The insurance industry is unique: It requires an unparalleled degree of trust from its customers. Clients routinely share their most sensitive financial, personal, and health information—trusting insurers with their data in return for better policy terms and lower premiums. With that trust comes high expectations around how personal data will be collected, stored, and protected. In reality, many financial services companies find it hard to measure up.
- 47% selected personal data protection as their #1 priority when interacting with a bank or insurance company.
- 50% would switch to a competitor if they received assurance around the storage and use of their personal information.1
- Within the financial services industry, 21% of sensitive files were found to be exposed.2
- 26% of financial enterprises faced a destructive attack in 2018.3
The numbers aren’t encouraging by any means. But with the right processes and technology, securing personal data (and winning customer trust) is possible. Read on to learn how insurers can protect client data with confidence.
Unstructured Data: The Hidden Culprit
At a recent conference, CEOs and CIOs from major carriers were heard laughing—in a nervous sort of way—about the volumes of faxes they still receive, and how many "wet signatures" they require. In all seriousness, the sheer volume of paper and scanned documentation that insurance organizations grapple with is no laughing matter.
Unstructured data and lack of searchability means that insurance firms are inundated with mounds of data they can neither find nor protect. In the case of a breach, a company is forced to manually sift through thousands of unstructured documents to locate and remediate all instances of clients’ sensitive data. That would be an alarming—and frankly impossible—reality for an insurance organization of any size.
Turning data protection expectations into reality
To overcome their unstructured data challenges, insurance organizations need to protect their data assets and implement a file analytics solution as part of their overall data protection strategy. To meet best practices for protecting clients’ sensitive information and reduce the risk associated with unstructured data, organizations must:
- Perform a data audit to discover what client data exists and where it resides.
- Convert unstructured data to a standardized machine-searchable format.
- Eliminate redundant, obsolete, and trivial (ROT) data to reduce their PII compliance footprint.
- Extract values to determine PII risk and perform the necessary remediation steps (such as redaction) to protect sensitive information.
Completing these steps, along with adopting a robust data enrichment solution, enables insurers to gain full visibility and access to all of their customer data (including PII), allowing them to take the necessary steps to protect sensitive client information and deliver on customer expectations.
The insurance industry has a special relationship with its clients. Because coverage requires so much sharing of sensitive personal information, a strong bond of trust is created—as well as high customer expectations around data protection. Turning client expectations into reality requires that insurers do everything they can to safeguard against breaches by finding, evaluating, and protecting sensitive data. To learn more about how Adlib Protect can help insurers secure sensitive client data, click here.
1Adlib Software, How Important is Customer Onboarding for Banking and Insurance Customers? (Survey Monkey: 2019)
2PURPLESEC: The Ultimate List of Cyber Security Statistics for 2019 & Varonis: 56 Must Know Data Breach Statistics for 2019
3Deloitte: Global Cyber Executive Briefing: Insurance
About the Author
As a senior executive, Scott has spent the last 20 years building Adlib into the thriving organization it is today. Scott has held customer-focused leadership roles spanning success, professional services, marketing, and support. He is passionate about business growth, the human impact of technology, and the pursuit of an ideal customer experience measured in the customers’ terms.