Insurance is still primed for automation
AI is changing how many insurers handle data and decisions. It can analyse images, detect patterns and surface insights quickly.
But AI has limits. It works by identifying patterns and making predictions. That makes it helpful for exploring options, but not always reliable for final decisions. In regulated industries like insurance, that uncertainty creates risk.
Business rules provide clarity. They are specific, repeatable and auditable. When AI is combined with well-structured business rules, insurers can improve performance without losing control. AI adds interpretation. Rules guide the outcome.
This combination is especially useful in areas like claims, underwriting and compliance, where accuracy, speed and accountability all matter. Here are seven use cases where insurers can apply automation effectively, using AI to enhance the process and rules to ensure consistency.
1. Claims intake and assessment
The problem
Even with digital portals, claims teams often spend time manually checking for missing documents, classifying claim types and chasing follow-ups.
The automation win
- Automatically validate submitted claims for required fields and documents
- Trigger tailored document requests based on claim type
- Categorise and route claims based on value or complexity
The balance of AI and rules
FAI can assist by analysing images, predicting claim categories or highlighting anomalies. But rules define what is required, what gets auto-approved and what needs escalation.
Impact: Shorter turnaround times, less manual case handling, and improved transparency for customers.
2. Underwriting pre-checks and risk scoring
The problem
Underwriting is one of the most expensive and time-consuming parts of the insurance process. While automation has helped fast-track low-risk applications, confidence in automated decisions for complex cases like life insurance is still a challenge. These situations require accuracy and trust, both for the business and the customer.
The automation win
- Validate data automatically, such as credit scores or asset values
- Route low-risk applications straight through when criteria are met
- Flag incomplete or inconsistent inputs before review
- Highlight potential risk areas using historical data
The balance of AI and rules
AI brings valuable context. Business rules determine how to act on it. Together, they support faster, more consistent decisions while giving underwriters clarity on where to focus.
We have seen this in action with insurers like Fidelity Life, who improved speed and flexibility by separating rules from legacy systems.
Impact: Quicker decisions, fewer delays and a better experience for customers.
3. Policy renewals and compliance checks
The problem
Policy renewals often rely on outdated information. Manual reminders and compliance checks slow everything down.
The automation win
- Notify customers automatically when KYC or ID documents need updating
- Flag policies that no longer meet compliance criteria
- Auto-renew simple policies without manual intervention
The balance of AI and rules
AI might flag unusual account behaviour or lapse risk. Rules ensure the renewal complies with current regulatory requirements.
Impact: Better compliance, fewer missed renewals and improved operational efficiency.
4. Fraud detection and anomaly alerts
The problem
Manual fraud checks are slow and often reactive. Teams rely on experience rather than consistent signals. Fraud indicators often go unnoticed until late in the claims process, costing time and money.
The automation win
- Monitor claims for suspicious patterns or data inconsistencies
- Flag and prioritise high-risk claims for manual review
- Maintain logs and trigger investigations based on predefined criteria
The balance of AI and rules
AI is excellent at surfacing complex patterns and detecting anomalies. Rules determine how those signals are handled, such as when to escalate, when to hold and when to pay.
Impact: Proactive fraud prevention with fewer false positives and a clearer audit trail.
5. Billing and payment workflows
The problem
Invoicing, payment reminders, and reconciliation are still highly manual in many teams, leading to delays and missed revenue.
The automation win
- Generate and send invoices automatically
- Trigger payment reminders before due dates
- Reconcile payments across multiple channels, including fintech platforms
The balance of AI and rules
AI can help predict payment behaviours. Business rules define how to act when payments are delayed or disputed.
Impact: Improved revenue reliability, reduced admin time and better customer experience.
6. Policy management and document handling
The problem
Updating policyholder information, changing beneficiaries, and sending renewal notices still create unnecessary backlogs for service teams.
The automation win
- Handle routine policy updates like address changes or beneficiary edits automatically
- Send instant digital confirmations and policy documents
- Auto-flag documents that require human review
The balance of AI and rules
AI can extract and interpret document content. Rules decide when to accept, reject or escalate changes.
Impact: Faster policy servicing and more time for high-value customer interactions.
7. Regulatory compliance and reporting
The problem
Compiling compliance reports requires hours of cross-checking data from multiple systems. Staying on top of regulatory changes is resource-intensive.
The automation win
- Auto-generate standardised compliance reports
- Maintain a complete log of decisions for audit purposes
- Trigger alerts when workflows need updating due to new regulations
The balance of AI and rules
AI can scan large volumes of regulatory updates. Rules ensure the right actions are taken in response.
Impact: Reduced compliance risk and faster reporting cycles with less effort.
How to prioritise what to automate
Even if you already have a digital claims platform or a rules engine, there are still hidden inefficiencies. Look for:
- Repetitive decisions your team makes dozens of times a week
- Routine updates or approvals that delay customer outcomes
- Compliance checks that are inconsistent or bolted on late in the process
- Manual workarounds where systems don’t talk to each other
These are ideal candidates for automation guided by business rules. AI can support by identifying risk signals or patterns, but rules are what ensure decisions are applied consistently and with confidence.
What’s next?
You do not need to transform everything at once. Even one or two improvements in core workflows like underwriting or claims can reduce processing time, lift customer satisfaction and lower risk.
If you want help identifying where to start, try our 2-minute Business Rules Health Check. It will help you pinpoint friction in your decision-making processes and prioritise what to automate next.
Why this matters for modern insurers
Insurers today are balancing speed, accuracy and trust. Customers, regulators and internal teams all have different expectations. Automation supported by rules and AI helps meet all three.
- Customers expect quick, seamless decisions, even for complex claims.
- Regulators expect consistent, transparent processes.
- And your teams need to focus on high-value work, not repetitive admin.
Bringing AI and business rules together helps modern insurers deliver consistent decisions faster, without compromising on control or compliance.
About Digital Experience Labs
At Digital Experience Labs we specialise in practical, business-first rules automation. We don’t start with tools or technology. We start by listening.
We run collaborative workshops to map your real-world processes, ask the questions others might miss, and help you define the logic tha drives your business.
Along the way, we often find issues you weren’t looking for or uncover simpler solutions than expected. Then we help you implement automation that works – not in theory, but in practice.
No jargon. No overcomplication. Just clear thinking and effective smart automation that makes your business easier to run.