Big data technologies are no doubtrecalibrating the insurance industry.

This expanded form of modeling by insurers is incredibly dynamichaving an almost limitless range of new variable assumptions andbranched connections that will significantly improve andrevolutionize how insurers measure risk.

Yes, this means that some will pay less and others more, butwith an added chance to get these costs offset via direct salesthat eliminates the broker and underwriting expenses. Regardless,it will arguably be more equitable because everyone gets charged acost closer to their actual risk.

Related: The power of analytics for insurance: You ain'tseen nothin' yet

But, this equity dispersion comes at a cost in the form ofindividualized risk profiling that if too prolific, could lead toexcessive risk pool segmentation, adverse risk selection and newindividualized risk assessments that could price out new segmentsof policyholders.

Regulatory pathway needed

I believe the U.S. market is ready to realize this potentialfrom these expansive technologies and networks but a regulatorypathway needs to be established to safeguard adherence to our coreinsurance principles and an individual's right of privacy.

This new and exciting frontier of seemingly limitless data,cross-integrations, and fluid networks brings a new approach inmeasuring and identifying risk which will ultimately transform ourindustry. Perhaps the most significant advancement is in thedevelopment of the policyholder behavioral risk profiles. This newrisk measure brings a new degree of precision but since behavior isoften inconsistent and ever changing, its success depends upon acommitment to its continuous refinement.

The power of these developing data technologies adds real valueto sales, pricing, underwriting, claims and service administrationwhich all translates to expense reduction, fraud reduction,improved precision/accuracy, streamlined administration and timesavings. But, the current excitement dominating discussions is indrastic need of sober regulation to move it along.

Credible data

It is understood that all data targeted must be associative,authorized, secured and disclosed. Most importantly, it is expectedthat every piece of public or non-public data targeted andcollected must credibly correlate to the risk transferred. Usingdata that is credible (strong probability or likelihood) and thatmaintains quantifiably demonstrable experience correlating to therisk is an insurance gold standard and something that cannot becompromised.

Here are five issues regulatorsmust tackle:

1. "Disruptors" creating wide scale disintermediationand cherry picking risk or selecting against traditionalcarriers.

2. Conflicts created from data collected that cannot bequantifiably correlated to the assumed risk. Datamaintaining a quantifiable relationship to the risk but because ofits recent identification or linked associations, has no credibleexperience correlating to risk. New multivariate data combinationsmaintain the same concern and would need to be unwound anddemonstrated.

3. The data that supports these behavioral risk profilemeasures is an ever changing moving target that relies on accurate,updated and credible data. How long will these riskprofiles be retained? What is their shelf life and how long willthey be credible? What's the acceptable look-back period? Will theybe continuously updated to track with their volatility? Can anyideal or adverse profiles be contested and corrected?Would the introduction of a formal appeals process be a reasonableconsumer protection?

4. Some data collected relates to prohibited riskdistinctions or potentially discriminatory distinctions which wouldrequire validation.

5. This mile-high supply of new variable data sets andindividualized custom risk profiling could lead to excessive risksegmentation delivering runaway risk distinctions orclassifications within risk pools. This of course runscounter to the basic precepts of insurance, absorbing smallerincidental risks within larger risk pools.

Arguably, any data that does not credibly correlate to the riskassumed should be off-limits for use.

Regulators must evaluate all of this data by state insurancestandards, adhering to the fundamental principles of insurance thathave proven timeless and that have insulated the industry from someof the worst storms. These products we sell cover risk in the formof a contractual promise.

All data shared, collected, priced/modeled, underwritten andprofiled must credibly correlate back to this risk. This is thefoundation we must preserve and continue to build upon.

Related: 4 ways analytics can transform the claimsprocess

Robert Chester is an insurance examiner based in Hartford,Connecticut. Connect with him on LinkedIn.