Staying agile post-pandemic

Staying agile post-pandemic | Insurance Business

Staying agile post-pandemic

Last year brought the insurance market increased competition, changing consumer expectations and agile opportunities, thanks to the pandemic’s working models.As COVID-19 bleeds into 2021, traditional insurers are increasing their investments in digital, agile and partnerships. The certainty of these investment decisions is likely to be with us long after the pandemic fades. We will continue to adapt to new agile operating models – likely at a pace that we historically have never experienced.

Insurers are increasingly comfortable with experimentation, a ‘fail fast’ attitude and quick partnership explorations with tech startups to scale their business. Agile is a matter not just of resources or market reach, but also of creating new bedrock business platforms and processes. Insurers’ ability to work with multiple partners simultaneously enables the quick movement from pilot to market to busi-ness as usual. The apparent winner will be the one that innovates, creates and can scale.

Claims processing, for instance, has always been conducted by an insurance adjuster. This model worked well in the past, but today the average insurance company can expect to have hundreds or even thousands of claims submitted in a single day. The quantity of information on a single claim has also skyrocketed to include information ranging from telematics to property sensors. Despite this surge in data, only 5% of insurance companies currently depend on process automation to review claims.

Why is that? Well, it could be as simple as vocabulary. It’s been documented that  most  adults  have  a  vocabulary  range  of 30,000  to  35,000  words.  The  experts  tell  us  that  to  be  conversationally  fluent  in a foreign language, we need  to  know  1,000  to  3,000  words.  Applying  this  logic  to  insurance,  the  terms  glossary  of  the  US  National  Association  of  Insurance  Commissioners  contains  approx-imately  600  definitions,  the  Construction  Design  catalogue  approximately  500  terms,  and let’s add a 1,000-word vocabulary used by every adjuster.

“Insurers are increasingly comfortable with experimentation, a ‘fail fast’ attitude and quick partnership explorations with tech startups to scale their business”

Tools  are  being  built  today  with  that  2,000-plus-word  vocabulary  and  the  ability  to  ingest  large  amounts  of  data,  including  unstructured  text,  and  to  parse  and  learn  from  that  data.  My  favourite  example  of  this  type  of  deep  learning  is  Google’s  AlphaGo.  Google  created  a  computer  program  with  its  own  neural  network  that  learned  to  play  an  abstract board game called Go, which requires sharp  intellect  and  intuition.  By  playing  against  professional  Go  players,  AlphaGo’s  deep  learning  model  learned  how  to  play  at  a  level  never  seen  before  in  artificial  intelligence.  It  caused  quite  a  stir  when  AlphaGo  defeated  multiple  world-renowned  masters of the game – not only could a machine grasp the  complex  techniques  and  abstract  aspects  of  the  game,  but  it  was  becoming  one  of  the  greatest players of it as well.

Insurance executives  have  long  struggled  to assess the business value of AI. They understand  the  potential,  but  the  general  lack  of  institutional AI knowledge has made the evaluation  process  somewhat  uncertain.  Despite  the uncertainty, executives remain undeterred from doubling down on their AI investments: 71%  of  AI  adopters  plan  to  increase  their  spending  by  an  average  of  26%,  according  to  a recent Deloitte study.

The  reason for  the  flurry  of  investment  is  that insurance C suites envision several operational benefits too exciting to pass up.

  • Machine learning to determine repair costs and  automatically  categorise  the  severity  of  damage  to  vehicles  involved  in  accidents, whether the damage is from a collision or hailstorm
  • Internet  of  Things  (IoT)  sensors  to  mitigate  risk  and  reduce  losses,  plus  the  use  of  home  and  industrial  IoT  data  to  build  operational  intelligence  on  the  frequency and severity of accidents and feed into underwriting and product pricing
  • Process mining techniques to identify bottle-necks and improve efficiencies and conformance with standard claims processes
  • Increased transparency for all parties, faster claim settlements, and better customer experience and CSAT scores Agile insurance could be the new AI.

 

Vijay Pahuja is corporate SVP of client services for WNS, a provider of global business process management services.