Lessons LearnedThere is anticipation and excitement right now in Silicon Valley concerning disruption in the insurance industry.  This is due the excitement over recent financial technology discoveries and innovations (think Blockchain).  If you search Twitter and other social media websites, you can observe all kinds of interactions between start-ups, academia, and the insurance industry.  Government is now getting involved in the discussions, too.

This year, I was fortunate to attend two of conferences focusing on technology disruption for the insurance industry.  There are some amazing and brilliant people working on various concepts and platforms which will allow the insurance industry to be more efficient and prosperous, indeed.  Yet there has been one topic missing from all of these discussions – – claims.

Our on demand generation is requiring businesses to change the way products are marketed and sold.  People want to research a product, compare the best offers, and then purchase the product by using their smartphones.  Thus, there is a lot of effort to make that happen in the industry.  But what about claims?  Claims costs are increasing.  The cycle to receive a claim and settle it seems to take too long (the on demand generation’s expectations).  The adjusted claims expenses can be decreased, along with the claim cycle, and increase the volume of customers in one seamless process

Lessons Learned

Validating genuine claims accurately, efficiently and promptly is the best way to retain customers and grow your book of business.  The fraudulent claims cost the industry billions of dollars annually, and the nefarious activity seems to be increasing across all lines of insurance.  The organized criminal enterprises are flourishing and increasing their activities for this genre of white-collar crime.

Conducting a claims investigation can be summed up as intuitive and technical in nature.  What I mean is that 20 percent of conducting an investigation is comprised of technical skills, while the remaining 80 percent are intuitive and based on work and life experiences.  I used to really enjoy speaking to new law enforcement officers about this and point out that upon graduation from training, all of them will be able to successfully investigate cases and bring them to a proper conclusion.  However, not one new officer would complete an identical investigation at the same time.  The main difference is based on professional and life experiences.  So how do we tap into the intuitiveness of an accomplished claims representative and SIU investigator?

Welcome to next generation analytics with machine learning and data enrichment!

Next generation analytics for the insurance industry must have machine learning and data enrichment on the analytics platform to achieve better results in the claims process.   Bringing together the underwriting data sets and the claims datasets together into one cohesive and seamless process is the key to getting started.

Dynamic modeling must be introduced into the insurance business process.  Simply put, there are three basic rules for dynamic modeling:

  1. Gather the data
  2. Enrich external data, integrate and analyze
  3. Make the right decision

Machine learning is a form of artificial intelligence that can be very powerful when applied to specific algorithms in analyzing past claims and underwriting data sets.  There are numerous hidden links and connections in the patterns of activity of insureds, claimants, product – coverage usage and pricing. Next generation analytics will allow you to discover these patterns and trends much more accurately, efficiently and quickly.

Now is the time to start getting involved in the disruption and innovation discussions in Silicon Valley for the insurance industry.

We will be at the SVIA Q3 Conference on September 7th & 8th[1] leading this discussion, and look forward to collaborating with our partners and peers on next generation analytics for the insurance industry.