Big data, data analytics, data driven, artificial intelligence and machine learning dominate the topics currently discussed in actuarial and insurance conferences in general. What does it mean to be data driven?

A company in any sector is data driven when facts, relevant metrics and data drive strategic decisions.

Insurance companies certainly collect vast amounts of data but often the granular components are missing.

Commercial property insurers are notorious for failing to store in a systematic way all the aspects of the properties they insure (value, location, construction type, occupancy, etc.). This level of data is often provided and stored in stand-alone spreadsheets that cannot be easily aggregated. When claims arise, it is difficult to associate the claims to the granular details of the building insured.

In addition to this, it is not uncommon for insurance companies to accept incomplete submission from brokers where key elements of data such as values allocated to each location missing.

In the absence of data provided by the insured, insurance companies must make a series of conservative assumptions that may lead to a higher premium than if complete data was provided.

During the soft market underwriters may be more flexible to accept submissions with incomplete data that during the hard market.

2020 has seen insurance rates skyrocket following years of soft market. The insurance market is seeing an increase demand for pricing actuaries and reviewing and enhancing pricing models and processes has become a top priority.

Insurance pricing models should be data driven, the parameters used to arrive at the premium should be calibrated with claims experience, but often they are not simply because the existing data do not allow actuaries to carry out such exercise or due to the extremely low frequency of events in that class of business.

We anticipate that insurance companies would enhance submission forms to ensure clients provide complete data for pricing and risk exposure purposes. Given current capital constraints insurers would be happy to decline to quote for incomplete proposals.

Wherever possible we expect insurance companies to be more reliant on external sources of data to calibrate their models and make use of technology to automate the transmission of that data to the pricing models.

For classes of business where data are scarce due to the low frequency of claims actuaries would seek collaboration not only from underwriters but also from other professionals with expertise in that sector.

For real estate transactions we would expect increased liaison between actuaries, underwriters and solicitors or conveyancers to determine the risk drivers that may lead to a claim covered under title insurance policy.

Those insurance companies that embrace change and technological advancement in 2020/2021 would rip the benefits of big data at granular level in 4 to 5 years’ time. So, when the soft market starts again (and it will!) these companies would have the data to drive strategic decisions.

2020 and 2021 would be key years for commercial and specialty insurers in laying the foundation to be better positioned for the next decade.

At TitleHub we are helping insurers and managing general agencies put in place efficient pricing processes following our three-step framework:

1) Design and develop a consistent pricing framework for the class of business. This allows companies to generate a technical price at policy level.

2) Put in place an end to end process that allows insurance companies to generate data driven key pricing and profitability indicators.

3) Design a digital platform that hosts the pricing framework, collects all the data used for pricing and provides management real-time access to profitability reports from step 2.

The insurance sector is notorious for their resistance to change and complacency. Every company wants data and digitalisation but very few would be prepared to make the investment in time, effort and human capital to make this happen. So, our prediction is that only a small proportion of mid to small size companies would have better processes and data when the soft market strikes. Watch this space!