Digital Transformation Technology Implications For Insurance Companies

As such, traditional user id and password based security will soon be viewed as deficient by consumers. Therefore, an additional security barrier will be essential to maintain consumer confidence. Initially, the addition of a personal question can be added to the sign-on process which is stored in a segregated database. However, this represents a temporary band aid. The emergent security paradigm is biometric authentication, which will initially be based upon fingerprint authentication.

Coordinating Data, Data and More Data

For Insurance Companies, consumer data retention has to be a top priority. Why? The key driver behind any behavior and associated predictive analytics is data. Insurance Companies need to establish their unique data enablement strategy. This is due to a host of variables which vary by organization including: current data retention which is extractable, specific data variables available, length of data retention, quality of data retained, etc. Often, the available organizational data has to be supplemented though the purchase of consumer specific data from third party data sources. The hiring of a data scientist is essential to participate in determining what data is mined from existing sources, which then forms a basis from which supplementary data requirements can be established. The final outcome from these activities is an initial analytical database.

In order to mine relevant behavioral and predictive data, the purchase of a purpose-built solution should be considered. The emergence of Big Data solutions, while providing an analytical framework and associated analytical tools, have a dependency related to what baseline data can be extracted from the organization.

Even if data analytics is not currently relevant to an organization today, enabling the retention of the maximum amount of consumer related data is critical. This non-specific data retention strategy is required, due to the fact that behavioral and predictive analytics represent an art vs. a science. The key data elements that enable behavioral and predictive analytics only become emergent when the data scientist performs their data modeling against a baseline data set. Thus data retention, prior to a potential future need, remains essential, as the value generated from behavioral and predictive analytics is directly tied to the breadth and depth of available data.

Utilizing the Cloud

Today, there is hardly a single computing user who has not stored data and or applications in the cloud. Some have even advanced to the point where the cloud is their single point of technology solution access. For Insurance Companies offering customer cloud technology, they are now accountable to keep user data safe and secure, while at the same time enabling the cloud to be a single point of technology access.

I forecast the broad based use of purpose-specific clouds. For example, there may likely be a cloud for Insurance Companies to market their products, against their competitors, where head to head competition occurs. CIOs I have spoken with, are not particularly worried about further cloud physical implementation, but rather are focused on how best to enable patent security for their cloud-based usage. Cloud security is an emerging security paradigm. Thus CIOs need to incrementally increase cloud security, based upon organizational relevant emergent security technology paradigms.

Conclusion

Stated directly, consumers are driving Insurance Companies to enable the integrated technology experience articulated in this publication. The associated implications to the organization are material. CIOs must become relevant to board level executives, as their input is an essential component, from digital distribution transformation project envisioning, resultant solution delivery scope, which then enables synergistic organizational “buy-in” resulting in the optimal organizational synergistic solution delivery plan.