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Big Data and Insurtech: A Carrier Perspective

By James Merz, Chief Actuarial and Analytics Officer, Westfield Insurance

 

Carriers have used big data to drive business decisions before “big data” became a term. Insurers have tapped large volumes of raw and curated data to provide insights into customer behavior, predictive models, and other market opportunities. Data provided by “traditional” data brokers provided vast amounts of information on market performance, financials, and customer preferences. 

But the massive increase of “big data” has magnified the benefits of understanding risks, customers, and the markets by providing far more detailed and individualized information that is more quickly accessible for direct analysis as well as analysis by AI/ML technologies.  

Over the past several years, many new data sources have become available to allow insurers to improve insights into risks, create the right products to provide the most peace of mind for customers, provide competitive pricing on those products, and provide accurate reserving.

Non-traditional insurance information such as high-quality imagery, smart devices, and social data enables carriers to provide the best coverages for the best prices within the markets we serve. High-quality imagery allows carriers to provide faster, more satisfactory claims resolution. It also helps us identify risks to mitigate before a claim event occurs and provides customers with insights they can take advantage of to grow their own businesses.

Data is becoming more ubiquitous throughout all of society, and companies are monetizing the data that passes through their systems. New data micro-providers are challenging the models of the traditional data brokers. These boutique collectors offer far greater insights into the select business verticals they specialize in. Social data helps carriers understand their relationship with consumers at an entirely new level, detect fraud, or identify gaps in customer coverage. Additionally, carriers are ensuring that customers, claimants, and related parties’ data will be used appropriately and protected.

Nimbleness needed

While some insurers were challenged with accessing their own data, insurtech and big data require a newfound nimbleness to handle data from other sources, which has enabled additional techniques to source their own data. The rise in insurtech has made information collection quicker and easier than ever. 

In the past, data collection devices required physical connections and significant data standardization, and often resulted in gaps in data. Insurtech has brought focus and investment on identifying and solving needs through improved connectivity and security in IoT (Internet of Things) devices. 

Insurers are using these technologies to identify risks, provide early warning, and reduce losses. Insurtech has expanded not just in the commercial markets and not just machinery, but also in the creation of “smart wear” that identifies risks to workers before they become injured. 

 

Personal and home IoT devices have become commonplace to detect failures in appliances, furnaces, and hot water tanks. IoT devices are also used in autos to reduce or eliminate claims with early warning and automated decisioning that protect the driver and prevent vehicle damage. 

 

We’re already seeing “smart cars” that include everything from lane-drift warnings, self-braking, and safe distance technologies. Although the vehicles are more costly to repair when there are accidents, these technologies will reduce in cost as they become more pervasive.   

 

Regulatory challenges

 

The more data becomes available about a company or an individual, the greater the privacy challenges -- in insurance and other industries. Generational perspectives on what is shared publicly are dramatically different, and more individuals are willing to share more private information about themselves to receive better treatment from businesses that serve them. 

 

Carriers have taken great strides in protecting the individual’s information. Data collection has its boundaries, as the more data companies collect, the greater the exposure for a potential data breach that could result in lawsuits, fines, and reputational damage. Reliable carriers invest heavily in their information security and governance programs and are always vigilant with monitoring behind walls of protection.

 

Federal and state privacy and consumer protection laws are increasing, like the California Consumer Protection Act (CCPA). Many other states are looking to adopt laws that place similar or even tighter protections for consumers. Such protection is putting a heavy strain on carriers, not only to secure their data, but also to have a deeper understanding of their data and whom the data is associated with. It requires carriers to uniquely identify each individual related to a record, be they policyholder, listed driver on a policy, claimant, or even a witness.

 

These involved parties are protected under the “right to be forgotten” provisions within select consumer protection laws. These laws are forcing carriers to invest in data modernization efforts and disciplines for managing data assets to identify a unique individual and remove information related to that individual without jeopardizing the other data related to the records.

 

New data sources certainly provide new insights for carriers, but with those integrations, carriers must increase their lines of defense as each new source opens with it a new risk to be addressed.                            

 

The impact of AI

 

The buzz around artificial intelligence (AI) and machine learning (ML) technologies is founded on strong evidence and a foundation of risk and fraud models that have been in place for decades across many carriers. 

 

What has changed is the volume of data, speed of the processing, the maturity of analytical skills to interpret AI/ML findings and recommendations, and easy-to-use tools that allow a broader user base to understand and feel more comfortable with the data for their own analysis. 

 

Technologies that enable more self-service to the worker allow empower employee to become a true “knowledge worker,” basing daily decisions on actionable data that is easy to understand and interpret. Simple interfaces with charts, graphs, notifications, and recommended attention areas are easier to build and use than ever due to many technology providers. 

 

End users of these technologies do not need IT or analytical backgrounds and find these tools even easier to use than spreadsheets. Knowledge workers making decisions based on data enable carriers to make faster, more accurate decisions that benefit both the company and their customers. 

 

Knowledge about the data itself is becoming easier for companies with AI/ML-assisted data catalogs. Carriers have large volumes of data and are incorporating more data into their processes daily. Having a deep understanding of each of those data elements is critical for a company to know not only what the data means, but what data is important and why, what is personally identifiable information (PII) or HIPAA related, which is used for critical business processes, which has levels of quality that require improvement, and other aspects needed to provide knowledge workers with the right data to make their decisions. 

 

Data catalogs have matured quickly over the past few years beyond the traditional business rules, glossaries, and standardized codes held in a repository, often requiring large teams to maintain the metadata captured about the data assets. Utilizing advanced AI/ML capabilities, data catalogs can now automate many of these processes, reducing the resources necessary to manage the data assets and identifying new relationships between data. 

 

Data catalogs are a must-have technology to keep up with the ever-growing volumes of new data being used across the organization. Without self-service tools and modern data catalogs, knowledge workers will struggle to keep up with demand for analytics and insurers will struggle to remain competitive.

 

Big data in real life

 

Westfield is leveraging big data in combination with AI/ML capabilities to further enable data-driven predictions and prescriptive actions as it relates to business decisions for our customers and ourselves. Use of big data is enriching our understanding of consumers to improve the effectiveness of our prospect lead and customer pipeline management, triggering account management actions for submission prioritization, underwriting and referral optimization, and improving overall customer satisfaction by being more proactive and responsive.

 

We’re also using big data to develop new products, including usage-based insurance and embedded home insurance. We’ve enabled business units to access and maintain vast amounts of structured, semi-structured, and unstructured data from numerous sources. This information has enabled and will continue to allow us to create new insurance paradigms that benefit our customers, our independent agency partners, and Westfield.

 

 


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