The Insurance Industry is Being Disrupted. AI Can Help Traditional Insurers Stay in the Game

The insurance industry is growing. Despite the 2020 slowdown, the market is poised to grow this year to pre-COVID-19 rates. Analysts project a strong V-shaped recovery in insurance premiums, with a return to around 3% positive growth in both life and non-life premiums in 2021. This growth is attributed in part to dramatic technological changes that are reshaping how the insurance industry works. Indeed, forward-looking insurers are  investing in becoming more customer-centric while increasing operational efficiency.  

However, there are numerous challenges that are hindering this traditional industry from achieving the growth vectors that are characteristic of other verticals. The top three challenges are the rapidly changing technology, evolving consumer behavior, and the emergence of new, purely digital insurance providers. According to a  PolicyAdvice research, three out of ten insurers believe that the rate of technological change is exceeding their ability to adapt; 21% of insurers expressed concern about changing consumer behavior, and 10% are worried about the new players in the market.  

Insurers in both advanced and emerging markets are struggling in these areas:  

  • Operational efficiency. The insurance industry is traditionally manual and paper-based. All processes —  from underwriting to claim assessment — are slow and resource heavy, demanding days and weeks to complete. In addition, the manual nature of the work makes it prone to human errors and biases.  
  • Consumer behavior and expectations. Consumers increasingly expect the speed, convenience and transparency that they’re used to from other digital experiences. This is especially true a year and a half into the COVID-19 era, which has seen exponential adoption of B2C digital services. Indeed, more than 50% of consumers think insurance companies are lagging other industries in both responsiveness, technology tools and personalized products​.  
  • The rise of InsurTechs. InsurTechs are dramatically disrupting the industry, changing the paradigm and posing new threats on traditional incumbents. Fast growing companies such as Lemonade and Hippo have the upper hand when it comes to providing personalized policies, superb digital experiences and advanced processes and payment models, hence challenging the industry’s  core business.  
  • Innovation hardships. More than perhaps any other industry, insurance companies face stringent regulation, making it especially hard to innovate. In addition, a lack of digital and technical knowledge within the industry is making the adoption of new  technologies slow and cumbersome in the best case scenario, and non-existent in the worst. For most insurers, digital transformation is not yet in the stars.  

The focus in insurance is shifting to AI 

Insurance executives are well aware of these challenges, as well as of the need to shift priorities. Nearly all insurers are at least planning to enhance their digital capabilities and offerings over the next 6-12 months to maintain resilience and competitiveness. 54% of insurance executives name the use of data and technology, digitization and automation as the most important areas of their company to grow in during the next year, according to Deloitte. Focus is shifting into three main areas:

  • More effective data analysis. This helps create a truly customer centric model, adding new bespoke products and services and offering rewards and promotions that are relevant to customers and would increase their loyalty. ​ 
  • More partnerships and collaborations with technology companies. These may include financial services organizations and possibly even competitors, with the aim of delivering a frictionless, more engaging customer experience. ​ 
  • Removing customer pain points. Customers dread filling out multiple forms with questions they’re not sure how to answer. This can be resolved by shifting focus from the organization’s internal process to the customers’ experience, delivering a user friendly mobile experience.​ 

Indeed, Deloitte predicts that “the insurance success stories of tomorrow will be those that can streamline administrative processes, such as onboarding and claims payout, and that can offer a range of innovative and flexible products to customers in a way that interests them.” The common denominator for achieving these goals is AI in insurancethe adoption of artificial intelligence for the innovation and automation of current processes within the industry.  

What does AI mean in insurance? 

Artificial Intelligence is a term used to describe a set of capabilities that can be performed by digital systems quickly and at scale, at a quality equal to or surpassing that of comparable manual processes. In recent years AI has reached an inflection point in key areas, making it uniquely suitable for the needs of the insurance industry. Some of these capabilities are: 

  • Natural Language Processing (NLP). Based on Machine Learning, a subset of AI, NLP can be used for processing any written text and for extracting data from complex, unstructured documents. ​It enables computers not only to “understand” the content but to also summarize it, translate across all languages, and further process it. 
  • Computer Vision. Enabling computers to “see”, computer vision (and its close cousin, Image Recognition) are widely used across industries for analyzing the content of photos and images to determine their content and its state (for example, identifying a person’s ID card or even selfie shot in the process of authenticating her or him as a customer). 
  • Speech Recognition. Speech recognition uses NLP capabilities to parse and process spoken text in any language, and is often used for interaction with customers across different platforms.  

When working individually or in tandem, these advanced capabilities are used by insurers across AI use cases to: 

  • Increase automation. AI technology can offer faster services with fewer errors. McKinsey estimates that by 2025, 25% of the insurance industry will be automated thanks to AI and machine learning techniques, including 50-60% of back-office insurance processes.
  • Use more advanced data analytics. The worldwide growth in data produced by connected devices (IoT) enables insurance companies to evaluate their customers’ risk profile more accurately – if they can capture that data and effectively analyze it with AI.
  • Deliver a streamlined customer experience. Like in other industries, AI can be used by insurers to expedite processes, automate customer service, and meet customer expectations for instant service, offered clearly on any mobile device with minimal interaction with a human service provider.
  •  Augment agents. AI will not replace insurance agents anytime soon, but it can surely empower them with data, analytics and tools to expedite and optimize their decision making and pricing, yielding better results.

Artificial Intelligence use case: Underwriting 

The global insurance underwriting market size is on the rise: According to a recent Pitchbook report, it’s expected to grow at a compound rate of around 4.6%, reaching $553B by 2025. Underwriting involves researching and assessing the degree of risk each applicant or entity brings to the table before assuming that risk; establishing appropriate premiums to adequately cover the true cost of insuring the policyholders; accurately pricing the investment risk; and ensuring a profitable outcome for the firm even in the case of claim submission in the future​. 

For insurance companies, underwriting is one of the most time-consuming and resource-heavy tasks. This is especially exacerbated since most insurers still rely on a legacy technology stacks, making the purchasing journey analog, expensive, manual, paper-based and exceedingly lengthy: some insurers take up to 30 days to decide on an insurance policy applicant. ​In addition, inadequate information, errors and biases often result in mispriced premiums​.  

As a critical function in the insurance value chain, insurers investing in AI to augment and streamline their underwriting businesses will inevitably gain a sustainable edge over competitors.

The following are examples showing how companies can strengthen their underwriting services with the power of AI:  

  • Optimizing risk and pricing. AI widens the scope of data sources that underwriters can use for evaluations. Big data analytics allows deeper visibility into customers’ risk profiles so that premiums that match each individual’s risk can be offered.  
  • Offering a quick, frictionless customer experience. With consumers increasingly expecting near real-time services across industries, drastically shortening the duration of onboarding and underwriting workflows from up to a month to an instant can be a game changer for the industry. 
  • More accurate segmentation and personalization. AI can be used to create granular segmentation based on similar and previous cases. This not only creates more personalized offerings for customers, but also enables insurers to optimize their pricing.
  • Improved profitability. By bringing all these capabilities together, the AI-based automation process improves underwriting profitability and improves the loss ratio.  

Artificial Intelligence use case: Claims Assessment  

ccording to the EY Global Consumer Insurance Survey, 87% of policy holders think claims experience has an impact on their decision to renew with an insurer, with settlement speed and process transparency being the next most important factors. Claims processes have a huge impact on customer retention, but it is also an area where insurers are focused on reducing their loss adjustment expense. This inherent tension negatively impacts both the customer and the insurer:  

  • ​Claims assessment is a time-consuming, highly manual process that’s frustrating for both insurers and customers: claim processing typically takes several days as insurance agents have to gather data and crosscheck it against multiple sources​. 
  • The process is highly prone to human error due to mismatched financial data or customer details, and decisions are often made with relevant data missing.
  • The insurance claims journey has historically been opaque and confusing to customers, who are increasingly expecting transparency and clarity.
  • Claims management is often seen as a cost center only.​ 

AI is the future of claims automation. AI domains such as Computer Vision and NLP have the potential to help individuals and insurance companies throughout the stages of the claims lifecycle, making it faster, more accurate and more efficient: 

  • Exceedingly faster processing time. By substituting manual, rule-based legacy workflows with AI automation, claims assessment processes become not only faster (reduced by up to 15 days) but can also be easily scaled, reducing processing costs by up to 50%.  
  • Improved profitability with data-based decision making. AI-based claim assessment makes the optimal use of all available data, including real time information. Better data analysis and processing lead to more accurate financial decisions and settlements. 
  • Higher customer satisfaction and loyalty. A faster, more personalized process that is transparent to the customer and provides quicker payments with less hassle leads to sustainable customer satisfaction and retention.  

AI adoption challenges

For insurers, AI promises to be a game-changer at every level of the value chain, completely transforming the way business is conducted. According to consulting firm LTI AI technologies have potential to create between $100B-$300B in value annually across the insurance industry. That’s why a whopping 95% of insurance executives intend to start or continue investing in AI capabilities in the future.  To maintain a competitive advantage in light of rising customer expectations, growing InsurTech companies, and an increase in data volumes, traditional institutions need to step up and work on strategies to combat the new players in the market. 

However, for most insurance companies, AI adoption and implementation rates are still slow. The main hurdles of implementing AI-driven solutions in insurance companies relate to a lack of clarity regarding business value, limited resources and internal knowledge, heavy regulation, and an overwhelming number of potential use cases, leaving the majority of companies stuck in “pilot purgatory.”

Developing AI solutions in-house to address specific  insurance use cases requires cutting-edge domain expertise. Enterprises attempting to build AI solutions on their own often end up amassing huge costs and an extremely long time to value on their AI projects, which, depending on scope, take anything between one and three years to achieve — if success in production does indeed ensue.  

The BeyondMinds end-to-end AI platform for insurance companies 

BeyondMinds has built the first end-to-end enterprise grade solution for insurance companies. From onboarding and underwriting to settlement on claim assessment, the BeyondMinds platform is the only platform in the market offering the full gamut of capabilities that enables insurers to maximize the potential of AI. By harnessing a full suite of cutting edge AI capabilities, including data capture, visual assessment, risk management and pricing/payment analysis, the BeyondMinds platform covers all major AI insurance use cases, enabling insurance companies to benefit quickly and enduringly from AI, while freeing themselves of the risks and burdens of its development and ongoing maintenance. 

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