Clare Eades insurance
The issue of Big Data has impacted on every single industry and non more so than the insurance sector. In fact it is perhaps one of the biggest industries to be able to benefit from Big Data and analytics.
Naturally, with something that is being adopted at a pace rarely seen before, there are a number of organisational, technological and people related issues which make leveraging the full advantages of Big Data and analytics challenging, and puts businesses that do not react at a competitive disadvantage.
Big Data is most definitely a game changer for the insurance industry:
The competitive drivers of business mean that there is growing competition. Cost is an important strand of this (achieved through increased efficiency, effective analytics etc.), alongside other metrics such as customer service. Big Data therefore presents insurers with the opportunity to improve processes and ensure adherence to the growing regulatory challenges.
Furthermore, securing and examining data associated with policyholders alongside more informal data from a range of sources, such as social media, can assist in the evaluation of risk factors and therefore set premiums accordingly, something which is revolutionising the industry.
Insurers are no longer competing for talent with other financial services companies. The traditional preserve of the insurance industry has been the actuary and financial analyst. These are now sought after in higher numbers by modern digital brands, such as Amazon and Google and well as more traditional solid brands names such as BP and Proctor & Gamble.
The employee proposition for these companies can be more interesting and they are often competing on salary.
So far, insurers are failing to market themselves to prospective employees. The stimulating, and progressive work that they are doing around big data and analytics in the insurance industry should be enough to entice anyone.
There is a move away from the sole focus being on areas related to risk, traditionally worked on by actuaries, underwriters, and financial analysts. There is now a need for knowledge of specific business areas in addition to particular data sources.
As the industry begins to concentrate on more specific or granular data to assess risk, the focus on combined and expansive data presents a shift from pure customer acquisition to service development. The techniques required to deliver this originate from outside of the insurance sector, and so must the people with these skills.
Finding the right people both internally (alongside upskilling programmes) and externally is therefore one of the most critical actions that insurers must undertake to ensure both short and long term success.
Investopedia state that. “Applying insights to customer call centres, customer retention analysis and customer behaviours, insurers can better route customers to the appropriate support.”
As customer preferences evolve, so must the products offered. Big Data analysis therefore better predicts customer behaviours so as to increase customer retention and thereby realise better revenues.
Predictive analytics are being used to greater effect in security matters related to fraudulent claims and losses. This type of analytics can be used at the underwriting stage to predict who is likely to make these types of claims, or, at time of claim to ascertain the likelihood that it is genuine.
As customer preferences change, insurers are under the gun to develop simpler and more transparent products. Companies can analyse Big Data to better predict customer behaviour so they can improve customer retention and become more profitable, within catastrophe (re)insurance Big Data and analytics can inform policy design. It makes for a more unique and effective pricing model, as granular factors make policy design more accurate. Big Data also allows insurers to revise their pricing models instantly, rather than having to wait for set points in the year
Embedding the technological requirements of Big Data and analytics is a significant task. The five V’s that define big data (volume, velocity, variety, veracity and value) all present individual challenges to insurers who are implementing technological solutions.
The choice of technology platform is one that insurers must consider at great length. This will have an impact on organisational and business priorities, data sources, and talent acquisition.
The new platform must be able to handle large volumes of data. Insurance companies already have significant levels of data. This will only grow as telematics, social media, and data from other unstructured sources is added to the mix. Big Data technologies such as Hadoop and NoSQL are becoming increasingly popular as they are introducing new approaches to analysing huge data sets very quickly.
In spite of the obvious benefits, adoption of Big Data and analytics within the insurance market is slow. Not least because of a skills shortage at every single stage of the planning, implementation and execution phases.
The task now facing insurers is to create an environment that promotes experimental approaches and testing of ideas or theories, whilst fostering a culture of shared learning around the change, transformation, and innovation of big data.
The first companies to successfully integrated big data and analytics will have given themselves a competitive advantage by benefiting from lower cost structures, commercial efficiencies and increased customer engagement. All of which should lead to greater business opportunities.