Tag Archives: computer vision

Property Claims: It’s Time for Innovation

The personal and commercial property claims process has traditionally lagged well  behind other segments of P&C insurance in the adoption of technology and innovation. That officially ended in 2020, aided by a global pandemic that changed virtually everything about life and business as we knew it. Understanding the factors behind the historical lack of innovation in property claims provides insights into why and how this segment is suddenly undergoing such rapid transformation.

Auto vs. Property Claims Process Transformation

When compared with the recent impressive rate of change in auto claims, property claims appeared to be a more of a laggard than it really was – but a laggard nonetheless. To put this in perspective, U.S. auto insurance policies, premiums and claims in 2019 were approximately four times larger than property. Further, auto claims are generally more visible and more consequential to the public than property claims. And the auto claims process was broken until about 1990, with the emergence of direct repair programs enabled by internet and database technologies, so the transformation has been that much more obvious and impressive.

Industry Fragmentation

The property claims repair market is characterized by extreme fragmentation, which exceeds that in the auto insurance claims industry. This is due to several factors: 

  • the relatively large number of service providers specializing in distinctly different major damage types, especially managed repair networks, as well as independent contractors, in general
  • the complexity of property claims themselves, which involve the coordination of numerous general and specialty provider types for a given claim 
  • the proliferation of task-specific software solutions, which are generally not integrated with one another
  • the smaller influence of property insurers on the repair process as compared with the influence that auto insurers have (because of less consolidation of property insurers and because they collectively represent only about 33% of repair industry revenue while auto insurers represent almost 90% of collision repair revenue)

A high-level comparison of market fragmentation of third-party auto and property claims repair provider markets provides another important explanation of the emerging transformation in property claims. The collision repair industry has undergone significant consolidation both in terms of the numbers of repair shops and shop ownership – and consolidation continues. Since 1990, the number of U.S. repair locations has fallen roughly 50% to approximately 32,000. Moreover, consolidators have created large multi-location, multi-regional and national MSOs (multi-shop operators) and now control almost 30% of the repair industry revenue. Private equity investments and relatively inexpensive debt have provided the enormous pools of capital required to enable this consolidation.   

See also: Key Advantage in Property Underwriting

Property Claims Ecosystem

In studying the property claims, mitigation and restoration ecosystem, we identified 110 companies with material market share, which we grouped within nine distinct categories:

  • Software applications for:
    • Property estimating
    • Restoration management
    • Claim management platforms
    • Accounting/financial, measurement, documentation, communication and productivity
    • Payment solutions
    • Imaging/aerial inspection
  • Services:
    • Third-party administrators (TPAs)
    • Property claims adjusting and estimating
    • Managed property repair networks

Industry Consolidation

When we researched corporate ownership profiles for these 110 firms, we discovered that 45 – or 39% of them – are funded or controlled by private equity, venture capital or a few strategic investors. While there is some such investor activity in every one of the nine segments, it is most pronounced in managed property repair networks, claims management platforms and imaging/aerial inspection verticals.

These investors are fueling consolidation in these segments in much the same way as they are in the auto claims ecosystem, and will spur greater adoption of cost-effective and process innovation technologies. This is already evidenced by the emergence and adoption of artificial intelligence, computer vision, augmented, virtual and extended reality, machine learning and natural language processing across property claims.

Opportunities

Emerging Property Repair Market Opportunity

The property repair industry is 40% to 50% mature, while we estimate the auto claims industry is approximately 80% mature. This is partially illustrated by direct repair claims penetration of the collision repair industry, which is at or over 50% for carriers with higher market share (and more for some auto carriers) versus less than 10% on average for property repair.

Homeowners property insurance claims and ecosystem software and technologies market, viewed holistically, represent a significant and mostly unaddressed market opportunity. The situation closely parallels the auto insurance claims process and collision repair markets of 1990, which saw technology and economics drive vendor consolidation and carrier adoption of managed national repair programs, which were enabled by automated estimating software development, digital communications, imaging and end-to-end claims workflow tools.

Property Claims Solution Platforms

Property insurance carriers increasingly will be seeking technology-driven end-to-end property claims management solutions featuring;

  • connectivity between all parties from report of loss to remediation to payment and closure
  • hybrid insourced/outsourced carrier claims and repair network management capabilities, including  universal, standardized contractor onboarding, performance metrics, automated skills/needs matching, user reviews and vendor rankings.
  • integration with Guidewire’s claims platform or similar partner ecosystems

Property Claims Technologies

Artificial intelligence (AI), machine learning (ML), robotic process automation (RPA),computer vision (CV), natural language processing (NLP), aerial imagery including drones and digital payments are being aggressively adopted across the P&C insurance claims process, and specifically property.

  • Smart home technology adoption will mitigate and in some cases eliminate claims and losses; Bain Capital predicts that in just five years there will be 50 billion connected devices and a trillion by 2030. According to Statista Market Forecast, the global smart home market was valued at $55.65 billion in 2016 and is projected to reach $174.24 billion by 2025, growing at an annual rate of nearly 14%. While 32% of homes currently have a smart device, that number is expected to reach 52% by 2025.
  • The impact of these technologies to the property claims and restoration industries is already — and will become even more — significant
  • As residential policyholders become more comfortable with self-administered smartphone photo and video inspections of property damage reported directly, insurers will gain more control over the restoration assignment process, which will promote the use of national repair networks (and the claims management software that can manage the end-to-end process)
    • It is estimated that the use of photo inspection services can reduce field claims cost from an average $550 down to between $60 and $90 and the cost of technical inspections from $550 to $300
    • Technical inspections or VAIP (virtual adjusting and inspection programs) will fuse services, including the use of a licensed adjuster. Claims will offer faster cycle times and savings of 35%.
    • Providers of satellite and aerial images, including drones, are gaining in importance in the residential property damage identification, validation, damage assessment and repair estimation process.
    • Satellite and aerial imagery are increasingly being used by the property insurance industry for catastrophe planning and response, including damage evaluation and estimation.

Property insurance carriers now seek to avoid the effort and responsibility of managing restoration contractor selection or oversight but require a complete end-to-end workflow management platform to achieve their goal.

See also: How to Pursue Innovation in a Crisis

The property insurance claims and repair industries continue to move through a multi-segment structural transformation caused by prevailing market conditions, including industry fragmentation, consolidation, investments, revenue and geographic scale, end-to-end technology and software integration, emerging technology adoption and claims process improvement. Companies and investors that recognize the numerous opportunities presented by this transformation and solve for these dynamics are likely to be the future industry leaders.

How to Use AI in Customer Service

How to manage the increase in incoming unstructured information is a key challenge in the insurance industry—we explore how Accenture’s Machine Learning Text Analyzer can achieve this using historical data.

How do you approach customer service and policy administration within your organization? In this blog post, I’ll demonstrate how artificial intelligence (AI) and a raised AIQ can help you get the most out of your data. (For the other articles in this series, click here.) To do this, I’ll discuss how insurers can use machine learning to analyze texts.

How can insurers use AI in customer service and policy administration?

The customer service and policy administration workforce can make their lives easier by using AI to:

  • Understand and act on external emails and requests.
  • Automate call center and webchat services—helping companies get on with more intricate work.
  • Enable self-service queries on policy issuance, endorsements, cancellations and renewals—using virtual assistants, for example.
  • Process unstructured data, which means fewer mistakes and better customer service.

How does AI improve customer services and policy administration?

AI enables more efficient administration processes. Insurance executives plan to invest in seven AI-related technologies in the next three years. They are: 

  • Machine learning; 
  • Deep learning; 
  • Natural language processing; 
  • Video analytics; 
  • Embedded AI solutions; 
  • Robotic process automation; 
  • Computer vision. 

See also: Policy Administration: Ripe for Modernizing  

In addition to increasing the efficiency of administration processes and enhancing analytical insights, AI technologies also benefit customer services through:

As I will show in the use case below, the customer service and policy administration workforce can use machine learning to process information faster and with greater accuracy.

Use case: Machine Learning Text Analyzer (MALTA)

Insurers today must figure out how to manage the exponential increase in incoming unstructured data. Eighty percent of data generated is unstructured, and the volume continues to grow exponentially. Forty percent of business executives complain that they have too much unstructured text data and don’t know how to interpret it.

Insurers face three main challenges:

1. Too much unstructured information

  • A large amount of information comes in through a variety of channels;
  • Incoming data is structured as well as unstructured;
  • Much of the workforce is occupied with processing unstructured information;
  • A large amount of unstructured information exists within the organization.

2. Too many communication channels

Customers use a large variety of channels to communicate with their insurance company, such as e-mail, contact forms, the service desk (e.g. ticketing), letters and applications.

3. The information is not linked to business processes

  • Workers lose a lot of time when they have to identify received information and allocate requests to the right channels;
  • They also lose time owing to inefficient processes caused by breaks in the system;
  • This prolongs the response time to clients;
  • Humans are prone to errors, which creep in at all points.

Solution: Machine Learning Text Analyzer (MALTA)

Now, insurers can automate the analysis and classification of incoming text by applying machine learning and using historical data.

How does MALTA work in customer service and policy administration?

MALTA can analyze any incoming documents, for example when customers send their policy documents via email.

These documents can be analyzed and classified using natural language processing methods and machine learning algorithms. MALTA is also trained with historical data, which enables it to classify, understand and extract information.

In the next step, MALTA links your customer’s policy document to business processes, prompting different functions to take action. Depending on the business and architecture set-up, MALTA or the output of the API triggers a process chain, a robot or an agent so that the necessary processing steps can be executed.

See also: In Age of Disruption, What Is Insurance?  

Benefits of MALTA

MALTA is flexible, customizable, independent, multilingual, state-of-the-art and end-to-end; using Accenture’s machine learning text analyzer, insurers can:

  • Increase classification accuracy and efficiency, and reduce errors.
  • Create individual learning models based on training data.
  • Deploy the solution on-premise, not only in the cloud.
  • Automate repetitive tasks, allowing employees to focus on more complex work.
  • Categorize new requests immediately and send them to the relevant departments.
  • Use state-of-the-art models and tools.
  • Work on a platform-independent web service.
  • Carry out classification outside regular business hours.
  • Cleanse data and extract and evaluate features.
  • Link robotics and process automation tools to classification.
  • Set up and train employees with minimal effort.

In addition to customer services and policy administration, insurers can use MALTA across other parts of the enterprise, for example:

Are you ready to power up your business with AI? Download the report on How to boost your AIQ for more insight.