There’s no doubt that the pandemic has upended the business processes of every industry, insurance included. Consumer behavior has shifted dramatically, and players within the sector have had to rethink priorities for 2021 and beyond.
At the end of 2020, Deloitte released the insurance industry outlook for the upcoming year, which revealed the initiatives companies planned to focus on as the “new normal” emerged. Among them, was a large interest in insurance software with data analytics.
“49% of respondents (led by 56% in North America) are looking to boost investments in data analytics.”
With the data volume growing rapidly thanks to the digitalization of the modern world, it’s easy to see why business leaders are eager to leverage analytics within the insurance industry. After all, it’s where all the customer behavior and risk insights lie.
Today, we’d like to dive deeper into the priorities industry leaders have specified for the near future and how data analytics analytics software for insurance can help address them. Let’s get started.
Why Should Insurance Firms Care About Data Analytics
According to Deloitte, there are a few clear priorities for insurance industry leaders amidst the world’s post-COVID recovery efforts. Interestingly, most of them relate to data analytics as it can either help companies reach the set-out goals or play an invaluable role in ensuring good performance. Let’s take a closer look at each one.
Cost reduction. First and foremost, insurers are looking to cut costs between 11% and 20%. However, not necessarily to simply reduce spending, but rather to reinvest in digital capabilities. In fact, “95% of survey respondents are already accelerating or looking to speed up digital transformation to maintain resilience”.
Customization. With consumers indicating a preference for greater customization of services, insurance companies are looking to invest in technologies that can help cater to these needs.
Underwriting automation. In North America, increasing automation of underwriting processes is a top priority among insurers. Some are interested in going beyond streamlining routine tasks and eager to leverage AI and advanced predictive tools to boost underwriter capabilities.
Cloud migration. Many insurers are looking at cloud infrastructure migration since it can greatly reduce expenses while facilitating innovation and agility. Moreover, business leaders realize that modernizing legacy systems through the cloud can help implement analytics and automation tools that will have an impact across entire insurance operations.
Cybersecurity. Finally, with the amount of digital information steadily growing, so is the potential for security breaches. As a result, insurers are eager to double down on cybersecurity projects that help keep customer data safe and well protected.
Now, you might be asking yourself, “Сan all these priorities really be addressed with data analytics in insurance?” Well, it turns out, maybe. At least, most of them can.
Keep on reading to find out how.
Insurance Data Analytics Benefits
First of all, let’s get on the same page with our terminology.
We define data analytics as a process of quickly analyzing digital information to discover hidden insights, forecast upcoming events, and answer important business questions to facilitate decision-making.
Since insurance industry operations are pretty much entirely based on data-driven processes, it’s unsurprising that proper analysis can be applied to many areas of this sector. So, today, we’ll focus on four key benefits of insurance data analytics and discuss how it can help tackle some of your prioritized tasks in this industry.
1. Lead Generation
Competition is fierce within the insurance industry. Every day your business is fighting for new customers, and the only way to do that effectively is to know who you’re fighting for and what it is that they need.
Luckily, the Internet is filled with that information. Ad networks, website performance monitoring platforms, and other channels you leverage for boosting your online presence, all accumulate valuable prospect data.
Now, it is certainly difficult to make sense of all that information by yourself and use it for lead generation. However, this is where data analytics can help. Algorithms can identify behavioral patterns and draw them out for you.
Which channels bring in the most lucrative customers? Which campaigns tend to get the best user interaction? All of this can be discovered and leveraged through insurance data analytics.
2. Customer Service Enhancement
Insurers have a lot of customer data at their disposal. Whether within a CRM system or from individuals sharing it willingly to secure a good insurance offer. However, not all companies make the most of the available digital information. Often, it is just collected, looked over during underwriting procedures, and never returned to for a deeper analysis.
This is unfortunate because customer data contains invaluable insights about what it is that your clients truly desire. So, if you make the most of software solutions that can analyze it — you’ll be able to anticipate these needs and deliver better services.
Moreover, if you recall, customization is one of the top priorities for insurance firms. Acquiring personalized policies is of utmost interest for clients, and insurers have to offer this opportunity to stay competitive.
Once again, data analytics in the insurance sector can come to the rescue. By quickly scanning myriads of customer data, analytical solutions can determine which type of policy will best suit a potential client and recommend it to them.
Additionally, platforms with AI-Powered Call Analytics can guide your sales staff throughout any client-agent interaction and gather intelligence to ensure the customer gets precisely what they are looking for as fast as possible.
3. Policy Issuance Simplification
A somewhat more innovative data analytics use within the insurance industry is for policy issuance. Specifically, self-servicing.
Thanks to modern technology, if your firm utilizes portals for customers to manage their policies and deal with any potential issues, informative data can be continuously collected and analyzed to identify clients who might be interested in an upgrade, modification, or a new policy.
For instance, data analytics can make smart recommendations precisely at the time when a policyholder is looking to purchase additional insurance or one for a family member. Thus, simplifying the process and helping deliver a better service.
4. Costs Optimization
Whether you’re in auto, health, or life insurance, data analytics can help you optimize costs like never before. At a time when spending reduction is a top priority for business leaders, this is an especially valuable benefit.
Since predictive analytics tools can scan enormous amounts of digital information, risky individuals who might be inclined to fraudulent behavior can be quickly identified. Thus, leading to a reduction of costs from dealing with fraudulent claims.
Moreover, faster claim management in the insurance industry can also be achieved with the help of data analytics. Automated data processing can simplify and speed up existing procedures to reduce time-consuming labor, thus decreasing personnel-related expenses.
Of course, you’ll have to think about custom software development costs if choosing to pursue data analytics. However, despite the at times significant initial investment, the final solution will end up saving you money in the long run.
Is Your Business Ready For Insurance Data Analytics
Data analytics in insurance may well be considered the industry’s golden ticket to better services and higher profits. However, with so many firms already looking to up their investments into such digital initiatives, it’s important to not fall behind and be stuck with outdated processes.
So, if you’d like to look into insurance software development for your organization — don’t postpone reaching out to discuss your needs. Velvetech’s team is highly experienced with building solutions that cater to client requirements and boost the bottom line. We’d be happy to help.