The claims process is the moment of truth for insurance companies. How efficiently and accurately insurers can settle claims directly impacts customer satisfaction, retention rates, loss ratios, and bottom lines. However, traditional claims workflows are tedious, opaque, inconsistent, and vulnerable to fraud, costing insurers and policyholders billions.
Fortunately, AI is revolutionizing claims automation, adjustment, and fraud detection with technologies like NLP, computer vision, predictive analytics, and RPA. This comprehensive guide explores the existential cost and experience challenges across the claims process and demonstrates how leading insurers leverage AI at each stage to delight customers and deliver breakthrough financial performance.
The High Costs and Low Satisfaction of Traditional Claims
Let’s examine some sobering statistics around existing claims operations:
- Up to 30 days average claims cycle time [1]
- More than $500 billion annual insured losses worldwide from manmade and natural catastrophes [2]
- Over $80 billion lost to fraudulent property & casualty claims each year [3]
- Almost 50% of customers frustrated by inconsistent, delayed or denied claims [4]
- Up to 25% of premiums spent on loss adjustment and claims processing [5]
These figures highlight immense inefficiencies within traditional claims approaches centered around manual paperwork processing, field investigations and isolated systems. The resulting delays frustrate customers, while overpayment and fraud leakage bleed insurer bottom lines.
AI is creating a watershed moment for claims via:
- 90%+ STP for routine claims through automation
- 99% accuracy in damage assessments and estimates from computer vision and predictive analytics
- 80%+ fraud prediction with data enrichment and machine learning
- 98% customer satisfaction through real-time digital interactions
The technology now exists to completely reinvent both experiences and economics of the claims process. Laggard insurers risk falling further behind innovators seizing this opportunity.
Critical Capabilities for AI-Enabled Claims
Here we explore five pivotal technologies set to disrupt claims outcomes:
1. Natural Language Processing
NLP software comprehends human language for applications like:
- Chatbots guiding policyholders through notices of loss
- Virtual Assistants managing routine inquiries
- Document Understanding extracting unstructured data from photos, emails etc
This reduces reliance on manual efforts while improving access and transparency for customers.
*NLP structurers testimony and documents for downstream analytics. Source: Accenture_
2. Computer Vision
Computer vision analyzes visual data for superior accuracy in:
- Damage Assessment: Identify repair needs from claimant photos
- Estimation Services: Match damages with parts catalogs and labor estimates
- Claims Validation: Confirm repair quality through photo audits
- Fraud Analytics: Detect duplicate claims for single accidents
Instead of manual inspections, computer vision extracts actionable insights from images in seconds while uncovering non-obvious fraud patterns.
Computer vision accurately identifies damaged parts from photos. Source: Guidewire
3. Predictive Analytics
Predictive analytics applies ML to predict outcomes like:
- Catastrophe Response: Forecast extreme weather events for proactive positioning
- Reserving Models: Project number and cost of future claims
- Subrogation: Identify liable third parties for recovery
- Claims Leakage: Detect potentially fraudulent or unnecessary payments
Predictive analytics transforms reactive claims operations into data-driven strategic management.
Insurers model the likelihood of claims outcomes with predictive analytics. Source: LexisNexis
4. Robotic Process Automation
RPA uses software “bots” to emulate users for repetitive tasks like:
- Validations: Auto-confirm coverage, banking details
- Data Extraction: Transfer info from documents into systems
- Calculations: Determine reimbursements, depreciation
- Reporting: Automated loss run creation
For high volume, rules-based claims activities, RPA reduces cycle times and human resource overheads.
RPA bots automate repetitive tasks like data entry and calculations. Source: AnotherMonday
5. AI Infrastructure
To unlock enterprise automation, insurers also need robust:
- Cloud Platforms: Scalable computing for AI workloads
- Data Foundations: Ingestion, warehousing and governance
- MLOps: Model development, monitoring and management flows
With reliable infrastructure, claims innovation is no longer constrained by technology limitations.
Insurers require data, platforms and MLOps to enable organization-wide AI adoption
These five symbiotic capabilities make straight-through, optimized claims processing a reality.
Introducing AI Across the Claims Lifecycle
We’ve surveyed the pivotal technologies enabling AI claims outcomes. Here we demonstrate specific deployments improving each phase:
Intelligent FNOL
The first notice of loss kicks off the claims process but has relied on manual approaches. AI modernizes intake:
- Self-Service Portals: Guided web questionnaires with NLP chatbots for instant loss details
- Media Capture: Photo, video and audio submissions provide visual damage confirmation
- RPA Handling: Bots validate submissions, trigger payments, assignments and investigations
Complete digital FNOL provides superior customer experience while feeding automated workflows.
AI enables real-time, self-service digital FNOL for faster cycle times
Accelerated Estimation & Adjustment
Investigation and quoting traditionally requires days of field inspections. AI improves in-house desk adjusting:
- Rapid Quoting: Computer vision matches damages to parts catalogs and labor time standards
- Predictive Adjusting: Models forecast optimal repair tactics and expected costs given case attributes
- High Volume STP: Bots handle data entry, coverage checks and calculations for simple claims
With AI, most claims resolve digitally through straight-through processing while complex cases route seamlessly to specialized resources.
AI radically accelerates key components of investigation and adjustment
Fraud: From Detection to Prevention
Insurers lose over $80 billion annually to false claims raising prices industry-wide. AI transforms oversight:
- Data Fusion: Combine internal data, public records, social media for complete context
- Risk Scoring: Identify claims with high fraud potential based on analytics
- Text Mining: Analyze claimant testimony to detect deception signals
- Network Analysis: Uncover collusion between parties across seemingly unrelated claims
While AI won’t eliminate all fraud, optimized scoring better focuses Special Investigation Units on material risks.
AI enables proactive identification of suspicious claims for Superior fraud prevention
Delivering Breakthrough Claims Results with AI
While incremental automation seems minor, AI collectively enables unprecedented claims achievements:
- 90%+ STP enabling real-time resolution
- 30-50% Cycle Time Reductions improving satisfaction
- 35-45% Loss Adjustment Expense Savings boosting margins
- 20-40% Increased Retention Rates spurring growth
- 75-85% Fraud Identification preventing leakage
Compound efficiencies also directly enhance profitability:
- 5-15% Higher Profit Margins from cost controls and fraud prevention
- 2-4% Increased Top Line Revenue through improved retention
- 20-40% Loss Ratio Improvements via accuracy gains
AI is no longer an option but an imperative for competitiveness. Laggards risk falling further behind.
Aggregated AI deployments transform claims economics and experiences
Critical Considerations for Implementation
Like any enterprise technology transformation, claims AI success depends on execution across people, processes and systems:
Winning Internal Buy-In
Despite hard ROI cases, many insurers struggle getting initiatives beyond pilots. Best practices to drive adoption include:
- Identify Executive Sponsors: Secure C-Suite champions to align stakeholders
- Showcase Quick Wins: Pilot validations build confidence for scale
- Communicate Customer Benefits: Align to corporate values beyond efficiency
Structuring Delivery
Technical integration and oversight processes also enable scale. Recommendations:
- Architect for Change: Flexible microservices prevent legacy blockers
- Democratize Skills: Upskill claims teams on identifying automation opportunities
- Implement MLOps: Rigorously monitor model performance, drift and governance
Preparing Culture
Perhaps most critical is cultural readiness as AI transforms individual roles. Approaches include:
- Embrace Transparency: Address perceptions by showcasing how AI improves work
- Welcome Hybrid Teams: Seth expectations around human + machine collaboration
- Nurture Growth Mindsets: Encourage capability building as AI evolves processes
While realizing AI‘s full potential requires evolution, the organizational upside justifies investment.
Becoming an AI-Led Insurer
Claims processing is only the starting point on the AI journey. Soon AI will transform underwriting, customer experiences, product development and more. P&C and Life insurers that postpone adoption risk disruption by born-digital entrants without legacy constraints.
However, incumbent insurers also possess unrivaled industry expertise and policyholder trust – advantages over InsurTech newcomers. Enterprises that strategically scale AI to amplify specialty strengths while optimizing operations will lead their fields. The winning formula combines business vision, technical talent and reliable technology partners specializing in insurance implementations.
To discuss your organization‘s AI-led growth opportunities, please contact our team. 2022 is the year to solidify the next era of insurance leadership.