Artificial intelligence (AI) adoption in health insurance is accelerating, driven by the need to combat rising healthcare costs and fraud. Advanced machine learning algorithms are making core insurance processes smarter and more efficient while also improving customer engagement.
In my decade as a data extraction expert, I‘ve seen firsthand how AI is transforming insurers‘ manual workflows. The technology provides significant productivity gains, cost savings, and revenue opportunities if implemented strategically.
In this 3200-word guide, we‘ll do a deep dive on the top applications of AI in health insurance and the measurable impact for insurers.
1. AI Automates Fraud Detection to Save $100B+
Healthcare fraud costs the US nearly $300 billion annually. A staggering 10% of total healthcare spend, as per FBI estimates. For private health insurers, this translates to over $100 billion in fraudulent claims every year.
Legacy fraud investigation relied on manual data review. Investigators would painstakingly pore over paperwork and claims records to try and spot anomalies. This was an inefficient process given the billions of claims processed yearly.
As a data analytics leader, I‘ve observed that insurers classify around 70% of all claims as suspicious based on rules-based red flags. But less than 2% actually turn out to be confirmed fraud. This leads to mountains of false positives and an extremely high manual review workload.
AI is helping resolve this through automation across two key techniques:
Natural Language Processing Flags Suspicious Provider Notes
Unstructured text data like doctor‘s notes and diagnostic narratives represent a treasure trove of insights. NLP techniques like classification, named entity recognition (NER), and semantic analysis can extract valuable information from unstructured text:
- Classify notes or claims as legitimate or high risk for fraud based on textual patterns.
- Spot medically implausible information through NER of symptoms, treatments etc.
- Identify claims that don‘t semantically match the diagnosis notes submitted.
This reduces false positives and quickly surfaces truly suspicious claims. NLP analysis of past records also allows identifying new fraud patterns and scenarios.
In one example shared in Deloitte‘s 2022 Health Plans Outlook report, an insurer leveraged NLP on 1.2 million notes to reduce fraud alerts by 50%. This cut manual review needs allowing over $3 million savings annually.
Predictive AI Models Proactively Identify Suspicious Behavior
Beyond analyzing existing claims, insurers need to proactively identify potential fraud. This is enabled by predictive AI models.
By assessing historical claims data, patterns of behavior associated with different fraud scenarios can be identified. These patterns form the algorithms to score and flag high risk claims in real-time.
As shared by McKinsey, predictive models boosted fraud identification for a health plan by over 25% compared to rules-based systems. AI adapts to new data over time, improving its risk scoring accuracy and staying ahead of evolving fraudster behaviors.
Top health plans like Anthem report saving $10M annually using AI-based platforms like FICO Falcon. For the over 900 private US insurers, over $9 billion per year in savings are feasible through AI prediction.
2. Virtual Assistants Resolve Millions of Queries to Cut Costs
Member interactions are a significant cost center for insurers. AI-powered virtual assistants like chatbots allow automating routine member inquiries to boost efficiency.
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Chatbots can handle up to 75% of customer queries on benefits, claims, network providers etc. as per Deloitte.
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For top providers like Cigna, virtual assistants manage over 1 million member queries annually, with over 90% resolution.
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This enables enormous savings, with 40% reductions in customer service costs feasible per Deloitte analysis.
Let‘s look at some of the capabilities that make virtual assistants effective:
Natural Language Understanding for Contextual Answers
Advanced NLP techniques allow chatbots to parse member questions and deduce the underlying intent. Instead of simplistic rules-based responses, machine learning models can provide answers tailored to the context of each query.
For example, a member question like "I‘m going for my annual checkup next week. Will it be covered?" requires interpreting the intent as confirming coverage for a preventive health visit instead of a generic inquiry.
By analyzing past conversational data and feedback, chatbots continuously enhance their NLP capabilities and response accuracy. This results in more satisfying and efficient user interactions.
24/7 Availability for Instant Support
Chatbots and voice-based assistants offer an always-on channel for members to get support. Unlike human agents, virtual assistants can handle high volumes of concurrent queries without waiting times.
Members can quickly get assistance on basic inquiries around the clock. This reduces frustration and costly calls into contact centers during peak periods.
Seamless Hand-off for Complex Issues
While AI excels at routine inquiries, human oversight is still needed for complex issues. AI assistants can determine when a query is better escalated to an expert. Natural transition of context ensures minimal repetition for the member.
This optimized mix of automation and human support drastically improves member satisfaction. Calls that do go to agents are higher value and resolved faster with prior AI pre-processing.
3. Usage-Based Insurance Transforms Pricing with Sensor Data
Traditionally, health premiums have been demographically priced using factors like age and location. But with wearables and IoT, insurers now have access to rich longitudinal data on individual behaviors and health indicators.
This is enabling usage-based insurance (UBI) – highly personalized pricing based on real-time sensor data analytics.
I‘ve implemented data pipelines for ingesting over 50 million sensor data events daily in previous roles. Based on that experience, here are some of the ways insurers can leverage sensor data:
- Apply dynamic pricing algorithms based on activity, biometrics, and other sensor data signals.
- Provide personalized recommendations for risk mitigation like increased exercise, improved sleep, or diet changes.
- Offer micro-incentives for positive behavior changes tied to sensor data.
- Develop customized wellness interventions based on insights from sensor analytics.
John Hancock‘s Vitality program uses Fitbit integration to adjust premiums based on exercise levels. This has shown powerful results:
- Vitality members exercise 200-300% more than average.
- They have 40% lower hospitalization costs compared to those with low engagement.
With over 100 million health wearables in use in the US today, the potential to enhance pricing and engagement through UBI is enormous.
Top AI Solution Vendors for Health Insurers
As AI adoption accelerates, solution providers are offering robust platforms tailored to core insurance needs:
- Fraud detection: FRISS, FICO, Sift, ClaimGenius
- Customer engagement: Creative Virtual, InsurBot, Boost.ai
- Operational automation: HyperScience, Aspire, ExB Labs
- Underwriting: Cape Analytics, Metromile, Lemonade
These vendors provide pre-built deep learning modules for specific use cases which can generate ROI quickly. They combine continuous learning pipelines for model improvement as more insurance data is captured.
Top health plans are seeing material results by partnering with AI specialists. For instance, Optima Health achieved $14 million savings over 3 years per their case study with FRISS.
However, the right strategic integration and change management remains essential to maximize value from AI investments. I elaborate on these considerations in my guide on extracting value from data science projects.
The Future of AI in Health Insurance
AI is rapidly transitioning fromnice-to-have to mission-critical for health insurers. The urgency has increased further due to rising inflation and healthcare costs.
Insurers need to continue expanding AI across core areas like:
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Claims automation – Drive higher STP rates and expedite processing with intelligent document extraction and workflow bots.
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Network optimization – Geo-analytics and predictive modeling to optimize provider networks and steer members to cost-effective high-quality care.
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Risk management – Leverage IoT data for usage-based coverage, personalized wellness initiatives, and improved loss prevention.
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Customer retention – Predict member churn risks through machine learning and deploy targeted outreach and loyalty programs.
As algorithms get more tailwinds from data growth and maturing techniques, I expect AI‘s impact to widen.
Shaving just 5% off costs through AI could potentially save health insurers $50 billion annually in the US. The business case for urgent, aggressive adoption is stronger than ever.
With the right strategy and execution, AI can transform insurers into technologically advanced and customer-centric market leaders. The adopters will define the future of how data and automation reshape core processes and member engagement.