Introducing analytics as a value-add service for clients is one of the most successful ways to win new accounts and secure them for years to come.
We have a team of qualified actuaries and experienced CAT modellers that work alongside our specialists and our broker clients to combine programme design and placement services with expert technical analysis.
In this article, we explore where to apply analytics, the process, why introducing analytics can lead to long-term client partnerships, and how you can take the first step.
Where to apply analytics
Data fuels analysis, and analytics drawn from a meaningful volume of good quality data provides the greatest source of insight on a risk. Relevant data sources include claims and exposure information, as well as company financials, where applicable.
In our experience, the following projects provide the best opportunities for leveraging analytics to deliver client value:
- Portfolios of accounts
- Retention optimisation for captives with multiple lines of business
- Commutations for M&A activity
- Consolidation of existing books of business into a handful of placements
The analytics process
The analytics engagement begins at the outset, unlocking information in the data supplied by the client, which we use to develop ideas and recommendations. This includes spotting trends, identifying the portfolio’s strengths and being aware of any data deficiencies.
For example, analysing the data available and using it to build a loss model that predicts ground up claims allows us to get a clear idea of the nature of losses and the most suitable style of cover.
Carrying out modelling at this early stage rather than in response to market queries further down the road also ensures we arrive at the answers before the market does. This puts us in a strong negotiating position on behalf of our clients. It informs our market strategies and their own risk appetite.
Peak exposures and large losses typically attract the most attention from modellers and other analysts, so we consider various ways of including these in our modelling to ensure the market does not apply unreasonable terms that the insured is forced to accept.
Once the cover has incepted, it is important to monitor performance over time and use this information to prepare for renewal discussions.
Increasing client understanding and engagement
There are numerous examples where introducing analytics into a broker’s offering secures accounts that they had been chasing for some time.
However, the real sell in using analytics to underpin client engagement is that after the client recognises its value, they come back for more year after year. Once the client has a framework to quantify risk appetite, compare covers and select the ideal structure for their needs, it becomes a reference point for regular monitoring and annual renewal discussions.
Moving the account to another broker becomes considerably harder as other brokers are unlikely to provide the same level of technical support.
How to introduce analytics
By now, it is likely you have an idea of key clients where there is an opportunity to use analytics. However, for many companies, jumping in at the deep end with analytics is a recipe for disaster. Clients prefer a guide to help them along the journey with small, but consistent steps.
For retention optimisation reviews for captives, a useful starting point is to ask the insured to consider the largest amount of loss they are willing to pay for each insured event, as well as across all events in a year. For example, a maximum of USD 250k per event and USD 1.75m in a year. If there are multiple lines of businesses, these amounts may vary for each line.
Scenario testing of hypothetical losses can help tease this information out of the client.
Over time, they should begin to express their risk appetite with reference to probabilities rather than absolute amounts. For example, they require a 95% chance that the cost of all retained losses are lower than USD 1.75m and a 99% chance that they are lower than USD 2.5m. Relaxing the risk appetite criteria allows for a more flexible insurance cover, usually at a lower price.
We run loss models to design insurance covers that meet these requirements, built using data provided by the client. Data needs vary depending on the nature of the client’s business as well as the lines of business and attachment points for which they are seeking risk transfer.
Once an analytical framework is set up and the client is using it to make decisions, the possibilities are endless.
As levels of competition and consolidation continue to soar, there has never been a greater need to differentiate your brand and services.
Engaging with the client to understand their needs, the data they have available and their criteria for success is a valuable starting point for understanding how analytics can benefit a project.
It shifts the focus from sentiment to data, and provides a consistent and impartial framework for making decisions around risk.
As the adage goes: “what gets measured gets managed”, and once you can measure risk you have a much better shot at managing it.