Process Analytics

Insights from an insurance leader

Why choose process analytics?
Insurance leaders are looking to remove bottlenecks in the claims process.

Meet Connie…
Connie is head of claims at a large insurance provider, she’s seeking to increase the efficiency of claims processing and payment.

The process mystery
Most of the operations in Connie’s organisation are repetitive and manually intensive. The claims processing department has a unique challenge because it’s not easy to apply pre-set criteria. This makes automation more difficult. Connie’s team needs to manually process data from unstructured sources, a slow and error-prone process.

Connie has been aware of inefficiencies in the claims process for some time and has led multiple projects aimed at process detection and modelling. These projects have turned out to be time-consuming and expensive. This is due to knowledge gaps and poor objective validation techniques. Processes can become harder to understand when they involve many people.

The process mystery at Connie’s organisation extends to operating procedures, policies, and best practices. The effect of this mystery is that employees are not always certain what the correct behaviour is in a given situation. This uncertainty meant that there was little return on investment with traditional process management and modelling.

Competition pressure
New entrants into the market have placed additional pressure on Connie’s organisation. These new market pressures have brought process optimisation back on the agenda. However, the mystery remains and the leadership team cannot agree on how to objectively decide on the most appropriate process.

Switching focus
After a lot of consultation, the leadership team realised that they had been focusing too much on trying to figure out ideal processes. They realised the need for a digital detective to give impartial insight into their processes. So, they switched their focus to visualising the processes currently used by their top performers and using this information to improve business operations.

Agility and productivity
Connie’s organisation was able to use process analytics as their digital detective. This approach allows them to establish a single source of truth as to the organisation’s current processes. The process analysis project made bottlenecks apparent. Identifying these bottlenecks allowed the team to develop process models in far less time than previous projects. This kind of process modelling also makes it easier to update models as market conditions change. This has given Connie’s team confidence that they have the organisational agility to deal with sudden changes to the competitive landscape. They now consistently visualise a wide variety of processes, generate better quality data, and operational efficiency is increasing.

Analytics Automation
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Management People Analytics Supervised RPA
Operations Process Analytics Unsupervised RPA
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