Process Mining in Insurance: Current challenges

Insurance companies across the globe face similar challenges — from the impacts of fraud to the acceleration of climate change and from staff shortages to the inflexibility of core IT systems.

The digitization of processes within insurance has long been seen as a solution to addressing the inefficiencies within this highly interconnected industry. The pressure has increased with the introduction of new digital-first insurers, which can generally offer a higher quality customer experience more akin to an online retailer or bank.

It means insurance companies need to find a way to deliver superior customer experiences, but this involves fixing process performance across their back-, middle- and front-offices, as well as providing consistent services across multiple channels including mobile, online and phone.

However, the insurance industry faces several challenges when it comes to digital transformation and the adoption of intelligent automation (IA), from legacy systems and cost pressures to complex operating structures and siloed functions. 

What Is Process Mining in Insurance?

Many insurers are still at the beginning of their automation journey, and the concept of widescale, cross-sector digital transformation can be daunting. One place that insurers choose to begin is process mining.

Process mining in insurance defined

Process mining is a solution that enables insurance companies to discover and extract data from their systems to help them understand, monitor and improve real insurance processes. Process mining is part of a larger process intelligence strategy. Process intelligence uses data-driven insights from process mining and other analytics to optimize, continuously improve and manage insurance processes.

Essentially, instead of trying to stay on top of how well business processes, such as the underwriting process, are running by using reports and dashboards, insurers can use process mining to look inside how their people and systems are managing particular systems of work in real time — and understand exactly which processes to improve with automation.

Examples of process mining in insurance

Here’s a list of how process mining can help in insurance:

• Understand the claims management process: Analyze the entire journey from the moment a customer contacts their insurer to the final payout, identifying all steps and variations.

• Identify manual vs. automated processes: Determine which sub-processes in claims management are still handled manually and assess opportunities for automation.

• Optimize process execution: Explore possibilities for running sub-processes simultaneously to reduce delays and improve efficiency.

• Regulatory compliance: Ensure that processes adhere to industry regulations by providing transparent process documentation and alerts for non-compliant activities.

• Resource allocation: Optimizes the allocation of resources by analyzing workload distribution across different teams and processes.

• Fraud detection: Analyze patterns in claims data to detect potentially fraudulent activities.

• Improve customer experience: Helps identify pain points in the claims process that cause delays that affect a smooth customer experience.

How does process intelligence help you do more than process mining

Process intelligence builds more functionality on top of process mining, enabling insurers to take the next steps forward with intelligent automation. SS&C | Blue Prism® Process Intelligence, powered by ABBYY Timeline, offers tight integration between process mining, task mining, robotic process automation (RPA) and IA.

This means that once insurers have used process mining to identify areas for improvement, they can start thinking about how they deploy digital workers to speed up tasks, improve accuracy and reduce operational costs. Then, insurers can also weave in new technologies like generative AI to streamline processes even more.

Process Intelligence example in insurance

Instead of having expert staff work constantly to move data from one system to another, digital workers can take up the heavy lifting. They can access the same applications and systems as humans, which means they’re able to take on laborious data handling and migration tasks, while freeing up people to undertake higher-value work — all without disturbing core systems.

Here are a few reasons why Process Intelligence goes further: 

Visibility across multiple systems	Process Intelligence creates a complete history of every process iteration from beginning to end, even when some steps are performed using multiple systems. Recorded as timelines, processes can be compared, filtered, searched and aggregated to look for inefficiencies and potential for improvement. Unlike process mining, which uses a schema-centric approach, Process Intelligence uses a numerical analysis approach that works equally well on all types of processes, not just on those processes with little variability in terms of the sequence of steps.
Continuous monitoring and improvements	Process Intelligence monitors every iteration and alerts process owners about any deviation that could cause delays. This means it enables continuous improvement, and continues to deliver return on investment as businesses operate, not just at the initial stages.
Compliance delivered	Traditional process mining applications enable users to review process outputs to identify present and past deviations that could lead to compliance issues in the future. While this approach relies on the expertise of the users reviewing the data, Process Intelligence allows organizations to define process rules, then trains the system to identify problems and alert the business. This approach can extend into calling for a human to step in and fix the issue.

How can process intelligence accelerate automation?

The fact that Process Intelligence looks at end-to-end processes, even when they are running across different systems, creates more opportunities to prioritize processes with the greatest automation potential in terms of cost-savings and efficiency gains. These factors can be quantified with data-driven return on investment calculations based on factors such as the number of transactions, process duration, number of process steps and cost per transaction.

How to target high-value automation opportunities

Process Intelligence also helps insurance organizations avoid automating broken or poorly executed processes. It gives organizations “as-executed” process visibility, so they can see where the bottlenecks or compliance risks are. They can then redesign or fix the process before investing in automation that will not deliver expected benefits, or that will have to be redone.

Monitoring automated processes after deployment

Organizations can use Process Intelligence to do more than identify opportunities for automation. They can continue to monitor the impact of an automated process to ensure ongoing protocol compliance, for example.

This can include monitoring scenarios where digital workers incorporate human assistance and ensure that the right alerts are sent to the right people in the business. And, last but not least, real-life cost impacts can be monitored too, helping to justify future automation initiatives.

Proven Benefits of Process Intelligence and Automation in Insurance

Improving claims processing

Insurers’ processing operations are built around a complex network of interconnected and interdependent processes. When looking at how to reduce operating costs through automation of claims processing, insurers need to understand exactly how they manage it currently — this includes all participants, from internal staff to contract adjusters and brokers, as well as third parties such as repair shops.

With Process Intelligence, insurers can map this network of different workstreams into a timeline and understand where, when and why there are interruptions or delays. SS&C Blue Prism Process Intelligence provides a completely new way to analyze different patterns of execution across all or any filtered subset of claims.

Here you can see an insurance claim process visually represented in our tool. On the left, you can see the entire process is color-coded and key milestones in the process are easily identifiable.

Insurers can quickly identify the red deviations from expected behavior or situations where any step may be skipped, repeated or missing entirely. Any pattern of interest can be used to drill down directly to those claims that exhibit that behavior. For example, you can drill down the most commonly executed series of steps:

With this view, insurance organizations can then compare all the steps according to several factors using filters, such as average throughput time, skipped steps, repeated steps and even costs.

Create Smarter Insurance Processes

Improving the efficiency of claims processing operations is just the start of the benefits that process mining and intelligence can bring to insurers. Along with optimizing multiple connected steps in the claims cycle, insurers will also deliver a better customer-centric experience, which will help build loyalty and reduce attrition.

And with insurance process analytics, insurers can deliver business value for internal staff, contract adjusters, third-party support organizations, and other partners. It’s a win for everyone operating in the insurance ecosystem, and it promises to deliver transformation to an industry that’s long overdue for modernization and reform.