Insurers have come to realize that regardless of size, they must embrace digital transformation. To remain competitive and meet consumer needs, it is critical to make informed decisions with quality data.


Wavicle’s data analytics consultants help life and property and casualty insurers collect, ingest, store, and analyze the data needed to streamline processing, improve efficiency, and reduce risk. The result: better decisions, lower overall costs, improved profitability, and satisfied customers.

The state of data, analytics, and digital operations in insurance

Wavicle’s consultants are experts in helping their insurance customers collect, access, analyze, and optimize their data, but we wanted to better understand the current state of data and digital operations in the industry.


In Q3 2022, we commissioned a survey of 320 financial services and insurance executives to find out more about their data and analytics capabilities, their plans for digital transformation, and the value they expect to realize from these changes. See some of the surprising insights and trends we uncovered.

Financial Services Executive Survey
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Connect device and customer data to drive better decisions

Thanks to the internet of things (IoT), cars are smart enough to track driving behavior, appliances can connect and communicate, and more data is available about individuals’ daily activities than ever before Yet, it can still be difficult to make smart decisions with all of this smart data.


Why? Because many insurance companies still rely on outdated, siloed systems to collect, analyze, and share data. Wavicle can help you harness information from all of those smart devices, migrate it to a cloud platform, and summarize it in a visual dashboard so you can make informed decisions and reduce risk.

Automate and accelerate claims and underwriting

Today, even simple claims and underwriting can take up valuable time and resources. That’s why large insurance companies are turning to automation.


Using artificial intelligence (AI) to automate the majority of claims can reduce the cost of manual labor and identify fraud early. Machine learning (ML) can also be used to accelerate the underwriting process, improving pricing accuracy, service levels, and customer satisfaction.

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Drive model behavior with third-party data

The most accurate models typically require a combination of your customer data and third-party information. From government statistics to weather patterns to credit scores, integrating multiple types of data into data science models allows you to more precisely predict risk and improve the customer experience.


However, models are only as strong as the data supplied. When using third-party data and ML models, you cannot overlook the importance of effective insurance data management to ensure quality data is being integrated and used.

A faster, more cost-effective approach to compliance

Meeting insurance regulatory requirements requires a modern data architecture. It’s as simple as that. Without a cloud-based data platform, you’ll need to allocate too many people and spend too many dollars to meet the basic requirements.


Maintaining legacy data platforms often is not the best use of your resources, nor does it protect you from future privacy or compliance issues as the adoption of third-party data grows. The data management consultants at Wavicle can help you develop the right infrastructure to meet evolving compliance standards, drastically reducing time and costs.

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Big goals, not so big budget?

Budget concerns should not keep your company from accelerating its digital transformation with insurance data solutions.


Wavicle’s data and analytics consultants specialize in helping life and property and casualty insurance companies leverage their data to quickly realize a return on their investment. With the help of our proprietary accelerators, Wavicle’s team can increase the speed of your data projects while reducing labor costs.

The importance of insurance data analytics


Data analytics in the insurance industry is now critical for companies to make the most of their greatest asset: information. Given the volume, velocity, and variety of big data in insurance, robust analytics is necessary to help organizations achieve key business objectives, including:


  • Opportunity identification: Comprehensive data analytics makes it possible for companies to uncover trends across their CRM and other customer data collection solutions. This enables them to better understand the consumer journey and pinpoint potential opportunities for additional sales. Armed with relevant insight, marketing teams can be better prepared to create campaign content that captures customer interest.


  • Customer satisfaction: Satisfied customers stay; unhappy customers leave. Data analytics makes it possible to zero in on customer pain points and take steps to remediate them before clients take their business elsewhere. Consider call centers — if data analysis reveals that wait times are increasing exponentially, companies can act before customer churn begins in earnest.


  • Fraud detection: Effective fraud detection can save companies time and money, but pinpointing fraud is often a time- and resource-intensive task. In-depth analysis of historical trends and current claims can help businesses reduce their total fraud risk.


  • Risk evaluation: Risk evaluation is a critical part of insurance underwriting and requires skilled insurance professionals equipped with actionable data. Effective data analytics makes it possible for underwriters to focus on the human side of risk evaluation and leave the heavy lifting of massive data sources to robust analytics tools.


Put simply? Effective big data analytics in insurance is now instrumental for successful insurance organizations.



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