Adopting intelligent automation in claims

Adopting Intelligent Automation in Insurance claims

Automation means many things to many people. But what does it mean for insurance claims professionals? 

Automation is often promised as a cure-all for a whole host of operational claims issues. From bringing down the high costs of claims administration to reducing the risk of fraud or human error to delivering frictionless service to consumers who are used to smooth services elsewhere.

At an organizational level, it’s also pitched as a panacea for dealing with data volumes that are fast becoming too much for humans to handle. Or as a way to standardize processes on a global scale and to improve services or increase productivity to compete with digital natives.

All of this may well be possible – and yet, other than some small-scale wins, examples of the transformational outcomes promised by automation are hard to come by. So why is this?

Recognizing the challenges

While claims departments face all of those aforementioned issues, there are bigger factors at play too. Wider insurance operating models typically involve multiple sources of data. Processes are ‘owned’ by different parts of the value chain and, while some parts are highly digitalized, many others are not. 

Off-the-shelf approaches—like standard robotic process automation (RPA)—may work for some generic processes, but every claims department, every company, and every part of the claims ecosystem is very different. Data quality remains a key issue, as teams often process unstructured as well as structured data, and this makes it harder to apply standard automation.

It’s not just the data, workflows, and ownership that are the key challenges. As one Forrester report puts it:

Automation technologies…are transforming business processes and operating models. But most companies don’t have the competencies to implement automation technologies successfully. It’s not that they lack the technology prowess; rather, it’s the people, leadership, and organizational structures that hold back success.

RQ 2.0: Assess Your Readiness For Artificial Intelligence, Automation, And Robotics – Forrester

The lack of successful transformation through automation is not exclusive to claims nor the insurance sector. It is a much wider challenge and there are a number of reasons for this:

  • Standard automation typically delivers change in one area or one process. It doesn’t impact the whole value chain. 
  • Standard automation is often just software that is designed to follow a set of pre-programmed rules. So it cannot handle the knowledge-based aspect of the workflow.
  • Standard automation tends to be focused on implementing a single technology. This approach lacks clear alignment with strategic business outcomes.

Changing the approach

For transformative change, claims departments need to look beyond standard automation towards Intelligent Automation. Intelligent Automation—or IA—leverages a new generation of software-based automation, including artificial intelligence, machine learning, natural language processing, computer vision, and unstructured data processing.

IA combines these methods or technologies to execute business processes automatically on behalf of knowledge workers. This is typically achieved by learning and mimicking the capabilities that knowledge workers employ when performing their tasks.

Implementing IA requires specific skills that aren’t always prevalent in claims departments (or insurance companies at large). What’s more, claims automation isn’t just a decision about whether to automate. It’s a decision to re-engineer claims processes and implement new ways of working. To be able to make that decision and then to benefit from IA transformation, claims departments need to present automation in a way that makes sense to both technical and business teams. It is important to point out that IA does not remove human knowledge or assessment from the claims cycle. But it does free teams from the mundane, repetitive and error-prone processes that eat up far too many resources and prevent claims professionals from spending more time with customers.

To achieve this, IA has to be an iterative, value-led approach. Fast feedback and rapid lessons learned at each stage of the transition will lead to new ideas or improvements that can be applied to the overall workflow. This is where the Emergn approach to Intelligent Automation can make a difference.

Delivering transformational outcomes

Emergn takes a strategic approach to all our engagements and none more so than with Intelligent Automation. Following our VFQ principles (Value, Flow, Quality®), we provide value early and often while enabling organizations to implement ways of working that save operational costs, quickly introduce new value propositions, and – ultimately – transform the business.

Instead of focusing on point solutions and individual technologies, we work alongside clients to design and implement IA that fulfills core business objectives through a three-step approach:

Step 1 – Structured approach to realizing automation potential

We explore how and where automation can be applied to have the most impact on the wider organization. Through a series of practical, interactive workshops, we analyze detailed use cases, prepare automation designs and define methods for calculating the benefits and priorities.

Step 2 – Rapid validation of benefits and approach

Based on a roadmap of use cases, we validate benefits through incremental and rapid proof of concepts. Using agile delivery practices and by selecting the tools and technologies best suited for automating claims at your organization, we deliver automation use cases in a quick sequence.

Step 3 – Scaling and building capability for the long term

Using our Center of Excellence model, we ensure the right platforms, ways of working, and governance are in place to scale capacity and maximize your claims automation process. We do this by blending education and pairing to create the most effective way to transfer experimentation principles, processes, and knowledge to your teams.

By focusing on strategic outcomes, including different people in the activity, and visualizing the automation landscape, we bridge the usual divide between business and IT professionals. By bringing together claims professionals, data specialists, and automation experts we are able to rapidly prove concepts for high-priority use cases. And by demonstrating how best to structure IA capabilities and new ways of working, we enable our clients to continue delivering on strategic outcomes long after our engagement comes to an end.

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