As any insurer can tell you: The more information you have about a risk, the easier it is to accurately insure.
That's why big data analytics promises so much for the insurance industry. It allows companies to take data from a range of new sources - everything from smartphones and embedded sensors in machines to Facebook and Google - to create insights for insurers.
One particularly apt example of this is telematics-based car insurance, where in-car tracking equipment monitors driver behavior and sends this data back to the insurer. If you're a safe driver, you can look forward to reduced premiums, while more erratic drivers will get charged a little more. One such U.K.-based company that uses this model found that it reduced accidents by 20 percent among drivers aged 17 to 25.
Any insurance model that can help cut down on car crashes should be welcomed, but there are some serious caveats around security that need to be kept in mind.
The information that big data analytics promises to offer up to insurers is profoundly personal - from driving behavior data for car insurers to personal medical data for health insurers. The latter is sourced by records, mobile health apps and even new innovations around genome sequencing.
But this is data that most people do not want shared around. As a result, insurers need to provide an opt-in policy to such models, where users clearly state which information they are comfortable sharing with insurers. They must then ensure that whatever customer data they hold on their own systems is as secure as possible. It is a simple equation: The more personal information an insurer collates to enhance their service offering, the greater their responsibility to keep this data protected from a privacy and security perspective.
The reason for this is threefold. Firstly, there is a duty of care to the customer. Secondly, if hackers do manage to steal personal data from insurance companies, the brand damage is considerable. And finally, there is an increasing regulatory pressure to ensure customer data is secure.
The latter of these three considerations is particularly pertinent as the European Union launches its General Data Protection Regulation. Critical to this law is the subject of security, with demands being placed on companies to safeguard against accidental loss or destruction of, or damage to, the personal data they hold on customers and clients. Violators face a highly punitive fine of up to as much as four percent of annual revenue.
Security and big data must therefore go hand in hand. Insurers should look to design a data environment that is sufficient to secure the sensitive information it holds. This means more than simply putting in place firewalls. It demands a security-centric business model.
- Such a model should be founded on full risk assessments to understand vulnerabilities in its business infrastructure and to ensure compliance with any and all relevant regulations, including HIPAA and the emerging EU data regulations.
- Insurance firms should also look to arm their businesses with the right technology, including web security gateways specifically designed to protect against threats like malware, zero-day vulnerabilities and data loss.
- As most sensitive information resides in databases, firms should ensure these entryways to and repositories of critical data are locked down from an access and encryption perspective, and are regularly scanned and penetration tested for vulnerabilities and misconfigurations.
The primary currency for insurers is data. As this evolution fortifies, they must ensure their information governance and security strategy keep pace. If not, they risk punitive fines and a loss of trust that could prove fatal to their businesses.
Jane Dotensko is Trustwave marketing manager in EMEA.
