How to operationalize analytics into insurance workflows

Insurers and the insurance industry have the challenge to keep ahead of the constantly evolving risk environment. Whether it is the rise of cyber risks, increasing extreme weather events, or the rapid emergence of intangible assets and the difficulty in valuing such assets.

Some carriers thrive and prosper amidst these challenges. They find new ways to innovate and new opportunities in a changing environment. Others decline under pressure, whether by a major miss on a strategic issue – or via slow decline by being too cautious, too predictable. 

Any experienced insurance executive can tell you a story or two of how a once strong carrier devolved to obsolescence – or how a once middling carrier grew rapidly based on one strategic decision or innovation. But it is not just anecdotal, there are detailed analyses that show that innovators thrive and grow in the insurance industry.

According to one of these, a study by McKinsey, "insurance market shapers," those who boldly innovate, create significantly more economic value than their peers, create profits up to twenty times the industry average.

Where are these innovators making their mark? There are certain characteristics of these leaders – but one area is abundantly clear:  Those who boldly and strategically apply advanced data and analytics capabilities to their business, whether in risk selection, underwriting, pricing or claims, are winning the competitive race.

In fact, I would dare to say that future success for insurers largely lies in their data and analytics strategy. 

However, most insurers today are spending resources and time trying to figure out technical issues: 

• How to tap into the needed data.
• How to build risk models.
• How to integrate analytics and models into workflows. 

Too often they sacrifice a focus on the more strategic aspects: determining where to apply analytics in their business – and how to differentiate their data mix and models from their competitors to create differentiation and strategic advantage.

The ultimate goal is to be able to innovate quickly -- and have that innovation drive results rapidly. If an organization can become more agile in inserting analytics and intelligence into their natural work environment workflows, that will translate into business success. 

So, how does one build-in that agility in innovation through data and analytics?  

Designing your data strategy

The first step in building a modern analytics function is getting the data component right. Data is at the center of the challenge and the opportunities set before insurers, as it continues to become ever more ubiquitous. 

You need to have a data strategy and vision. If you are not figuring out (a) what data you are focused on leveraging, (b) where you are getting that data from, and (c) how you are going to be getting that information into your workflow or model, you're really going to be behind the eight ball.  And don't forget the need for on-going review and auditing of the data you purchase to ensure you are not paying for irrelevant or redundant data.

The capabilities exist to utilize data as it becomes available via cloud-based APIs – and your core solution providers should have or actively be developing tools to immediately insert and access that data in core workflows. 

The growth of data is happening at a speed faster than most insurers can handle. So, your organization needs to figure out how you are going to acquire, process, and integrate data at the speed of availability and business. This goes both for projects you have identified as priorities – and for future projects that may be out on the horizon. You need to think ahead – and be ready to integrate all kinds of new data and information: IoT data for residential properties from devices like Ring, Alexa and Google Home. Telematics data from personal automobiles and commercial trucking. IoT and systems data from commercial businesses. 

The market will see an escalating evolution in the availability of data. There are all kinds of legal, privacy, and access issues that will shake out and bring new data to market. The more prepared your organization and systems are to handle this data and pivot, the more set up for success you'll be.

Modernizing analytics and risk models

The second step is modernizing your models. Most insurers are now commonly using analytics models in risk selection and pricing. There are varying degrees of proficiency and sophistication, but the insurance market has widely adopted these technologies. 

The areas where insurers struggle most often are:

• Finding the right data for their models.
• Regularly monitoring and adapting existing models.
• Innovating and testing new models for new business applications.
• Efficiently managing the entire process to free up data scientists to focus on strategic priorities.

Most insurers are well-aware of the problems in getting the right data into the models in a timely manner. But they are not aware that there are solutions and consultants available to solve this technical problem for them in an efficient and cost-effective manner. Nor are they highly focused on the other challenges of managing and improving their risk models on a regular basis – nor on creating new models in an agile and rapid way. 
Being prolific in experimenting, testing, and innovating with risk models across risk selection, underwriting, pricing, claims, and marketing will eliminate significant pain points and present unimagined opportunities. In this day and age, your organization should have this as one of their primary business priorities.

Operationalizing analytics in workflows

The last step is operationalizing or embedding analytics into workflows. The biggest point of failure for firms is in operationalizing risk models – in integrating analytics and risk models into workflows and processes across their insurance lifecycle. 

If data, analytics, and risk models are not innovated, adopted, and put into the hands of those who need them when and where they need them – of what use are they?

Insurers are often proud of their risk models. The models may often be unique and a differentiator for their firm. However, they simply stall or do not want to expend the energy and resources to integrate into workflows and develop screens that their adjusters can use. Yet again, there are solutions and consultants that can handle these technical hurdles and details at a cost that is well worth the resulting innovation and ease-of-use for their underwriters and professionals.  

The most successful approach to managing analytics is to internally prioritize strategic considerations and specific use cases, while outsourcing technical hurdles, and then embedding analytics and models into workflows.  Putting the power of modern data and analytics into the hands of decision-makers.

With such an approach, you'll deliver embedded insights to underwriters and claims adjusters so they can make smarter decisions on risk selection, pricing, claims triage, and settlement. Modern analytics provides the capability to precisely calculate risk, increase premium revenue, improve claims results, and strengthen customer loyalty and value.

Providing targeted and integrated insights at the right time and place enables your organization to adapt to a constantly evolving environment and fulfill the insurance protection needs of individuals and businesses.