explained Kirsty. “It describes how customers and employees think about each phase of their journeys, and how they talk about their needs. It provides vocabulary for consistent messaging to each of four core customer personas and to employees.”

Marketing decisions are guided by a table of customer insights available for each journey stage, showing which voice-of-customer insights inform each stage and who owns it. Julie Garrah, Customer Experience Manager on Kirsty’s team at Hootsuite, explained: “We emphasize closing-the-loop in communicating what action we’re taking. This drives improvement in scores. We send customers a closing-the-loop email on a six-month cadence, sharing what we’re doing.”

The image below describes the interpretation. Green phrasing is the suggestion to foster outside-in thinking.

Research for Marketing Across the Customer Experience Journey

Hootsuite has defined four core personas and developed a customer journey map for each persona. (identify natural customer segments by looking for patterns across qualitative data) Hootsuite builds a deep understanding of each segment’s journey stages by answering these questions:

  • Need Something: How does a customer become aware of the need for what your category represents, how would they describe the need in their own words, what is it that triggers the activation of that customer need?
  • What are My Choices: Which other companies are in your customers’ consideration set, where are they finding information to make a decision in the category, what is their evaluation criteria?
  • Decide & Buy: What information are they looking for to make their decision, what is it that locks them in to your product versus your competition’s, do they talk to anyone, what does your purchase process look like, how long does it take, how easy was it for customers relative to their expectations?
  • Receive Order: What do they need to get started, where do they find information during this stage?
  • Install / Use: How do customers use your product/service, how do they define the value, how do you deliver upon that value, how do you reinforce that they’ve made the right decision?
  • Questions / Moments of Truth (1) : Which touch-points triggered repeat purchase, upgrade or expansion; where did you fail to deliver on their expectations; what caused customers to cancel, suspend, return, leave, what were the triggers; what information do they need and in what format?
  • Integrations (2): Which touch-points turn fans into loyal fans and advocates, what is the customers’ context for usage of your product, what are their interactions with your people, what is their connection with your brand?
  1. Questions / Moments of Truth: Researching the “moments of truth” stage can be a difficult process to go through, but Kirsty explained: “This information is rich and can be used in very productive ways for improving customer experience as well as your marketing mix and marketing touch-points.”
  2. Integrations: Integrations might be the most significant part of the journey as it answers “what is the customer trying to get done . . . with or by whom, under what circumstances, in combination with what processes or hardware/software?” This context can be a game-changer for up-leveling your marketing, product development, and operations.

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Research & Actions for Marketing Across the Employee Experience Journey

Hootsuite applies customer experience insights to all stages of the employee experience journey:

  • Need a Job Opportunity / What are My Choices: Does your Careers web page paint the image of a customer-centric company, how employees are portraying you on LinkedIn and GlassDoor, is your employer brand aligned with your corporate brand and customer-focus?
  • Decide & Sign / Start Job: Educate every new employee on buyer personas and user personas in every team and department, show videos from customers explaining what they use and like, provide pocket guide with customer needs and value proposition, explain company standards to new hires so they understand how important customer-focus is and how their specific role affects customer experience.
  • Daily Work: Empower everyone in the company to address customer issues since it’s impossible for your Customer Success team to manage every touch-point customers have with your company, role-based training, create a central repository of customer information and unified customer profile across the journey, design your tech stack to integrate the fewest systems necessary to house customer data for a comprehensive story of individual customers.
  • Higher Purpose: Make “customer love” visible through stories shared with employees, display your Customer Support vision, encourage employees to participate in shadowing, ride-alongs, and capturing customer quotes.
  • Championing: Encourage brand spirit through corporate apparel and swag, empower employees to “share love” through social media, arrange for people from development to shadow Customer Support and Sales Enablement to sit in on sales calls, invite Product Marketing and Vertical Marketing teams to attend customer events to see how customers are interacting and engaging with content.

Hootsuite studies a flow of qualitative data from marketing touch-points about what customers want and need. By gaining a deeper understanding of how customers are thinking and feeling about information at each stage in their journey, the company has also gained appreciation for how the touch-points interact with one another.

These insights re-orient employees’ outlooks. They break down traditional silo mentality. The goal is to become a more customer-centric organization by driving behavior in doing what’s best for the customer as the way to drive business growth.

Click here to access CustomerThink-10-Big-Ideas-Customer-Experience-Success Paper

Data Search and Discovery in Insurance – An Overview of AI Capabilities

Historically, the insurance industry has collected vast amounts of data relevant to their customers, claims, and so on. This can be unstructured data in the form of PDFs, text documents, images, and videos, or structured data that has been organized for big data analytics.

As with other industries, the existence of such a trove of data in the insurance industry led many of the larger firms to adopt big data analytics and techniques to find patterns in the data that might reveal insights that drive business value.

Any such big data applications may require several steps of data management, including collection, cleansing, consolidation, and storage. Insurance firms that have worked with some form of big data analytics in the past might have access to structured data which can be ingested by AI algorithms with little additional effort on the part of data scientists.

The insurance industry might be ripe for AI applications due to the availability of vast amounts of historical data records and the existence of large global companies with the resources to implement complex AI projects. The data being collected by these companies comes from several channels and in different formats, and AI search and discovery projects in the space require several initial steps to organize and manage data.

Radim Rehurek, who earned his PhD in Computer Science from the Masaryk University Brno and founded RARE Technologies, points out:

« A majority of the data that insurance firms collect is likely unstructured to some degree. This poses several challenges to insurance companies in terms of collecting and structuring data, which is key to the successful implementation of AI systems. »

Giacomo Domeniconi, a post-doctoral researcher at IBM Watson TJ Research Center and Adjunct Professor for the course “High-Performance Machine Learning” at New York University, mentions structuring the data as the largest challenge for businesses:

“Businesses need to structure their information and create labeled datasets, which can be used to train the AI system. Yet creating this labeled dataset might be very challenging apply AI and in most cases would involve manually labeling a part of the data using the expertise of a specialist in the domain.”

Businesses face many challenges in terms of collecting and structuring their data, which is key to the successful implementation of AI systems. An AI application is only as good as the data it consumes.

Natural language processing (NLP) and machine learning models often need to be trained on large volumes of data. Data scientists tweak these models to improve their accuracy.

This is a process that might last several months from start to finish, even in cases where the model is being taught relatively rudimentary tasks, such as identifying semantic trends in an insurance company’s internal documentation.

Most AI systems necessarily require the data to be input into an AI system in a structured format. Businesses would need to collect, clean, and organize their data to meet these requirements.

Although creating NLP and machine learning models to solve real-world business problems is by itself a challenging task, this process cannot be started without a plan for organizing and structuring enough data for these models to operate at reasonable accuracy levels.

Large insurance firms might need to think about how their data at different physical locations across the world might be affected by local data regulations or differences in data storage legacy systems at each location. Even with all the data being made accessible, businesses would find that data might still need to be scrubbed to remove any incorrect, incomplete, improperly formatted, duplicate, or outlying data. Businesses would also find that in some cases regulations might mandate the signing of data sharing agreements between the involved parties or data might need to be moved to locations where it can be analyzed. Since the data is highly voluminous, moving the data accurately can prove to be a challenge by itself.

InsIA

Click here to access Iron Mountain – Emerj’s White Paper

 

Financial Risk Management – Global Practice Analysis Report

Survey participants indicated they are involved in the daily practice of financial risk management as financial risk managers, in supervisory roles, as consultants, academics and trainers, auditors and regulators. They self-identified as highly educated — 71 percent hold a Master’s degree or higher. While 61 percent of respondents had more than five year’s experience in the financial services industry, less than half — 41 percent — had more than five year’s experience in financial risk management. This indicates that experienced financial services professionals enter the field of risk management from other areas of responsibility at financial institutions.

GARP1

More than 40 percent of respondents worked at banks, with consulting and asset management firms employing 17 and 16 percent, respectively. Approximately one-third of respondents hold the title of risk manager, one-quarter are analysts and 11 percent are consultants. Approximately 61 percent are employed at firms with more than 1,000 employees.

The GARP Global Practice Analysis survey addressed 49 specific tasks across six process-based domains. Respondents were asked to assign an importance rating from 1 (not important) to 4 (extremely important) to each task. Significantly, all 49 tasks were found to be important on the 4-point Importance Scale, meeting the industry best-practices threshold of 2.5 out of 4. Forty-seven of the 49 tasks received a mean importance rating of at least 3.0, indicating that these tasks are considered of moderate to high importance to the work of financial risk managers.

The top five tasks identified by respondents as most important, earning a mean importance rating of at least 3.3 among all survey respondents, are to:

  1. Identify signs of potential risk based on exposure, trends, monitoring systems regulatory and environmental change, organizational culture and behavior.
  2. Analyze and assess underlying risk drivers and risk interconnections.
  3. Communicate with relevant business stakeholders.
  4. Monitor risk exposure in comparison to limits and tolerances.
  5. Evaluate materiality of risk and impact on business.

The five tasks identified as least important, with a mean importance rating of or below 3.0 among all respondents, are:

  1. Create and inventory of models.
  2. Generate, validate, and communicate standardized risk reports for external purposes.
  3. Develop transparent model documentation for independent replication/validation.
  4. Set capital allocations and risk budgets in accordance with risk management framework.
  5. Recommend policy revisions as necessary.

Respondents were asked to identify at what level of experience each task should be part of the financial risk manager’s profile, according to a five-level Experience Scale:

  • Not necessary
  • Less than 2 years
  • 2 to 5 years
  • 6 to 10 years
  • More than 10 years

One-half of respondents indicated that financial risk managers should be able to perform all 49 tasks within the first five years of practice.

More than 77 percent of respondents said financial risk managers should be able to perform these specific tasks within their first five years of practice in financial risk management:

  • Monitor risk exposure in comparison to limits and tolerances
  • Define and determine type of risk (e.g., credit, market, operational) by classifying risk factors using a consistent risk taxonomy
  • Gather quantitative data to perform model evaluation
  • Select monitoring methods and set frequency (e.g., intra-daily, daily, weekly, monthly)
  • Gather qualitative information to perform model evaluation
  • Generate, validate, and communicate standardized risk reports for internal purposes (e.g., staff, executive management, board of directors)
  • Identify risk owners
  • Investigate why limits are exceeded by performing root-cause analysis
  • Analyze and assess underlying risk drivers and risk interconnections
  • Escalate breach when limits or alert levels are exceeded according to risk management plan/policies/strategies
  • Generate, validate, and communicate ad hoc reports to meet specific requirements
  • Escalate unusual behavior or potential risks according to risk management plan/ policies/strategies

GARP2

Financial risk managers are vital to any integrated financial system of managing and communicating risk. The GPA study is a contemporary and comprehensive description of the work of risk managers across work settings, geographic regions, job roles and experience levels.

The process of a practice analysis is important for programs that desire to continually evolve and reflect the critical knowledge and tasks in the industry. It is important for practitioners who desire to evolve and be successful in their career.

Click here to access GARP’s detailed survey report

 

The State of Connected Planning

We identify four major planning trends revealed in the data.

  • Trend #1: Aggressively growing companies plan more, plan better, and prioritize planning throughout the organization.

  • Trend #2: Successful companies use enterprise-scale planning solutions.

  • Trend #3: The right decisions combine people, processes, and technology.

  • Trend #4: Advanced analytics yield the insights for competitive advantage.

TREND 01 : Aggressively growing companies prioritize planning throughout the organization

Why do aggressively growing companies value planning so highly? To sustain an aggressive rate of growth, companies need to do two things:

  • Stay aggressively attuned to changes in the market, so they can accurately anticipate future trend
  • Keep employees across the company aligned on business objectives

This is why aggressively growing companies see planning as critical to realizing business goals.

Putting plans into action

Aggressively growing companies don’t see planning as an abstract idea. They also plan more often and more efficiently than other companies. Compared to their counterparts, aggressively growing companies plan with far greater frequency and are much quicker to incorporate market data into their plans

This emphasis on

  • efficiency,
  • speed,
  • and agility

produces real results. Compared to other companies, aggressively growing companies put more of their plans into action. Nearly half of aggressively growing companies turn more than three-quarters of their plans into reality.

For companies that experience a significant gap between planning and execution, here are three ways to begin to close it:

  1. Increase the frequency of your planning. By planning more often, you give yourself more flexibility, can incorporate market data more quickly, and have more time to change plans. A less frequent planning cadence, in contrast, leaves your organization working to incorporate plans that may lag months behind the market.
  2. Plan across the enterprise. Execution can go awry when plans made in one area of the business don’t take into account activities in another area. This disconnect can produce unreachable goals throughout the business, which can dramatically reduce the percentage of a plan that gets executed. To avoid this, create a culture of planning across the enterprise, ensuring that plans include relevant data from all business units.
  3. Leverage the best technology. As the statistic above shows, the companies who best execute on their plans are those who leverage cloud-based enterprise technology. This ensures that companies can plan with all relevant data and incorporate all necessary stakeholders. By doing this, companies can set their plans up for execution as they are made.

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TREND 02 : Successful companies use enterprise-scale planning solutions

Although the idea that planning assists all aspects of a business may seem like common sense, the survey data suggests that taking this assumption seriously can truly help companies come out ahead.

Executives across industries and geographies all agreed that planning benefits every single business outcome, including

  • enhancing revenues,
  • managing costs,
  • optimizing resources,
  • aligning priorities across the organization,
  • making strategies actionable,
  • anticipating market opportunities,
  • and responding to market changes.

In fact, 92 percent of businesses believe that better planning technology would provide better business outcomes for their company. Yet planning by itself is not always a panacea.

Planning does not always equal GOOD planning. What prepares a company for the future isn’t the simple act of planning. It’s the less-simple act of planning well. In business planning, band-aids aren’t solutions

What counts as good planning? As businesses know, planning is a complicated exercise,
involving multiple processes, many different people, and data from across the organization. Doing planning right, therefore, requires adopting a wide-angle view. It requires planners to be able to see past their individual functions and understand how changes in one part of the organization affect the organization as a whole.

The survey results suggest that the best way to give planners this enterprise-level perspective is to use the right technology. Companies whose technology can incorporate data from the entire enterprise are more successful. Companies whose planning technology cannot link multiple areas of the organization, or remove multiple obstacles to planning, in contrast, plan less successfully.

Here are three areas of consideration that can help you begin your Connected Planning journey.

  1. Get the right tools. Uncertainty and volatility continue to grow, and spreadsheets and point solutions lack the agility to pivot or accommodate the volumes of data needed to spot risks and opportunities. Consider tools such as cloud-based, collaborative Connected Planning platforms that use in-memory technology and execute real-time modeling with large volumes of data. Not only can teams work together but plans become more easily embraced and achievable.
  2. Operate from a single platform with reliable data. Traditionally, companies have used individual applications to plan for each business function. These solutions are usually disconnected from one another, which makes data unreliable and cross-functional collaboration nearly impossible. A shared platform that brings together plans with access to shared data reduces or altogether eliminates process inefficiencies and common errors that can lead to bad decision-making.
  3. Transform planning into a continuous, connected process. Sales, supply chain, marketing, and finance fulfill different purposes within the business, but they are inextricably linked and rely on each other for success. The ability to connect different business units through shared technology, data, and processes is at the core of a continuous and connected business planning process.

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TREND 03 The right decisions combine people, processes, and technology

As businesses examine different ways to drive faster, more effective decision-making, planning plays a critical role in meeting this goal. Ninety-nine percent of businesses say that planning is important to managing costs. According to 97 percent of all survey respondents,

  • enhancing revenues,
  • optimizing resource allocation,
  • and converting strategies into actions

are all business objectives for which planning is extremely crucial. Eighty-two percent of executives consider planning to be “critically important” for enhancing revenues.

For planning to be successful across an organization, it need to extend beyond one or two siloed business units. The survey makes this clear: 96 percent of businesses state that
planning is important for aligning priorities across the organization. Yet even though companies recognize planning as a critical business activity, major inefficiencies exist: 97 percent of respondents say that their planning can be improved.

The more planners, the merrier the planning

When describing what they could improve in their planning, four components were all named essential by a majority of respondents.

  • Having the right processes
  • Involving the right people
  • Having the right data
  • Having the right technology

To support strong and effective change management initiatives, successful businesses can build a Center of Excellence (COE). A COE is an internal knowledge-sharing community that brings domain expertise in creating, maturing, and sustaining high-performing business disciplines. It is comprised of an in-house team of subject matter experts who train and share best practices throughout the organization.

By designing a Center of Excellence framework, businesses can get more control over their planning processes with quality, speed, and value, especially as they continue to expand Connected Planning technology into more complex use cases across the company.

Here are six primary benefits that a COE can provide:

  1. Maintaining quality and control of the planning platform as use case expands.
  2. Establishing consistency to ensure reliability within best practices and business data.
  3. Fostering a knowledge-sharing environment to cultivate and develop internal expertise.
  4. Enabling up- and downstream visibility within a single, shared tool.
  5. Driving efficiency in developing, releasing, and maintaining planning models.
  6. Upholding centralized governance and communicating progress, updates, and value to executive sponsors.

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TREND 04 Advanced analytics yield the insights for competitive advantage

Disruption is no longer disruptive for businesses—it’s an expectation. Wide-spread globalization, fluid economies, emerging technologies, and fluctuating consumer demands make unexpected events and evolving business models the normal course of business today.

This emphasizes the critical need for a more proactive, agile, and responsive state of planning. As the data shows, companies that have implemented a more nimble approach to planning are more successful.

Planners don’t have to look far to find better insights. Companies who plan monthly or more are more likely to quickly incorporate new market data into their plans—updating forecasts and plans, assessing the impacts of changes, and keeping an altogether closer eye on ongoing business performance and targets.

However, not all companies are able to plan so continuously: Almost half of respondents indicate that it takes them weeks or longer to update plans with market changes. For businesses that operate in rapidly changing and competitive markets, this lag in planning can be a significant disadvantage.

Advancements in technology can alleviate this challenge. Ninety-two percent of businesses state that improved planning technology would provide better business outcomes for their company. The C-Suite, in particular, is even more optimistic about the adoption of improved technology: More than half of executives say that adopting better planning technology would result in “dramatically better” business performance.

Planning goes digital

Rather than planners hunting for data that simply validates a gut-feeling approach to planning, the survey results indicate that data now sits behind the wheel—informing, developing, improving, and measuring plans.

Organizations, as well as a majority of executives, describe digital transformation as a top priority. Over half of all organizations and 61 percent of executives say that digital transformation amplifies the importance of planning. As businesses move into the future, the increasing use of advanced analytics, which includes predictive analytics and spans to machine learning and artificial intelligence, will determine which businesses come out ahead.

Roadblocks to data-driven planning

Increasing uncertainty and market volatility make it imperative that businesses operate with agile planning that can be adjusted quickly and effectively. However, as planning response times inch closer to real time, nearly a third of organizations continue to cite two main roadblocks to implementing a more data-driven approach:

  • inaccurate planning data and
  • insufficient technology

Inaccurate data plagues businesses in all industries. Sixty-three percent of organizations that use departmental or point solutions, for example, and 59 percent of businesses that use on-premises solutions identify “having the right data” as a key area for improvement in planning. The use of point solutions, in particular, can keep data siloed. When data is stored in disparate technology across the organization, planners end up spending more time consolidating systems and information, which can compromise data integrity.

It’s perhaps these reasons that lead 46 percent of the organizations using point and on-premises solutions to say that better technologies are necessary to accommodate current market conditions. In addition, 43 percent of executives say that a move to cloud-based technology would benefit existing planning.

In both cases, data-driven planning remains difficult, as businesses not employing cloud-based, enterprise technology struggle with poor data accuracy. By moving to cloud-based technology, businesses can automate and streamline tedious processes, which

  • reduces human error,
  • improves productivity,
  • and provides stakeholders with increased visibility into performance.

State-of-planning research reveals that organizations identify multiple business planning
obstacles as equally problematic, indicating a need for increased analytics in solutions that can eliminate multiple challenges at once. Nearly half of all respondents shared a high desire for a collaborative platform that can be used by all functions and departments.

Highly analytical capabilities in planning solutions further support the evolving needs of
today’s businesses. In sales forecasting, machine learning methodologies can quickly analyze past pipeline data to make accurate forecast recommendations. When working in financial planning, machine learning can help businesses analyze weather, social media, and historical sales data to quickly discern their impact on sales.

Here are some additional benefits that machine learning methodologies in a collaborative planning platform can offer businesses:

  1. Manage change to existing plans and respond to periods of uncertainty with accurate demand forecasting and demand sensing
  2. Develop enlightened operations, real-time forecasting, and smart sourcing and resourcing plans
  3. Operations that maintain higher productivity and more control with lower maintenance costs
  4. Targeted customer experience programs that increase loyalty and improve customer engagement
  5. Products and services that are offered at the right price with effective trade promotions, resulting in higher conversions

Anaplan4

Click here to access Anaplan’s detailed White Paper

EIOPA reviews the use of Big Data Analytics in motor and health insurance

Data processing has historically been at the very core of the business of insurance undertakings, which is rooted strongly in data-led statistical analysis. Data has always been collected and processed to

  • inform underwriting decisions,
  • price policies,
  • settle claims
  • and prevent fraud.

There has long been a pursuit of more granular data-sets and predictive models, such that the relevance of Big Data Analytics (BDA) for the sector is no surprise.

In view of this, and as a follow-up of the Joint Committee of the European Supervisory Authorities (ESAs) cross-sectorial report on the use of Big Data by financial institutions,1 the European Insurance and Occupational Pensions Authority (EIOPA) decided to launch a thematic review on the use of BDA specifically by insurance firms. The aim is to gather further empirical evidence on the benefits and risks arising from BDA. To keep the exercise proportionate, the focus was limited to motor and health insurance lines of business. The thematic review was officially launched during the summer of 2018.

A total of 222 insurance undertakings and intermediaries from 28 jurisdictions have participated in the thematic review. The input collected from insurance undertakings represents approximately 60% of the total gross written premiums (GWP) of the motor and health insurance lines of business in the respective national markets, and it includes input from both incumbents and start-ups. In addition, EIOPA has collected input from its Members and Observers, i.e. national competent authorities (NCAs) from the European Economic Area, and from two consumers associations.

The thematic review has revealed a strong trend towards increasingly data-driven business models throughout the insurance value chain in motor and health insurance:

  • Traditional data sources such as demographic data or exposure data are increasingly combined (not replaced) with new sources like online media data or telematics data, providing greater granularity and frequency of information about consumer’s characteristics, behaviour and lifestyles. This enables the development of increasingly tailored products and services and more accurate risk assessments.

EIOPA BDA 1

  • The use of data outsourced from third-party data vendors and their corresponding algorithms used to calculate credit scores, driving scores, claims scores, etc. is relatively extended and this information can be used in technical models.

EIOPA BDA 2

  • BDA enables the development of new rating factors, leading to smaller risk pools and a larger number of them. Most rating factors have a causal link while others are perceived as being a proxy for other risk factors or wealth / price elasticity of demand.
  • BDA tools such as such as artificial intelligence (AI) or machine learning (ML) are already actively used by 31% of firms, and another 24% are at a proof of concept stage. Models based on these tools are often cor-relational and not causative, and they are primarily used on pricing and underwriting and claims management.

EIOPA BDA 3

  • Cloud computing services, which reportedly represent a key enabler of agility and data analytics, are already used by 33% of insurance firms, with a further 32% saying they will be moving to the cloud over the next 3 years. Data security and consumer protection are key concerns of this outsourcing activity.
  • Up take of usage-based insurance products will gradually continue in the following years, influenced by developments such as increasingly connected cars, health wearable devices or the introduction of 5G mobile technology. Roboadvisors and specially chatbots are also gaining momentum within consumer product and service journeys.

EIOPA BDA 4

EIOPA BDA 5

  • There is no evidence as yet that an increasing granularity of risk assessments is causing exclusion issues for high-risk consumers, although firms expect the impact of BDA to increase in the years to come.

In view of the evidence gathered from the different stake-holders, EIOPA considers that there are many opportunities arising from BDA, both for the insurance industry as well as for consumers. However, and although insurance firms generally already have in place or are developing sound data governance arrangements, there are also risks arising from BDA that need to be further addressed in practice. Some of these risks are not new, but their significance is amplified in the context of BDA. This is particularly the case regarding ethical issues with the fairness of the use of BDA, as well as regarding the

  • accuracy,
  • transparency,
  • auditability,
  • and explainability

of certain BDA tools such as AI and ML.

Going forward, in 2019 EIOPA’s InsurTech Task Force will conduct further work in these two key areas in collaboration with the industry, academia, consumer associations and other relevant stakeholders. The work being developed by the Joint Committee of the ESAs on AI as well as in other international fora will also be taken into account. EIOPA will also explore third-party data vendor issues, including transparency in the use of rating factors in the context of the EU-US insurance dialogue. Furthermore, EIOPA will develop guidelines on the use of cloud computing by insurance firms and will start a new workstream assessing new business models and ecosystems arising from InsurTech. EIOPA will also continue its on-going work in the area of cyber insurance and cyber security risks.

Click here to access EIOPA’s detailed Big Data Report