Intelligent automation is poised to disrupt the insurance industry by optimizing processes, reducing costs, and delivering superior customer experiences. This comprehensive guide dives deep into the key use cases, examples, benefits, challenges, and future outlook for intelligent automation in insurance.
What is Intelligent Automation and Why Does Insurance Need It?
Intelligent automation refers to the combined use of robotic process automation (RPA) with artificial intelligence (AI) technologies like machine learning, natural language processing (NLP), and computer vision.
While RPA can automate repetitive, rules-based tasks, AI enables digitization of unstructured data, understanding of context, and data-driven decision making. Together, they allow end-to-end automation of complex workflows requiring judgement and analysis.
The insurance industry is ripe for transformation through intelligent automation due to several factors:
- Highly manual processes leading to inefficiency and errors
- Large amounts of unstructured data like claims documents and call center records
- Need for real-time responsiveness to customers
- Manual compliance management that is prone to gaps
- Focus on cost control in a competitive market
According to McKinsey, intelligent automation could boost productivity in insurance by 30-80% and drastically improve turnaround times.
Let‘s look at some of the most impactful use cases.
Key Use Cases for Intelligent Automation in Insurance
Claims Management
Claims processing is a prime target for automation given the high volumes and extensive paperwork involved. Here are some ways intelligent automation streamlines claims:
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FNOL Intake: Chatbots or conversational AI tools guide customers through First Notice of Loss (FNOL) by asking relevant questions and capturing structured data.
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Document Processing: Insurance documents like claims forms, medical bills and police reports flow in through multiple channels – email, fax, mail etc. AI techniques like optical character recognition (OCR) and NLP extract relevant data from unstructured documents and feed into claims systems automatically.
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Status Updates: Customers receive regular status notifications on their claim without having to call agents repeatedly. Chatbots can also provide real-time guidance on required documents.
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Fraud Detection: AI pattern recognition identifies claims that could be fraudulent or abusive based on anomalies in data. These can be flagged for further investigation to curb insurance fraud.
According to research from ICA, up to 40% of non-damage motor claims can be fully automated with AI. This results in massive gains in efficiency, accuracy and customer experience.
Automation leads to faster claims processing Image source
Underwriting
Intelligent automation can transform underwriting, which is the process of evaluating risk profiles and pricing policies for applicants:
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Extraction of data from submitted documents via AI techniques like OCR and NLP
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Analysis of data from internal systems, third-party sources and public records to generate risk profiles
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Use of machine learning algorithms trained on underwriting data to accurately price policies based on predicted risk
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Auto-decisioning with pre-defined business rules for simple applications, while routing complex ones to underwriters
According to research from Novarica, 63% of insurer CIOs are planning increased spending on AI-based underwriting and pricing automation.
This enables faster application processing, data-driven and competitive pricing, and highly streamlined operations. McKinsey estimates that intelligent underwriting can improve loss ratios by up to 5 percent and slash policy issuance times by over 80%.
Most insurers plan to increase automation investment Image source
Compliance
Insurance is a highly regulated industry. Intelligent automation assists with:
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Screening customers and third parties against sanctions and PEP lists
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Validating customer data across applications and claims
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Monitoring regulatory announcements to stay updated on new compliance requirements
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Auto-generating compliance reports for regulators
According to leading insurance law firm Clyde & Co, over 90% of insurers plan to invest in regulatory technology including automation to manage growing compliance complexity.
Detailed audit trails from bots along with reduced human errors result in improved compliance and lowered risks. Staff can focus on value-adding oversight rather than routine compliance tasks.
Policy Administration
Policy management encompasses new business, endorsements, renewals, cancellations and more. Automation handles high-volume tasks like:
- Issuing policies post-underwriting
- Updating policies based on customer requests and life events
- Managing cancellations and non-renewals
- Sending renewal notices with adjusted pricing
This creates straight-through processing for policy administration improving efficiency and accuracy.
Customer Service
Chatbots and conversational AI scale customer interactions by providing 24/7 assistance for common inquiries on policies, claims and payments. This improves experience and frees up agents for complex issues requiring human judgment.
According to Juniper, AI chatbots could deliver $14 billion in cost savings by 2023 through automating insurance customer service.
Fraud Detection
AI techniques like predictive modeling and anomaly detection identify suspicious transactions, claims or applications that could signal fraud. Tools can scan historical data to uncover hidden patterns indicative of potential scams.
According to leading insurer Allianz, its AI fraud detection system has enabled over €100 million savings by identifying fraudulent claims more quickly and accurately.
Examples of Insurance Companies Using Intelligent Automation
Now let‘s look at some real-world examples of leading insurers adopting intelligent automation and the results achieved:
Lemonade
Insurtech Lemonade built an end-to-end AI platform called AI Maya spanning underwriting, claims, customer service and more. Key results:
- Reduced underwriting time from weeks to minutes
- Claims approved 3 seconds after all documents are submitted
- AI handles 30% of customer support queries
Lemonade credits its AI focus for its explosive growth – crossing 1 million customers globally within 5 years.
Berkshire Hathaway
The conglomerate‘s workers compensation business leverages machine learning to extract insights from 15 million historical records and billions of data points. This enables:
- 480% faster underwriting
- 50% increase in straight-through processing of policy transactions
- 15-30% greater loss ratio performance
MetLife
Metlife‘s automated underwriting solution uses ML algorithms to provide real-time decisions for half of term life applications in Hong Kong. Benefits include:
- 90% reduction in manual reviews
- 75% lower policy issue timelines
- improved risk selection leading to $10 million savings
Next Insurance
This fast-growing Insurtech built an automated underwriting engine for small business insurance using ML techniques. This delivers:
- Bindable quotes in seconds versus days
- 60% auto-approval rates without human review
- improved loss ratios and right-risk selection
Key Benefits of Intelligent Automation for Insurers
Adoption is surging as insurers realize automation‘s massive potential to drive efficiency, control costs, and enhance experiences. Key benefits include:
Improved Efficiency
- Automating manual processes optimizes operations and saves thousands of human hours.
- Intelligent OCR and NLP extract data from unstructured documents like claims forms and emails that previously required manual entry.
- Straight-through processing minimizes hand-offs and rework across departments.
Cost Reduction
- Automation reduces overheads associated with manual processing like staffing and paper storage costs.
- Bots work 24/7 without human supervision at a fraction of the cost.
- Digitization of records lowers physical storage needs.
Enhanced Customer Experience
- Customers no longer have to wait days or weeks for updates on applications or claims. Real-time processing via bots provides instant answers.
- AI chatbots provide 24/7 assistance to customers for common queries.
- Automated notifications keep customers informed through the process without having to constantly follow-up.
Improved Compliance
- Detailed audit trails account for each automated step, improving compliance reporting.
- Automated screening and monitoring minimizes compliance gaps.
- Bots perform repeated tasks consistently without human error.
Higher Growth
- Competitive pricing and instant policy approvals provide a strategic edge to attract more customers.
- Straight-through digital processing aligns with changing customer expectations.
- Improved loss ratios and risk models minimize unprofitable policies.
According to Accenture, intelligent automation could improve productivity in insurance by 30-40% by 2025. The numbers speak for themselves in terms of unlocking measurable value.
Challenges in Implementing Intelligent Automation
However, insurers need to be aware of certain challenges involved:
- Legacy IT systems will likely require upgrades to support integration with automation tools.
- Heavily regulated processes mandate extensive testing before bots go live to ensure compliance.
- AI models need continuous monitoring and adjustment to maintain accuracy levels.
- Organizational change management is key to secure employee buy-in and upskilling.
- Identifying the right use cases that provide maximum ROI requires detailed process analysis and prioritization.
- Building user trust in bots may require human oversight and explainability of AI logic.
- Partnerships with automation experts can supplement internal capabilities.
"While the opportunities are compelling, insurers need a pragmatic approach to identify quick wins, run controlled pilots, and scale automation judiciously based on outcomes" says Mike Walsh, Partner at McKinsey.
Expert Best Practices for Implementation
To maximize the benefits of intelligent automation, insurers should:
- Start with pain points that friction customers and employees. These provide the best use cases.
- Focus on automating repetitive tasks rather than those requiring human analysis and oversight.
- Take an iterative approach – start with a limited bot deployment and expand based on results.
- Continuously monitor and enhance AI models to maintain accuracy as processes change.
- Implement change management strategies to secure employee support and reskilling.
- Maintain human accountability over bots to ensure customer safety and transparency.
- Develop centers of excellence on automation to build capabilities over time.
"Intelligent automation should be seen as collaborating with employees, not replacing them. The key is enhancing human capacity through bots rather than just reducing headcount. Training staff for more advisory and creative roles is crucial for long-term success," notes Kapil Dhama, Global VP of AI at Liberty Mutual Insurance.
The Road Ahead: Towards End-to-End Automation
Looking ahead, intelligent automation will move the insurance value chain towards seamless, touchless processing in the future. McKinsey predicts that up to 30% of insurance processes could be fully automated using AI by 2030.
Key trends shaping the roadmap include:
- Deeper end-to-end automation across core processes rather than just point solutions
- Growing use of AI techniques like computer vision and NLP for unstructured data-heavy processes
- Shift towards customer self-service model through virtual agents
- Increased integration of emerging tech like Internet of Things (IoT) and blockchain
- Continued improvement infused AI models leading to higher auto-decision rates
- Focus on total workforce optimization – bots and humans collaborating seamlessly
As intelligent automation becomes mainstream, the possibilities are endless for insurers to optimize costs, decision making and experiences. However, transformation needs to be planned gradually based on clear outcomes to create sustainable value.
The Bottom Line
Intelligent automation is primed to be a total game changer for the insurance industry struggling with legacy processes and pain points.
AI-enabled automation can transform core processes end-to-end spanning underwriting, claims, servicing, compliance and more. Insurers are already reporting massive efficiency gains, cost savings, and customer experience improvements.
However, to replicate these outcomes, insurers need an integrated automation strategy based on highest impact use cases. With the right approach, intelligent automation can enable insurers to modernize exponentially and meet the digital expectations of today‘s customers.