Top 3 Ways AI is Transforming Insurance Claims in 2024

Making a claim is a key moment of truth for insurance customers. A smooth, fast claims experience means happy customers who renew policies. On the other hand, delays and friction during claims erode trust quickly.

For insurance companies, claims processing significantly impacts costs and loss ratios. Manual, legacy processes lead to high expenses and inefficiencies. This results in lower margins and competitiveness.

However, AI is enabling insurers to optimize the entire claims lifecycle – from first notice of loss to adjustment and settlement. Advanced technologies like OCR, NLP, computer vision and predictive analytics are bringing speed, accuracy and automation to different parts of claims management.

Let‘s discuss the top 3 applications where AI is improving claims processing:

1. Automated Data Extraction with AI Saves 40% Processing Time

In the past, customers had to fill lengthy paper forms to report claims. Adjusters would then manually type and extract information from these documents. This was slow, expensive and error-prone.

Today, AI-based data extraction tools like optical character recognition (OCR) and natural language processing (NLP) eliminate manual document handling in claims.

OCR software can automatically scan forms and extract relevant text and data. NLP analyzes adjusters‘ notes, emails and call transcripts to pull out key details.

According to Accenture, AI-driven data extraction reduces claims processing time by up to 40% and lowers costs. For a major insurer, OCR helped cut data entry costs by $2 million annually.

NLP also enables 24/7 virtual assistants to handle first notice of loss (FNOL) through conversational interfaces. This provides faster and more convenient claims reporting for customers.

Key Benefits of Automated Data Extraction

MethodBenefits
OCR– Eliminates manual form data entry
– Reduces processing time by 20%
NLP– Automates information extraction
– Provides 24/7 self-service options

2. Computer Vision Cuts Loss Adjustment Costs by 30%

Evaluating damage and settlement amounts is a manual, time-consuming process for claims adjusters typically involving in-person assessments.

With computer vision, insurers can now automate damage recognition from photos and videos submitted through claims. Image recognition and geospatial data enables remote loss assessment and faster decisioning.

For example, an auto insurer used deep learning on photos of damaged vehicles to instantly categorize dents based on severity. This reduced reliance on in-person adjusters by 80% and loss adjustment costs by 30%.

Drone footage gives easy aerial views of property damage like roofing for home insurance claims. Satellite imagery analyzes disaster damage like floods over a large area quickly.

According to Capgemini, computer vision adoption for claims processing will grow 128% by 2022, enabling significant efficiency gains and cost reduction.

Benefits of Computer Vision in Claims

Use CaseEfficiency Gain
Vehicle Damage EvaluationReduce in-person inspections 80%
Property InspectionAssess roof damage from drones
Disaster DamageAnalyze flood damage from aerial data

3. Advanced Analytics Catch Up to 30% More Fraud

Insurance fraud costs the industry over $40 billion every year according to the FBI. Fraud ranges from small exaggerations to elaborate schemes spanning multiple polices/claims.

Previously, insurers used audits and manual reviews to detect fraud. But these methods were slow, expensive and identified only ~10% of fraud cases.

With advanced analytics, insurers now monitor claims patterns to identify suspicious behaviors and anomalies automatically:

  • Unsupervised learning spots new fraud patterns like sudden claim spikes
  • Supervised models flag known tricks like billing for unwarranted car repairs
  • Link analysis uncovers connections between colluding entities

This has enabled insurers to improve fraud detection rates by 30% over manual methods, according to McKinsey. Analytics-driven anti-fraud systems save insurers billions in false claims. This helps keep premiums lower for honest policyholders.

Benefits of Fraud Analytics

MethodFraud Detection Rate
Manual Audits10%
AI-based Analytics30%+

Adopting AI Requires Careful Change Management

While AI unlocks significant potential in claims processing, insurers need strategic implementations focused on value over hype:

  • Legacy systems must integrate properly with AI solutions
  • Poor data quality hampers AI – robust data governance is key
  • Human expertise plays a vital role in training and validating AI systems
  • AI decisions should be transparent and explainable to maintain trust
  • Customer communication and education helps drive AI adoption

The right foundation enables insurers to maximize returns on their AI investments. Claimants also need to see AI as assisting humans, not replacing them.

Looking ahead, real-time loss capture powered by IoT sensors and faster automated adjustments via drones and robots will define the future of AI-enabled claims processing.

The Bottom Line

AI is no longer a nice-to-have but a must-have for insurance companies dealing with rising claims processing costs and customer expectations. Early adopters like Geico and Progressive who have integrated AI across the claims value chain are already reaping benefits.

OCR, computer vision, NLP chatbots and advanced analytics provide actionable technologies to detect, assess and settle claims faster and more accurately. This simultaneously reduces internal costs while also improving customer experience.

However, AI should augment human capability, not replace it. Adjusters empowered by data and technologies will remain at the core of sound claims processing. Overall though, AI-based claims management is rapidly emerging as a key competitive advantage for forward-thinking insurers.

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