- Failing to harness and leverage data effectively puts organisations at risk of being under insured.
- Today’s businesses are dynamic, with exposure and structural needs often changing regularly.
- By capturing, aggregating, and housing data in one place, it can be analysed far more effectively to give a better overview of your risk.
- To make the most of the evolving data capabilities in the insurance market, the integration of machine learning and artificial intelligence is an important step in accelerating and expanding digitisation in the industry.
Despite the rapid pace of technological advancements in the past two decades, the insurance sector has struggled to keep up. To understand this phenomenon, it is crucial to explore how the industry has operated over the past few decades and the evolving landscape of risk management, which has given rise to complex and extensive datasets that demand attention. Failing to harness and leverage data effectively puts organisations at risk of being underinsured or incurring excessive premiums. It hampers their ability to gain valuable insights into their business needs while burdening them with administrative complexities.
Meeting today's insurance needs
Insurance is typically cyclical, with annual renewals driving behaviour that tends to repeat the previous strategies and structures of years prior. However, this behaviour often means that the scope of information risk managers seek to understand their present risk is limited. Using the example of an insurance policy – while helpful in capturing most of the data needed – it is only accurate on the day of its creation.
Today’s businesses are dynamic, with exposure and structural needs often changing regularly. Unless you can track that, your original insurance policy becomes a snapshot of that risk rather than an accurate reflection of the business you may be underwriting or presenting to underwriters.
Understanding the true cost
September 11, 2001, is a date ingrained into everyone’s minds, with an impact that was felt worldwide, including in the insurance industry. According to AXA, it resulted in one of the largest cumulative claims payouts in history – $32.5 billion ($65.1 billion today) of losses across several lines of insurance, leading to multiple losses that were not understood. This led to companies being unable to grasp a complete, holistic picture of their risk. Before the attack, there was uncertainty as to what the coverage included. A terrorist attack was a remote possibility, so its wide-ranging effect on policyholders and insurance classes was not anticipated.
This lack of transparency and traceability meant that many claims were held up in court proceedings and took years to be paid. Fast forward to February of last year, with Russia’s invasion of Ukraine, not much has changed from a risk data perspective. While terrorism coverage may have improved over time with more certainty around the details, the act of capturing it remains essentially the same. As a result, the industry was once again struggling to cope with the dynamic needs of the conflict, with many organisations needing help to count the cost of claims across their political risk, marine and aviation business lines.
However, there are solutions to this problem. By capturing, aggregating, and housing data in one place, it can be analysed far more effectively to give a better overview of your risk.
Lessons from the past
While events like the pandemic and the Ukraine conflict were once considered ‘black swan’ events, the rapid increase of such events means that to continue to have a grasp of your risk, you need a dynamic solution that can keep everyone in the insurance value chain connected and up to date so that should such events occur again, the data you hold is readily available and complete.
“While the industry refers to these instances as black swan events that only occur infrequently, the reality is that we have had far more occurring at regular intervals. And with the increasing frequency of these events, now more than ever, the aggregation of good quality data is essential to give your balance sheet insights which can lead to a better understanding of risk retention, risk transfer and risk management,” explained Mark Costin, Commercial Director at Insurwave.
With the hard market cycle making the cost of risk transfer expensive and the fall in solutions that adequately cover them, many are seeking their own solutions, such as captives – a method through which the company retains the risk as part of its balance sheet. This capacity shortage in the insurance industry has resulted in a clear value proposition for companies to consider creating new capacity themselves through a captive, which can then mitigate the continued premium increases.
Gross premium flowing through captives has never been higher, with estimates at more than $200 billion, split across North America and the rest of the world. For Mark, the more clients can capture and aggregate their data, the more they can take control of their own balance sheet, but the benefits can only be felt if you stick with it.
“Data provides the confidence you need in making decisions in risk transfer or the retention of risk […]”, he explained.
While we continue to see additional consideration given to emerging risks and risks not previously financed through captives, such as cyber, the opportunities for insurers to mine their data for greater business value have never been higher. The computing firepower, sophisticated toolsets, and scalable platforms necessary to process and analyse enormous data volumes are commonly available.
Looking to the future
To make the most of the evolving data capabilities in the insurance market, the integration of machine learning and artificial intelligence is an important step in accelerating and expanding digitisation in the industry.
As the quantity and quality of available data sets increase, the potential for AI to revolutionise the insurance industry grows. More data will require more processing power, but also, in turn, new models and algorithms that can process vast amounts of data and identify patterns in data alongside new trends but, most importantly for insurance – new risks.
An immediate priority, however, is the quality of data and structuring it in a way that is ready for use. It doesn’t matter how vast a data set you hold – if the quality of data is poor, the insights you get from it won’t be much better. With the help of AI and machine learning, data can be cleansed, enriched (identifying missing information or code), and aligned to standards to support and improve downstream processing such as cat modelling, exposure management, pricing and policy administration.
“With AI-powered data capture, the benefits to the underwriting process can be clearly seen, with new insights in real-time for risk selection, portfolio optimisation and exposure modelling completed in minutes instead of days”, said Richard Archer, Insurwave’s Chief Strategy Officer.
Embracing AI at a business-wide level is not merely an IT problem to be solved but a real business opportunity that will shape the future of insurance.
On the edge of change
The specialty insurance industry stands at the precipice of a data-driven revolution. The challenges of capturing and leveraging vast amounts of data have hindered the industry’s ability to accurately assess risk, understand coverage needs, and make informed decisions.
The rise of captives as an alternative risk management strategy further highlights the industry’s quest for greater control and flexibility. By embracing technology like artificial intelligence and data aggregation, insurers, brokers and insurance buyers can gain the confidence to navigate the dynamic landscape of emerging risks, black swan events, and evolving customer needs.