The insurance claims process is rapidly evolving, driven by changes in customer expectations and new technological capabilities. Insurers that want to remain competitive in 2024 need an integrated claims transformation strategy centered around data and analytics.
The Changing Claims Landscape
There are two major shifts occurring in claims:
1. Customer expectations are changing
While many customers still prefer telephone and human interaction for submitting claims, there is also growing demand for digital and self-service options. Research shows:
- 22% of business insurance customers now prioritize fully digital claims processing when choosing a provider.
- Millennials and younger generations tend to prefer digital services whenever possible. According to a 2021 survey by JD Power, nearly 70% of millennials and Gen Z customers said they would likely use an electronic self-service platform for common service tasks.
However, for complex, high severity claims, most customers still desire personal communication with experts. A 2022 customer research report by McKinsey found that 76% of customers wanted phone contact for submitting claims, indicating the continued need for human interaction.
Key Insight: Insurers need to balance automated, digital options with human interaction to satisfy diverse customers.
2. New technologies are emerging
Artificial intelligence, IoT sensors, blockchain and other innovations now allow insurers to:
- Automate routine claims tasks like information collection and document processing
- Detect potential fraud earlier with predictive analytics
- Accurately predict claims outcomes/costs with advanced machine learning algorithms
- Resolve claims up to 70% faster for better customer satisfaction
For example, leading insurance AI software provider Shift Technology found that its algorithms could detect up to 70% of fraudulent claims that originally were undetected. Aviva Canada decreased its customer contact rate by over 60% after implementing AI-enabled self-service options.
Key Insight: Insurers can leverage technology to optimize efficiency and accuracy while meeting rising customer expectations.
These two shifts create opportunities as well as complex challenges for insurers. The solution lies in taking a data-driven, customer-centric approach to claims transformation.
A Data & Analytics Framework for Claims
By integrating data and analytics into every step of the claims process, insurers can make precise decisions tailored to each customer:
Claim Intake & Submission
- Use predictive analytics to determine eligibility and likely validity of claim before submission
- Provide customized submission portals based on customer tech preferences
For example, by combining structured claims data with satellite imagery and smart home IoT data, an insurer could pre-validate a home damage claim and prepare necessary documentation ahead of customer submission.
Information Collection & Validation
- Employ NLP algorithms to extract data from documents
- Detect likelihood of fraud using anomaly detection algorithms
An NLP parser can extract salient information from a claims adjuster‘s notes or police report and input it directly into relevant case management fields, saving countless hours of manual work.
Estimation & Predictive Modeling
- Leverage computer vision AI to estimate damage costs from photos
- Improve predictive models of claims outcomes drawing on deep historical datasets
By feeding images of damaged properties into convolutional neural networks, insurers can accurately estimate repair costs within 5-10% of actuals, allowing much faster claims fulfilment.
Prioritization & Processing
- Set processing priority based on predicted settlement times and customer sentiment analysis
- Identify bottlenecks in existing processes using process mining algorithms
Analyzing text-based customer interactions with sentiment analysis algorithms can identify cases at risk of abandonment due to frustration. These cases can be expedited to improve retention.
Settlement & Closure
- Offer dynamic settlement options adapted to customer risk scoring
- Model optimal settlement amounts to reduce litigation risk
Combining the right mix of digital self-service and human expertise allows insurers to operate with new efficiency while offering differentiated service – the foundation of the claims process transformation needed today.
Key Insight: An integrated analytics framework provides greater efficiency, accuracy and personalization across claims processes.
Emerging Data Sources for Enhanced Claims Insights
In addition to traditional claims data, insurers are increasingly tapping real-time data sources to improve decision-making across the claims process:
Smart Home IoT data – With customer permission, IoT sensor data from smart home devices can validate or refute claims. For example, data from water leak detectors could verify the cause of dampness claimed in a property damage filing.
Aerial imagery – High resolution aerial images enable remote visual damage assessment for quicker estimation and processing for property claims.
Social media – Insights extracted from location-tagged social media posts using geotagging and computer vision algorithms can provide additional validation for certain claims.
Financial transaction data – Fraud patterns can sometimes be identified by analyzing patterns and anomalies in recent bank/credit card transactions provided by a claimant.
Key Insight: Emerging data sources allow insurers to operate with greater accuracy and efficiency throughout the claims process.
Cutting Edge Techniques for Battling Fraud
Insurance fraud causes $40 billion in economic losses each year. Here are some advanced techniques enabled by AI and analytics for combating different forms of fraud:
Graph analytics – Mapping connections between claims adjusters, autobody shops, medical providers and claimants can uncover clusters indicative of collusion. Link analysis algorithms can highlight risky relationships in these networks.
Outlier detection – Supervised models can detect anomalies in features like average payouts, length of hospital stays, treatment costs, etc. to flag potentially excessive or unnecessary billing.
Text mining – Analyzing the text of adjusters notes, police reports and medical diagnoses can trace inconsistencies that expose potential fraud.
Social network analysis – Connecting claimants across multiple social sites like Facebook and LinkedIn can uncover fake personas used to file fraudulent claims.
Key Insight: Advanced analytical techniques enable the early detection of fraudulent signals that can accelerate investigations.
While technology creates new opportunities, the human factor remains central to successful claims processing. Let‘s look at two case studies of analytics-powered transformation.
Case Study 1: Acme Insurance Claims Process Automation
Acme Insurance sought to automate repetitive manual claims tasks to improve efficiency and policyholder satisfaction. By implementing machine learning-based document processing and chatbots, they achieved:
– 75% reduction in claims information entry through automated form and document data extraction
– 60% resolution rate for policy questions via conversational chatbots with NLP
– 43% faster claims fulfilment through predictive analytics-based triaging and prioritization
Critical success factors included cross-functional collaboration, extensive agent training on new tools, and maintaining human oversight of all automated decisions.
Case Study 2: Allstate Claims 360 Customer Intelligence
By integrating external data like credit scores and IoT smart home data with internal structured and unstructured claims data, Allstate gained a 360-degree customer view that improved loss predictions by 30%. Improved pricing segmentation also resulted, with 10-15% reduction in average claims for focused customer groups after experience-based segmentation.
This transformation delivered $3 billion in cumulative operating profit over 5 years – showcasing how an analytics and data infrastructure overhaul can yield bottom line returns while benefiting customers.
4 Strategic Recommendations
To transform claims successfully, insurers need strategies tailored to their customer base. Here are top recommendations:
1. Assess your customers’ digital preferences
Analytics tools can classify customers based on digital literacy and channel preferences. Use these insights to determine optimal channels for different claims.
2. Take an iterative, test-and-learn approach
Continually test changes at small scale before broader rollout. Rapid experiments allow flexibility amid shifting landscapes.
3. Invest in integrated data infrastructure
Modernize systems to capture, integrate and analyze data from internal, external and emerging sources under a unified platform.
4. Enable faster decisions with analytics
Reduce reliance on intuitive human decisions in favor of analytically-guided choices for superior accuracy and consistency.
5. Ensure AI transparency and ethics
Explain AI outcomes to build trust. Continually monitor for biases to ensure fair treatment of all customer groups.
Powered by analytics and technology while centered on customer needs, insurers can transform the heart of their business – claims processing – to gain a vital competitive edge in an increasingly complex market. The future remains uncertain – but an integrated data strategy offers the most resilient path forward.