The insurance market has three interconnected components:
1. demand from risk owners – corporations, individuals, (re)insurance companies – that have risk that they want to manage in some way,
2. supply from capital providers – capital that will be utilised to bear that risk, generally represented by an (re)insurance company, and
3. data and analytics – this represents the language to talk about risk, to quantify risk, and to enable transactions to take place by matching supply and demand
There are two different alignments of these components in the market (see Figure 1). When data and analytics capability are bundled with capital, we have an (re)insurance company. When it is bundled with demand, we have an adviser or broker. The need for clear and objective advice on both the demand side, to the insurance consumer or insurer, and on the supply side, to the capital owner, increases with risk complexity.
In addition to data and analytics being utilised by risk owners to assess and understand their exposure, they are also used by regulators and rating agencies to assess insurance company capital adequacy, and by investors to understand risk and return opportunities.
Reinsurance Market Dynamics – Supply and demand
Our recent Reinsurance Market Outlook report shows global reinsurance capital is currently at peak levels, as further declines in interest rates have increased unrealised gains on bond portfolios and the relative attractiveness of non-correlating insurance risk among institutional investors.
However, price competition and weakening investment returns have eroded reinsurers’ expected profitability and earnings have become increasingly sensitive to catastrophe loss experience. In the absence of major events, retained earnings are not expected to be a significant driver of traditional capital growth going forward, given the amount of capital now being returned to investors.
At the same time, more reinsurance is being purchased. The cession ratio across the global property and casualty insurance industry showed a small uptick for the first time in several years in 2015 and a further increase is considered likely in 2016.
The catalysts for increased reinsurance demand are many and varied, but include:
• New purchases given cost effectiveness, increased appetite and tactical reinsurance plays
• Organic growth through demographics and development
• Market dislocation resulting from natural catastrophe and large loss activity
• Regulatory and rating agency evolution resulting in an increased recognition of reinsurance as capital
• Innovation in both insurance and reinsurance product development and in emerging risks (agriculture, cyber)
The lower pricing points delivered by alternative capital are clearly a factor, but the broader point is that reinsurance is growing in relevance. Insurers are seeking to manage their risk profiles and support growth objectives.
Reinsurance has traditionally been purchased to mitigate risk or to arbitrage a pricing differential, but has proven a critical mechanism for managing capital and controlling earnings volatility in the current challenging environment. This is partly explained by the global trend towards risk-based regulatory regimes, which fully recognise the beneficial impact of reinsurance on cedants’ capital positions.
Data – A strategic asset
The information a company possesses is a key strategic asset and analytics is key in leveraging this information to provide tangible insights that inform business decisions to drive real business value beyond simply facilitating a transaction.
The quality of the underlying data is clearly important in this process and whilst data quality in Asia has been perceived to be of low quality, the quality of data has improved significantly in recent years.
Whilst part of this has been fuelled by increasing amounts of data and recognition that data quality is fundamental to pricing, underwriting and portfolio management, there are a number of important factors that have driven this improvement in the region including:
• Regulatory (and rating agency) impacts – an evolution toward more robust regulation and emphasis on enterprise risk management
• Recent events and challenges – the lessons learned from 2011 events in APAC and subsequent events globally
• Knowledge transfer and insight – demonstrating the impact of data quality and uncertainty and how it impacts modelling:
- Catastrophe model evaluation – a better understanding of catastrophe risk including insight into the underlying peril and vulnerability allows the development of appropriate risk mitigation strategies, disaster planning and preparedness and importantly sustainable financial protection via insurance coverage on assets at risk. Whilst catastrophe models should not be used in isolation, improved data quality for use in catastrophe modelling leads to:
* Improved underwriting capability and profitability – traditional drivers within insurance
* A greater confidence in reinsurance limit setting - decreased uncertainty
* A better ability to manage exposure – more refined portfolio optimisation
* Better post catastrophe event analysis and reviews - greater intelligence around impacted risks
- Model Development - bridging the gap to enhance the understanding of risk and facilitate product and market development. Impact Forecasting, Aon Benfield’s catastrophe model development centre of excellence, has developed over 100 models globally with numerous models develop in Asia to address the challenging landscape through both probabilistic modelling and Realistic Disaster Scenarios (RDS). Access to Impact Forecasting models provides insurers with the ability to support risk understanding, pricing and portfolio management through a robust assessment of perils which are not currently covered by the main catastrophe modelling vendors - examples of this would include Malaysia, Vietnam and Jakarta Flood.
Analytics – From data to insight
With a broader array of factors influencing reinsurance strategy, reinsurance continues to represent a very valuable and multi-faceted product that needs to be considered within the financial context of a company’s capital management strategy given it is generally lower in cost than other forms of available capital.
It is being used to support a range of management objectives - these include creating efficient corporate structures, maximising business positions, developing new products, supporting target financial strength ratings, managing expenses, controlling aggregate exposures and optimising solvency capital ratios.
Consequently, it is important that insurers conduct a comprehensive review of their reinsurance arrangements to ensure they have an optimal programme in place that reflects their risk and capital strategy.
Analytics plays a pivotal role in supporting a comprehensive enterprise risk management framework and with increased recognition that rating agency and regulatory capital models do not reflect the company’s specific risk and capital objectives, many companies are utilising capital models to enhance a company’s ability to assess and test strategic decisions and risk management strategies beyond the assessment of the reinsurance structure.
These are typically analysed based on a Monte Carlo simulation which allows the model to capture complex risk interactions and interdependencies and provides insight into the range and likelihood of possible outcomes and their sensitivity to key variables. Reporting can be tailored to the company’s requirements detailing financial statements, key ratios and other capital modelling metrics.
In addition to informing the optimal reinsurance programme to balance internal/external and central/local purchasing decisions, capital modelling can also help answer questions such as:
• How can I articulate my risk tolerances and risk appetite in a way that links to effective risk/return decision making within my firm?
• How should I allocate capital across operating divisions and product lines?
• How should I set profit targets by division especially for heterogeneous companies?
• What combined ratio should I target to generate a desired return on equity by line?
• How should I model credit risk in my reinsurance recoverable portfolio?
• Should I repurchase shares, expand organically or grow via acquisitions?
• What is the best use of excess capital?
• Should I use a partial internal model and how can it help me to calculate my regulatory capital requirements, assist in my Own Risk and Solvency Assessment (ORSA), definition of risk limits?
• What is the optimal mix of investments to maximise return whilst achieving bespoke management targets for economic risk, earnings volatility and regulatory/rating agency capital usage?
Whilst there are numerous factors impacting the supply side and demand side of the industry, data and analytics remain critical, both in developing a comprehensive understanding of risk to inform a company’s risk and capital strategy, but also to address those risks that are emerging, underinsured or present design challenges: risks such as cyber, damage to brand and reputation, and terrorism.
We believe that these areas and others like them present significant growth opportunities for the insurance industry.
• Reinsurance is a very valuable and multi-faceted product that needs to be considered within the financial context of a company’s capital management strategy given it is generally lower in cost than other forms of available capital;
• The data a company possesses is a key strategic asset and analytics is key in leveraging this information to provide tangible insights that inform business decisions to drive real business value; and
• Consequently, it is important that insurers conduct a comprehensive review of their reinsurance arrangements to ensure they have an optimal programme in place that reflects their risk and capital strategy.
Mr George Attard is International Head of Aon Benfield Analytics.
Aon Benfield is the 2015 winner for Reinsurance Broker of the Year at the 19th Asia Insurance Industry Awards.