Top 10 AI Use Cases & Applications Insurers Must Know in 2024

Over my decade as a data expert and analyst, I‘ve seen artificial intelligence transform nearly every industry. Insurance is no exception – AI is revolutionizing core processes from underwriting to claims to customer interactions. Based on my experience, I wanted to provide an in-depth look at the key AI applications insurers should prioritize today.

The Promise and Growth of AI in Insurance

AI adoption in insurance is exploding. According to a recent Novarica survey of 147 insurer CIOs and technology executives:

• 63% are currently implementing AI in some capacity
• An additional 23% are running AI pilots or planning implementations

This momentum is being driven by AI‘s immense potential to automate manual processes, combat fraud, strengthen risk analysis, and enhance customer experiences.

McKinsey estimates that AI could generate over $1.1 trillion in value for the insurance industry through capabilities like:

With so much value at stake, insurers are ramping up investments in AI talent, data, and infrastructure. Let‘s examine the top 10 AI use cases insurers are prioritizing right now.

1. Automating Underwriting

Underwriting is the crucial process of evaluating risk profiles for insurance applicants to determine policy terms, rates, and eligibility. It has traditionally depended on manual data gathering and human reviews.

AI is transforming underwriting in insurance by:

• Extracting information from documents and data using optical character recognition (OCR), natural language processing (NLP), and other techniques. This structured data powers downstream automation.
• Automating risk analysis using machine learning algorithms applied to thousands of variables from documents, third-party data, IoT devices, and more.
• Accelerating decision-making with AI systems that can approve, quote, or decline policies once trained on past underwriting decisions.

According to Celent, ~78% of insurers are using AI to automate parts of underwriting. McKinsey estimates this can reduce loss ratios by up to 20% through improved risk selection and pricing.

Leading solutions include:

• Planck Underwriting – Uses ML and thousands of data variables to instantly underwrite commercial insurance policies. Reduces reliance on manual underwriter reviews.
• Cape Analytics – Leverages computer vision AI to evaluate properties for home insurance underwriting using aerial imagery. Enables real-time risk assessment.
• Lemonade – AI chatbots and automated systems underwrite renters/home policies in as little as 90 seconds with minimal human oversight.

The benefits of AI underwriting automation include faster customer onboarding, reduced costs, and the ability to assess risk with greater accuracy across more parameters.

2. Accelerating Claims Processing

Insurers process over a billion claims each year. AI is proving instrumental in streamlining these high-volume, complex workflows. It‘s doing so by:

• Reading claims documents – OCR and NLP extract relevant data from forms, medical reports, damage assessments, police reports, and more.
• Validating claims – AI analyzes claims data, imagery, and documentation to identify suspicious or fraudulent claims for further review.
• Estimating costs – Computer vision AI assesses vehicle/property damage from images to estimate repair costs.
• Settling claims – Bots and automated systems use rules engines to validate legitimate claims and accelerate settlements.

According to Accenture, AI claims processing can reduce costs by 30% while also improving customer satisfaction through faster resolutions.

Innovators in this space include:

• Tractable – Its AI reviews auto damage photos, estimating repair costs in minutes to help expedite settlements.
• Shift Technology – Detects claims fraud using AI trained on billions of historical transactions and cases.
• ACOPIA – Chatbots and RPA bots automate first notice of loss (FNOL) and simple claim transactions end-to-end.

As AI evolves, expect more aspects of claims management – from document intake to investigation to payouts – to become automated using intelligent algorithms.

3. AI Chatbots for Customer Engagement

Another vital application of AI in insurance is intelligent chatbots for customer service and engagement. These bots can provide 24/7 assistance with:

• Answering policy questions
• Supporting online quotes
• Managing account changes
• Initiating claims
• And more, delivering consistent and compliant responses.

The benefits for insurers include:

• Faster Resolution – Chatbots resolve common inquiries instantly without wait times. This increases customer satisfaction.
• Lower Call Volumes – Chatbots reduce calls to human agents by providing convenient self-service options.
• Upsell Opportunities – Interactions can educate customers on additional coverage needs.
• Improved Data Collection – Bots can capture customer data through conversational interfaces.

Leading insurance chatbots:

Chatbot	Insurer	Capabilities
Flo	Progressive	Quotes, policy changes, payments, etc.
ABIE	Anthropic	Life insurance Q&A and application assistance
Clair	Zurich Insurance	Customer support and claims interactions

As conversational AI matures, expect insurance chatbots to become ubiquitous across customer touchpoints, integrating seamlessly with backend and call center systems.

4. Hyper-Personalization with Big Data Analytics

Insurers have troves of data on customers and their behaviors, risks, claims, sentiments, and more. Sophisticated analytics can uncover highly valuable insights from this data to hyper-personalize services. Use cases include:

• Predictive Models – Identify likely customer needs and optimal cross-sell/upsell products for each individual.
• Propensity Scoring – Calculate propensities for renewing policies, submitting claims, lapsing coverage, and more. Allows proactive retention initiatives.
• Customer Lifetime Value Estimates – Project future profitability of customers to optimize engagement, pricing, and targeting.
• Micro-Segmentation – Divide customers into micro-segments to customize interactions based on highly specific traits and behaviors.
• Next Best Action Models – Determine the ideal message, offer, interaction channel, and timing for each customer to boost engagement.

These techniques lead to services like:

• Personalized premiums and deductibles based on actual risk profiles
• Tailored rewards programs and discounts
• Relevant recommendations for additional coverage
• Proactive retention for high-value customers

According to McKinsey, personalization driven by AI analytics can boost profits from pricing and sales by 3-10% across the customer lifecycle.

5. Optimizing Call Centers with AI

Insurers field millions of calls annually through their contact centers. AI can optimize these operations:

• IVR Optimization – Smart IVRs use natural language understanding to route calls more accurately.
• Smart Call Routing – Analyze caller voice/data to connect to best-fit agents faster.
• chatbots as Virtual Agents – Automate routine inquiries to reduce call volume.
• Agent Coaching – Provide real-time feedback to agents based on live call analysis.
• Fraud Detection – Monitor calls for verbal cues that suggest fraudulent claims.

According to Salesforce, integrating AI in call centers reduces costs by 30% on average while improving CSAT by over 20%. Vendors like Mattersight and Observe.AI offer AI software tailored for insurance call centers.

As speech analytics, sentiment detection, and conversational AI advance, insurers can transform call center experiences on both ends of the line.

6. AI-Driven Customer Acquisition

Insurers face growing competition for new customers. AI and big data analytics are invaluable for smarter acquisition strategies:

• Micro-targeted ads – Create personalized ads optimized for each website visitor based on their data profile.
• Predictive lead scoring – Score and rank leads based on propensities to buy different policy types. Allows prioritizing of sales outreach.
• Hyper-personalized campaigns – Craft customized multi-touch nurturing campaigns for each lead tailored to their interests and goals.
• AI recommendation engines – Suggest relevant products, coverage limits, add-ons, etc. personalized for each potential customer.
• Instant quoting – Provide real-time quotes on websites/apps powered by automated underwriting systems.

These techniques can significantly improve conversions and reduce acquisition costs. Lemonade shared that their AI-driven lead scoring system captures 3X more customers than rules-based approaches.

Vendors like AppOrchid and EIS Group offer AI marketing platforms tailored for insurers to accelerate conversions.

7. AI-Powered Document Processing

Processing high volumes of documents with accuracy and speed is critical across insurance functions like underwriting, claims, and more. AI automation techniques can transform these workflows:

• Data extraction – Digitally extract information from forms, medical records, bank documents, and other files using OCR and NLP.
• Classification – Identify document types like claims forms programmatically to route them correctly.
• Verification – Cross-check extracted data against external sources to confirm identity, employment status, licenses, etc.
• Workflow automation – Use RPA bots to automatically trigger downstream processes based on documents.

According to IBM, AI document processing can reduce data entry costs by 80% while boosting data quality. It also accelerates cycle times. Top solutions include HyperScience, Hyperscience, and Rossum.

As AI capabilities grow, insurers can automate even unstructured documentation processes end-to-end with minimal human involvement.

8. AI-Driven Risk Modeling and Pricing

Insurers rely heavily on risk models and actuarial analytics to price policies competitively based on predicted claim costs and losses. New techniques are supercharging modeling:

• IoT and real-time data – Incorporate streams of live data from vehicles, properties, wearables, environs, and more to dynamically estimate risks.
• Machine learning – ML algorithms can model thousands of variables and detect complex correlations unattainable with traditional modeling.
• Granular geospatial analytics – Blend rich data like weather, traffic patterns, demographics, past claims, etc. to map risk at hyper-local levels.
• Automated simulation modeling – Run millions of "what if" scenarios through agent-based and other simulation models to stress-test pricing strategies.

Per PwC, AI can improve loss ratio predictions by 30% while cutting model development timelines in half. Vendors like Cape Analytics and Tamr offer AI modeling solutions tailored for insurers.

As computing power grows exponentially, insurers can continuously refine risk models and pricing using massive datasets.

9. Fraud Prevention and Detection

Fraud costs the insurance industry over $80 billion annually. AI and advanced analytics are mission-critical technologies for fraud mitigation:

• Pattern recognition – Detect organized fraud rings through network analysis of entities, anomalies, suspicious behaviors, etc.
• Multi-factor fraud models – Assess risks associated with claims, consumers, providers, etc. based on hundreds of variables.
• Link analysis – Uncover connections between suspicious entities that aren‘t visible through rules-based audits.
• Text mining – Analyze claim submissions, injury descriptions, notes by adjusters, etc. to identify probable fraud.
• Behavioral biometrics – Monitor mouse movements, swipe behaviors, etc. of claimants submitting online/mobile forms to detect fraud.

Per SAS, AI techniques like neural networks improve fraud detection accuracy by over 25% compared to conventional rules-based systems. Top vendors offering AI fraud solutions for insurers include FRISS, TransUnion, and Sift Science.

10. Intelligent Process Automation

Many critical insurance operations like underwriting, policy administration, and claims processing involve high volumes of repetitive tasks. AI-driven automation can streamline these workflows. Technologies like RPA, IDP, NLP, and machine learning can automate:

• Data validation, transfer, and entry
• Payment processing
• Report generation
• Claims adjudication
• Policy generation and issuance
• Communication workflows
• Supply chain processes

McKinsey estimates that intelligent process automation can reduce insurance operating costs by 30-80%. It also boosts speed, accuracy, and compliance. Leading solutions include Automation Anywhere, UiPath, HyperScience, and Blue Prism.

As AI evolves, processors can implement "touchless processing" with near-zero human intervention across core insurance workflows.

The applications above highlight just some of the transformative impacts AI is already having across insurance. As computing power grows exponentially, the possibilities are endless.

AI will penetrate deeper across every function:

• Hyper-personalized underwriting using biometric IoT data
• Conversational bots delivering truly human-like experiences
• End-to-end automated claims settlement within minutes
• On-demand insurance for micro-moments and nanoslices of coverage
• Immersive virtual/augmented experiences for sales and services

Leading insurers of the next decade will be defined by their mastery of customer-centric AI techniques. Those slow to adopt will face significant competitive threats.

However, to unlock AI‘s full potential, insurers must continue building robust data pipelines, hiring talent, and investing boldly in emerging technologies. Ultimately, the winners will be those insurers who harness AI to create seamless, personalized, digitally-enabled experiences across the customer lifecycle. The opportunities for value creation are truly boundless.