Claims automation can remove repetitive work, shorten processing times and improve customer satisfaction. The strongest results, however, start with understanding exactly how an insurance claim moves through the organisation before deciding which parts should be automated.
Why mapping claims workflow must come first?
A faster poor process still produces poor outcomes. Claims operations often contain duplicated checks, manual data entry, disconnected systems and informal handoffs that are invisible in standard process documents.
Deloitte’s robotics research found that while 53% of 400+ organisations were implementing robotic process automation (RPA), only 3% had scaled beyond 50 robots in 2017, rising to just 4% the following year. The gap highlights the importance of process design and governance alongside technology.
Before selecting a claims automation platform, map the real workflow from first notification to closure, including exceptions and workarounds. Record every system, decision, queue, document and handoff, with particular attention to duplicate data entry, waiting time and movement between core and satellite systems.
These friction points often offer the strongest opportunities for claims process automation.
What does the claims lifecycle look like, stage by stage?
A typical insurance claims processing workflow spans considerably more than the initial claim submission. Mapping the complete lifecycle makes it easier to see where automation can help and where human judgement still matters.
1. FNOL and intake
The first notification of loss (FNOL) may arrive through a call centre, web form, email, mobile application, broker or other channel. Information can be structured, incomplete or entirely unstructured.
This is a common starting point for intelligent document processing, natural language processing and automated data capture. Instead of asking an employee to read a document and manually enter its contents, an automated system can extract relevant information and populate the appropriate fields.
2. Validation and coverage verification
The claim is checked against policy information, coverage, exclusions, dates and other conditions. This stage often involves moving between the claims platform, policy administration system and external relevant data sources.
Map every system lookup and re-keying step. A process that requires an adjuster to copy information between existing systems may be a stronger RPA candidate than a more sophisticated AI use case.
3. Segmentation, triage and complexity scoring
Claims are assessed and routed according to factors such as value, severity, complexity and potential fraud risk.
Machine learning can support segmentation and prioritisation by identifying patterns across historical claims. The map should show exactly where a score is generated, who reviews it and what happens when the score falls outside an accepted range.
4. Reserving
The insurer establishes and updates the expected cost of the claim. Depending on the line of business, this can involve rules, historical information, adjuster judgement and actuarial input.
Mapping the process helps separate calculation steps that could be automated from decisions that require specialist oversight.
5. Investigation and fraud referral
More complex cases may be investigated internally or referred to a Special Investigations Unit (SIU). Fraud detection can use artificial intelligence and machine learning to identify unusual patterns, but the workflow still needs to show who investigates an alert, what evidence is collected and who makes the final decision.
6. Loss adjustment and supplier instruction
Adjusters may assess the loss, arrange inspections and instruct repairers, engineers, medical providers or other suppliers. These handoffs can create queues and delays, particularly when information has to be transferred manually between organisations.
7. Decisioning and adjudication
The insurer determines whether the claim is covered, what amount is payable and whether additional investigation is required. This is a critical control point for any automated claims processing initiative because decisions can have significant consequences for customers.
8. Indemnity and payment processing
Once settlement is approved, payment processing begins. Automated systems can check payment details, trigger approved payments and update the relevant records, while controls remain in place for exceptions.
9. Recovery, subrogation and salvage
The insurer may recover costs from another party, pursue subrogation or manage salvage. These activities often involve separate teams and external parties, making them useful areas to map for handoffs and rework.
10. Closure, reopening, complaints and appeals
A claim can be closed, reopened or challenged through a complaint or appeal. These later stages are easy to overlook when the focus is on the initial claims process, but they reveal whether earlier decisions created additional work.
Across the lifecycle, mark every handoff, queue, rework loop and re-keying step. The most persistent problems often occur at the seams between systems and teams rather than inside individual tasks
Mapping the process is step one. Here's what step two looks like.
How Future Processing helped Hiscox migrate 58,000 policies and turn months of pricing analysis into minutes.
Which mapping method fits which question: BPMN, SIPOC, value stream or swimlane?
Different mapping techniques answer different questions, so choosing one method for every stage can create unnecessary complexity.
SIPOC – Suppliers, Inputs, Process, Outputs and Customers – works well at the beginning of a project. It gives teams a quick view of the process boundaries and helps agree what sits inside the scope. Its weakness is that it can create a false sense of understanding: the general shape may be clear while the operational detail remains invisible.
Swimlane or cross-functional diagrams are more useful when handoffs are the problem. By separating activities by team, role or system, they show exactly where a claim moves from one owner to another. This is particularly valuable for claims operations because every handoff can introduce waiting time, rework or duplicate data entry.
Value stream mapping adds an economic perspective. It helps quantify cycle time, waiting time and other forms of waste. This is useful when the goal is to build a business case for claims automation and identify where reducing elapsed time would have the greatest effect.
BPMN 2.0 provides the most structured option. It is a standards-based notation that can represent events, activities, gateways, messages, exceptions and participants in a way that can support detailed process design and, in some environments, execution.
For most claims automation programmes, a hierarchy works best. Start with a high-level SIPOC or value-stream view, use swimlanes to expose handoffs, then develop detailed BPMN models where automation or redesign requires precision.
Most importantly, map the current state first. Capture the exceptions, workarounds and awkward steps alongside the clean path. The “warts and all” version is usually the one that reveals where automation will have the greatest impact.
Process mining or workshops: how do you discover the process that runs?
Workshops show how employees believe the claims process works; process mining shows how it actually runs. Interviews remain valuable for understanding why claims are moved between queues, why workarounds exist and where employees depart from the official process.
The limitation is that workshop maps often reflect the intended or most familiar path. Rare exceptions, long queues and repeated rework can easily disappear.
Process mining reconstructs claims processing paths from event logs. At minimum, these need a Case ID, Activity and Timestamp: the Case ID links events to a claim, the Activity records what happened and the Timestamp establishes the sequence. Additional data such as team, claim type, value and channel provides greater insight.
Conformance checking then compares the actual process with the expected model, highlighting skipped approvals, unexpected routes, out-of-sequence activities and repeated rework.
The main constraint is event-log quality. Missing timestamps, inconsistent case identifiers and gaps between systems can produce an incomplete picture. For this reason, the strongest approach combines workshops for context with process mining to validate those assumptions against real claims processing data.
Is your data and architecture ready for claims workflow automation?
Before automating a claims process, check whether the underlying architecture can support it.
Start with the core claims platform. Determine which data can be accessed through APIs, which transactions require a user interface and which activities still depend on spreadsheets, email or manual uploads.
This matters because robotic process automation can bridge gaps between applications, but using RPA as a permanent substitute for system integration can create fragile automation. Screen scraping may work when no interface exists, yet changes to a screen layout, authentication method or application workflow can break the robot.
The better long-term architecture usually separates orchestration from individual applications. A workflow layer can coordinate the claim, call existing systems through APIs where possible and route exceptions to employees. Document processing, fraud detection, payment processing and other specialist capabilities can then plug into the wider workflow.
For older estates, robotic process automation SPA solutions can still have a role where replacement or integration of a legacy system would be disproportionate. The key is to understand why RPA is being used and where the dependency sits in the wider architecture.
Data quality deserves equal attention. Claims automation depends on accurate policy records, consistent customer information, reliable timestamps and accessible documents. Artificial intelligence can help interpret unstructured information, but poor source data will still create poor downstream decisions.
Which metrics must you capture before you automate?
Automation needs a baseline. Without one, it becomes difficult to prove whether a new system has improved claims processing or simply moved work somewhere else.
Start by separating cycle time from touch time. Cycle time is the elapsed time from one defined point to another; touch time measures the hands-on work performed by employees. A claim can have low touch time but a long cycle time because it spends days waiting in queues.
Useful measures include:
- Touch rate: how many claims require manual intervention?
- Straight-through processing (STP) rate: what proportion reaches settlement without human intervention?
- First-notification-to-settlement time: how long does the complete journey take?
- Rework and exception rate: how often does a claim return to an earlier stage?
- Leakage: where are avoidable payments or process losses occurring?
- Indemnity spend: how much is paid to settle claims?
- LAE ratio: how much loss adjustment expense is associated with the claims portfolio?
- Customer effort and NPS: how easy is the claims experience?
- Complaint volume: how frequently do customers challenge the process or outcome?
These measures also help identify the right automation target. If a process has high touch rates but low cycle time, the opportunity may be labour efficiency. If touch time is modest but claims spend days waiting between teams, workflow redesign may matter more than automation of individual tasks.
The measurement gap across the industry remains significant. Capgemini’s World Property and Casualty Insurance Report 2026, based on research involving 344 insurance executives, 809 employees and 1,113 policyholders, found that 42% of insurers do not measure AI outcomes, while around 60% remain in exploration or proof-of-concept stages and only about 10% are scaling successfully.
That makes baseline measurement a practical requirement for claims automation rather than a reporting exercise.
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What do the FCA, GDPR, the EU AI Act and DORA require of an automated claim?
Automation changes the control environment as soon as an automated system influences a customer’s claim.
Under FCA rules, ICOBS 8.1 requires claims to be handled promptly and fairly. GDPR Article 22 also applies where personal data is used for solely automated decisions with legal or similarly significant effects, with the CJEU’s C-634/21 SCHUFA judgment reinforcing safeguards around such decisions.
For EU insurers, the EU AI Act classifies certain AI systems used for risk assessment and pricing in life and health insurance as high-risk, bringing requirements around risk management, transparency and accountability. DORA adds operational resilience and third-party oversight where external technology providers support critical functions.
The process map should therefore show where AI is used, what data it receives, how decisions are made, where human review occurs and how exceptions are handled. Keeping inputs, outputs, model versions, approvals, overrides and timestamps also makes decisions auditable.
The same controls apply to fraud detection, where AI and machine learning can flag suspicious claims for investigation. Intelligent automation works best when workflows, data, decisions and controls remain visible throughout the claims process.
