Below is a snippet from the article “Analytics process automation promises big benefits for re/insurers,” by Michael Parcelli, head of global solutioning and process at Xceedance. To read the full article on the Insurance Day website (subscription required) please click this link.
The insurance industry is beginning to catch up with other financial sectors in the use of technological advances such as analytics process automation (APA), but it still has a long way to go. Until our industry gets to grips with deep learning, it will be like a car manufacturer trying to upgrade a combustion engine vehicle rather than recognising the inevitable switch to electric cars. APA is a useful technological tool, increasingly used within the insurance value chain to automate time-consuming and tedious processes. By examining data, it can also simplify decision-making for the user. APA allows all aspects of the insurance lifecycle to be centralised on the same platform and is best compared to multiple assembly lines working collaboratively.
For each individual assembly line, APA enables a unified, centralised structure in which data is captured, processed and eventually presented contextually to the user for actionable decision-making on that information. This centralised automation makes it easier for incoming data to be consumed and managed byre/insurers and then governed for matters of compliance such as the General Data Protection Regulation (GDPR) in the UK or privacy laws in the US.
Having a single, standardised methodology to coordinate and manage incoming data also enables are/insurer to show it has a control mechanism, while a centralised and standardised approach helps to reduce waste and the need to rework. Aggregating the bespoke processes into a centralised dashboard will result in greater efficiency and value of APA.
APA looks for opportunities for a task to be automated. There is a spectrum of automation that exists between objectivity and subjectivity – the more objective a task is and the more predictable the outcomes, the more it can be automated. In 1913, Henry Ford developed the first moving assembly line for the mass production of automobiles, reducing the time it took to build a car from more than 12 hours to just one hour and 33 minutes. But doing the same task over and again was considered terrible drudgery, which is why Ford paid high wages. Over time, manufacturers realised this kind of work was boring and simple and a machine with limited cognitive capacity could carry out the same task with greater efficiency and at a lower cost. There is a spectrum of automation that exists between objectivity and subjectivity – the more objective a task is and the more predictable the outcomes, the more it can be automated. Today, automation provides not only the assumption of the process, but also a resulting decision about what will be done as the next step in the sequential workflow.
The result is decisions about tasks that are repetitive, of low complexity and high objectivity can be made by a computer programme. An example of this for insurers is business rules, where submission insurance documents are processed and an objective decision is reached about whether it is agreeable for the company to underwrite a risk or pay a claim. Data entry from insurance application forms is another example of a very low complexity activity that was once carried out by an analyst or processor. Now there are tools that extract the information from forms and populate the system, removing direct human intervention from this time-consuming and error prone process. The benefits of using this kind of technology include increased accuracy and reduced decision-making time, which enables insurance professionals to up-skill and spend less time on tedious, low-value work.
Read the full article on the Insurance Day website (subscription required).