Big Data in Insurance Offers Opportunities as well as Threats
Insurance companies have a wealth of data on clients and policies across their organizations. The challenge is that data may lie in different file locations across numerous computers and servers in their networks. Insurers can be proactive or reactive in handling privacy. Being proactive means finding out where all the data resides and fully understanding any potential risks. Being reactive means waiting for data subject requests, and subsequently searching for where the requested policy or customer data resides within all of the available data sources.
Big data in insurance is more important than ever. Insurers have historical data on claims by policyholders as well as details on the insured's property (for property casualty policies) and personally identifiable information (PII) on an insured's age, family information and payment data — including bank or payment card details that must be protected from hackers. Life and health insurers will have PII data on a person's health, which must be protected against hackers not only as a good business practice but also to avoid expensive fines and penalties, or loss of reputation and business.
Proactive or Reactive?
Insurers face several challenges in finding data and protecting sensitive information. Data tends to reside in many different locations and much of it is unstructured or unclassified, therefore it remains hidden during simple searches. Customer payment information may be on a computer or server dedicated to that purpose. The same is true of claims details, with marketing-related customer data possibly residing in yet another computer or server. Furthermore, there can be separate files for each customer. An insurer needs to access all relevant data quickly when requested. However, this must also be done in a way that protects the insured's privacy.
There are two approaches insurers can take in locating data and ensuring that PII details are secure:
- A proactive approach involves finding and understanding what data resides where as well as ensuring that data is properly secured not only where it resides but also that it stays secure when "called up."
- A reactive approach involves waiting until receiving a request for data and then searching to locate it while keeping PII details secure.
The proactive approach protects insurers from surprises when the data is needed, like missing files or, more importantly, discovering that PII details are secure at rest, but not when recalled. Being proactive in locating this data can also help insurers identify policy information that is no longer needed so the insurer can schedule it for destruction.
Work With a Resourceful Partner
Despite the advantages to the proactive approach, most insurers don't have the internal resources when it comes to personnel or up-to-date technology to undertake this process by themselves.
To uncover all the data's benefits while keeping it secure, insurers need to leverage machine learning-based classification to unlock deeply hidden dark data and make it useful, while also applying neural networks and deep learning to uncover data insights usable across a variety of market segments. Data search and storage technology should also reduce risk by automatically applying and enacting retention, privacy and security policies.
However, such robust capabilities are beyond most insurers' legacy technologies. Another issue is that insurers tend to lack the IT expertise to add the additional technology needed to be proactive with their data.
To ensure that they can meet the challenges of big data in insurance, insurers should work with an experienced data partner that understands the nuances of all the privacy laws and has a solution to quickly identify data trends as well as find all relevant information for data requests. Ultimately, this approach will provide greater efficiencies and reduce costs through automation.