Are you managing your digital ops team to deliver operational efficiencies? – Part 2
In Part 1, I looked at the project management view of the pre-bind process and where we can use automation, integration, and assisted decision support to gain operational efficiencies. In this blog, I’ll cover some of the key steps that take place in the underwriting process and how we can use digital transformation approaches to gain efficiencies.
Carriers can receive information about a new risk from several different sources (typically brokers) and through a varied array of technologies: email, placing platforms, online portals or face-to-face. The structure of the information that carriers are provided can also vary from propriety templates to structured formats such as ACORD and LM TOM Market Reform Contract.
There are advantages with entering the details of the risk into a system, although the process of entering a very high volume of potential risks comes with an operational overhead and therefore cost. Due to this overhead, many underwriters would prefer to store details of declined risks on a network and only expend effort and cost on risks that they deem relevant, can be profitable and fit within their risk selection parameters.
By storing details of declined risks in a structured data store, they can be used as a repository of opportunities that an underwriter can explore when more favourable times or conditions present themselves. They can also be employed to identify new products or services that an underwriter may like to explore.
Automating the process of deciphering emails or documents and risk details from a placing platform into a system of record can create a new submission which artificial intelligence and business rules applied to, that support the underwriter’s decision process of risk selection and classification.
2. Risk Selection and Classification
Underwriters cannot accept every risk that is presented to them, even when the risk falls within their guidelines. They make an intuitive decision and peer review high-value opportunities to make their best (profitable, low risk) selection. In some cases, an underwriter may accept a higher risk opportunity to put them in a favourable position on other risks.
Risk Selection relies on access to many data points, produced by several operational and risk specialist teams, as well as the underwriter’s intuition, something that I wrote about in my other blog, The Underwriting Decision at the Right Moment: Implied Knowledge. Classifying risks on value and relevance can free the underwriter from having to assess low-value, low-risk opportunities, which they would want to accept to concentrate on much higher value and newer opportunities.
When the details of a risk have been ingested into a system of record, several processes and rules can be run across the details which perform a similar role to the risk selection decisions. Low-value, low risk, high volume business can be selected automatically and assigned to operational and risk assessment teams to furnish the record with data points and reports that the underwriter can then review to classify the risk and start pricing. This can free the underwriter to concentrate on risks that are more complex and are of higher value.
Spreadsheets, love them or hate them, they are highly flexible tools that are still used to rate and price risks. Carriers are starting to explore pricing tools, and many have built their own in-house systems that provide their competitive edge.
The ops team or underwriter’s assistant, and even the underwriter in many cases take the risk details, attributes, coverages and perils and transform the information into a format that their pricing sheets or tools require. For complex details with specific requirements, this can be a lengthy task.
The operational team would also be required to assist the underwriting team with producing supporting data points such as exposure details and loss history.
Integration between the underwriting system and spreadsheet or pricing tool can reduce the time, effort, and cost of producing compatible information. Automatic task creation and assignments based on the pricing operations can also be used to inform operational teams of the information that is required.
Integration with exposure management, peril scoring, and loss history systems can present the underwriter with the information they require without the need to incur cost and more importantly wait for the information to be sourced and produced.
4. Issuance and Acceptance
Quote forms are typically issued as a document or an email with links to download further information. Producing the forms can be a manual activity that operational teams perform, although that is less common now as document production systems can easily integrate with systems of record.
The process of producing and reviewing the quote document is commonly a manual activity and can require a fair amount of time ensuring the inclusions and exclusions are accurate. Additional clauses and wordings may also be added based on the coverage and terms.
Low-value, high volume risks may require thousands of documents to be produced, and renewal periods can be a busy time.
Automating the renewal, quote and bind process for low-value and high volume risks can ensure the underwriter has a steady recurring revenue stream, and digital audits (business rules, compliance checks, wording checks) can release the operational teams to concentrate on high-value business.
Digital Ops Team
By pushing the low-value, repetitive tasks into background processes and designing a “digital ops team”, automation, integration, and intelligence can provide our “Ops Project Manager” with a digital team that drives business through the underwriting process. The checks and balances can ensure that underwriting leakage is kept to a minimum, tasks are completed efficiently and within SLA’s and revenue can be recognised much sooner.
Integration with legacy systems can be achieved with robotic automation connecting the “digital ops team” to all the systems used in the underwriting process. KPMG has commented on how robotics and machine learning can boost operational efficiency.
Ops managers can concentrate on managing a lean business focussed team instead of a data and process management team.