Underwriters unchained
Underwriters unchained
Insight

Underwriters unchained

Reducing the time and improving the accuracy of a property and casualty insurance quote for a small commercial business from weeks to just a few days is no longer a dream.

Underwriters can now quickly and profitably issue a policy by leveraging a KPMG intelligent automation-powered tool designed to evaluate risk profiles and accurately price policies by gathering and interpreting “signals’’ about data from sources that surround and that are inside insurers’ businesses.

The KPMG Intelligent Underwriting Engine allows underwriters to quickly apply enormous amounts of data, involving geography, property value, population statistics, the industrial landscape, social media behavior, crime, or thousands more variables used in risk profiling and pricing.

In addition to the amount and type of data being important in underwriting, we find that the information about the risk – what we call its signals – is critical to reducing underwriting time, lowering costs, more accurately pricing a policy, while limiting manual adjustments, and – perhaps most important – enhancing the experience of the customer.

This all means that underwriters, who for decades were saddled with having to perform mundane and manual data-gathering and -validation tasks, can now focus on adding value to their business. They can devote more time on their portfolio of policies and use advanced predictive modeling techniques to search for rating and marketing solutions. Underwriters also can take on other value-add challenges they may not have had time to address when doing the work now being done by the intelligent engine.

Our Intelligent Underwriting Engine employs machine learning combined with our proprietary KPMG Signals Repository, and the engine is deployed on the Microsoft Azure Cloud, with access to Azure’s application program interface capabilities, including machine learning and cognitive capabilities.

At the moment we have deployed the KPMG Intelligent Underwriting engine in the small commercial business property and casualty market, which is:

  • Growing.
  • Profitable.
  • Facing significant cost pressures.
  • Seeking to address high-volume, low-premium issues.
  • Already being disintermediated by nimble, savvy insuretech competitors.
  • Wrestling with the challenge of managing enormous amount of information (largely untapped) that is directly related to profiling risks and pricing.

The KPMG Intelligent Underwriting Engine has three discrete implications:

People and skills impacts:

  • The underwriter can be the “custodian’’ of the process and provide learning to “bots’’ that are performing the rote, repetitive, manual work that underwriters had been doing for decades.
  • The engine is instrumental in developing innovative pricing rules and frameworks, particularly for multifaceted policies and risks.

Process impacts:

  • Processes are streamlined as increases in efficiency reduce the time to issue a policy.
  • Continuous improvement becomes inherent in the underwriting process.

Technology impacts:

  • There is the creation of leverage through the technologies, such as intelligent automation and predictive analytics, which generate real-time insights.
  • There is more use of cognitive platforms that make predicting and assessing risk more accurate and timely.

While this engine currently is largely devoted for use in small-business commercial underwriting, we see a time in the not-so-distant future when other parts of the insurance value chain (claims, marketing, customer service) can use the same technology to save time, enhance customer experiences, and lift the bottom line.

Time for the underwriter to meet the future … today.