Tag Archives: artificial intelligence

How AI Transforms Risk Engineering

In a year marred by crisis and uncertainty, the mature property and casualty (P&C) insurance industry has seen its workload increase in both volume and complexity. According to the Insurance Information Institute, insured losses from natural catastrophes in 2019 totaled $71 billion. That number is only expected to rise in 2020 with the onslaught of hurricanes and wildfires hammering the U.S.

Insurers must contend with a rapidly changing risk landscape. Falling interest rates, climate change, man-made risks and civil unrest are causing unprecedented destruction and business interruption. This is exacerbated by the COVID-19 pandemic, cyber security threats and global terrorism, causing the number of claims to skyrocket.

Traditional methods of risk analysis are slow and expensive. Risk engineers spend considerable time performing repetitive assessment and administrative tasks that do not add value to clients.

One saving grace is the global movement toward digital transformation and automation, including the adoption of artificial intelligence (AI). Changing client expectations have propelled organizations to rethink age-old processes. 

An artificial intelligence study by PwC said, “AI could contribute to the global economy by 2030, more than the current output of China and India combined.” The same report estimated $6.6 trillion would likely come from increased productivity alone.

See also: Stop Being Scared of Artificial Intelligence

How do you know if you’re ready to embrace AI, and what are some of the areas it could improve within risk engineering? Below are three points to consider:

The easiest way to get started, is to contemplate your market in five years’ time and consider what capabilities you will need to compete – McKinsey

1) Align Business and AI Goals.

A certain appetite and readiness for change is required on the part of the C-suite and by the risk engineers within your workforce. A real pain point must be met, and the implementation of AI must align with the overarching business goals of your organization. For risk engineering, the time is ripe for AI disruption. According to McKinsey, “Efficiency improvement is an imperative. The industry’s current trajectory is inefficient and unsustainable, creating the conditions for disruption. This would involve digital technologies, automation and data and analytics to not only reduce error-prone manual processes but also enable an agile way of working.” 

If account engineers and risk engineering consultants spent more of their time on risk verification and selection rather than aggregation and analysis, this would help underwriters speed up the time to assess and quote on a new bid and ultimately increase the chances of winning business. The first response to a submission wins over 50% of the time. 

Still, the question remains, whether your organization wants to be an early adopter, fast follower or follower. Will the AI solution you create in-house or via a third-party vendor disrupt the sector and provide you with a competitive edge?

2) Examine Internal Talent. Find Your AI Champions.

Another critical factor is talent. Are there champions within your company willing to take on the added time it requires to inform the user journey and customizations, perhaps even label the initial data and ultimately execute on the AI opportunity at hand? It is vital that there is a top-down and, equally, a bottom-up culture of adoption for AI implementation to succeed.

A global digital practice survey revealed that insurance companies are attracting less digital talent than other financial services companies such as fintech and asset management. In a recent survey, 80% of millennials said they have limited knowledge of the insurance industry, and 44% said careers in insurance sound “boring.” Orbiseed’s recent interview with a veteran risk engineer also revealed that the majority of senior risk engineers are close to retirement and may resist employing new technologies. “Indeed, perception can shape reality, and the current reality is that the insurance industry isn’t viewed as relevant or exciting to up-and-coming digitally savvy workers,” the report concluded. 

3) Partner With AI Vendors You Trust to Scale Quickly.

An AI firm should know your industry inside and out, have secure networks to help protect your data and enable you to scale your AI program fast. You will also need to consider whether to select AI integrations over ground-up builds. An integration will vastly reduce the time it takes to produce a working model for your business. A good software integration will also layer into the existing system you have rather than force your employees to learn an entirely new system.

See also: 3 Tips for Increasing Customer Engagement

Next Steps Toward AI Transformation in Your Organization

AI is fundamentally changing the way business is done in 2020. For mature industries that still rely on manual, labor-intensive processes, adopting new technology can make a measurable difference in efficiency and deliver significant competitive advantages.

Risk engineering seeks to manage risk: Adopting AI practices early will ensure that your organization hedges against the risk of falling behind the competition. Firms that effectively adopt AI early report significant performance gains compared with competitors, including higher revenues and reduced expenses.

Best AI Tech for P&C Personal Lines

Artificial intelligence technologies are everywhere. The great leap forward in AI over the past decade has come along with an explosion of new tech companies, AI deployment across almost every industry sector and AI capabilities behind the scenes in billions of intelligent devices around the world. What does all of this mean for the personal lines insurance sector? SMA answers this question in a new research report, “AI in P&C Personal Lines: Insurer Progress, Plans, and Predictions.”

The first step toward answering this question is to understand that AI is a family of related technologies, each with its own potential uses and insurance implications. The key technologies relevant for P&C insurance are machine learning, computer vision, robotic process automation, user interaction technologies, natural language processing and voice technologies. It’s a challenge to sort through all these technologies, the insurtech and incumbent providers that offer AI-based solutions and where each insurer will benefit most from applying AI.

The overall value rankings indicate that user interaction technologies fueled by AI are at the top of the list for personal lines insurers. Every insurer has activity underway, mostly by leveraging chatbots for interactions with policyholders and agents or using machine learning for guided data collection during the application process. Insurers see high potential for transformation in policy servicing, billing and claims – areas where routine interactions can be automated.

Robotic process automation is in broad use across personal lines, although the RPA technology is viewed by many as more tactical. There is high value related to streamlining operations and reducing costs, but most wouldn’t put it in the innovative category.

Machine learning and computer vision have great potential for personal lines in both underwriting and claims. The combination of computer vision and ML technologies applied to aerial imagery is already becoming a common way to provide property characteristics and risk scores for underwriting. Likewise, images from satellites, fixed-wing aircraft and drones are frequently used for NATCAT situations. And AI technologies will be increasingly applied to these images for response planning.

There are many other examples. But for the purposes of this blog, the main question – which technologies are most valuable – has been answered. AI-based user interface (UI) technologies, machine learning (ML) and computer vision demonstrate the best combination of high value today and transformation potential for the long term.

But perhaps the more important question is not which technologies are valuable, but rather where AI technologies are most valuable in the enterprise. The short answer is that there are so many potential value levers and so many unique aspects to different business areas and lines of business that it is difficult to select just a couple of high-value areas. That said, it is relatively apparent that underwriting and claims both present major opportunities, and activities are already underway there. There are great possibilities for AI in inspections, property underwriting, triage, fraud, CAT management, automated damage assessment, predictive reserving and other specific areas.

See also: Stop Being Scared of Artificial Intelligence

There is no shortage of opportunities for AI in personal lines. Fortunately, there are increasing numbers of tech solutions in the market and growing expertise in the industry involving AI technologies and how to apply them. Ultimately, we expect to see a pervasive use of AI technologies throughout the insurance industry. Some will become table stakes. Others will define the winners in the new era of insurance.  

AI Investment in Commercial Lines

Artificial intelligence (AI) has been in almost every technology-based headline over the past 24 months. If an incumbent technology provider or a newly emerging insurtech organization wants to grab attention – well, just insert AI in the first few lines of the description. Better yet, insert AI in the product or organization name.

In fact, AI holds exceptional business promise, and there are numerous proven use cases. But AI is a complicated topic.

There are many sub-categories of AI, and one of the first steps in choosing the appropriate technology is to break down AI into consumable bites. SMA finds that there are six primary AI technologies in play within commercial lines organizations: machine learning (ML), computer vision, natural language processing (NLP), user interaction technology, voice technology and robotic process automation (RPA). The big question is – which AI technologies drive the most value for commercial lines?

Not surprisingly, there is a tug-of-war between AI for transformational purposes and AI for tactical purposes. According to commercial lines executives and managers, ML, RPA, computer vision and NLP (in that order) will transform commercial lines the most. Given the general need for transformation across the insurance industry, one could conclude that the previously stated order of technologies would be where the industry is heading in terms of investment. But that is not the case.

The actual investment order is new user interaction tech, machine learning, RPA and NLP, with the remaining technologies following. Does this mean commercial lines insurers have gotten it wrong? The answer to that question is “no,” with possible shadings of “could be.” Much of the framing for this answer lies in the product mix.

For the small business and workers’ comp segments, new user interaction technologies such as chatbots and text messaging have been invaluable in contact centers. This affects underwriting and claims by clearing tasks from work queues, thus freeing up technical expertise for more complex interactions. Billing benefits, as well. Collaboration platforms and real-time videos proved highly valuable during the pandemic’s height and continue to be highly worthwhile.

Machine learning has universal value across product lines. Whether it be ML to improve straight-through processing for less complex lines, such as small business and workers’ comp, or to provide decision support for complicated product lines, ML can contribute in all areas. The great thing about this is that investment in adopting ML skills pays off across the enterprise.

RPA is a technology that not only improves operational efficiency and expense management – important internal goals – but also enhances customer and distributor satisfaction through rapid request fulfillment. Policy service, underwriting and claims all gain value through RPA adoption. Because almost all commercial lines segments have repetitive processes, RPA skills are used universally.

See also: COVID-19 Sparks Revolution in Claims

The “could be” warning comes in terms of computer vision and NLP. Both technologies have significant transformational value in commercial lines, ranging from turning aerial images into information to digitizing paper-based information sources. Prioritizing these technologies sooner rather than later is critical across all product segments.

More than almost any other technology, AI technologies work best in combination — for example, NLP with RPA to increase process penetration. The industry is in its early days when it comes to AI usage, and skill sets are still advancing. The “getting it right” discussion is frequently dependent on product segments. But, over time, value will be universal regardless of product complexity, albeit for different reasons.

For additional information on all six AI technologies and survey results, see SMA’s new research report, AI in P&C Commercial Lines: Insurer Progress, Plans, and Predictions.

How Insurers Are Applying AI

AI is everywhere. Insurers are piloting various AI projects, insurance technology vendors are building it into their solutions, some insurtech startups are all AI-powered and horizontal tech vendors are creating AI platforms that sit underneath it all. Insurers that haven’t experimented with AI yet are benefiting from the technology through third-party relationships, even if they don’t realize it. 

Unfortunately, the broad scope covered by the umbrella term “AI” can cause confusion for insurers — especially because some technology providers use this label to better position their offerings in the marketplace.  

Usage of AI in the insurance world can typically be broken down into four categories:  

  • Machine Learning. The goal of machine learning, a process where an autonomous system learns from a data set to identify novel patterns, is often to refine underwriting or claims algorithms. Applications include advanced predictive modelling and analytics with unstructured data. 
  • Image Recognition. Until recently, images were a type of unstructured data better resolved by humans. Image recognition leverages AI to extract insights from digital image analyses. Applications include photo analysis and handwriting processing. 
  • Audio Recognition. AI-enhanced audio recognition captures any sound (from human speech to a car horn) and turns it into a rich, usable data source. Applications include speech recognition and non-voice audio recognition. 
  • Text Analysis. AI-powered text analysis is pulling out meaningful insights from a body of text (structured or unstructured). Applications include form reading and semantic querying. 

Justifying the Use of AI in Insurance

Novarica’s Three Levers of Value framework can help conceptualize the business value of each AI use case for insurers. Each of these levers — Sell More, Manage Risk Better and Cost Less to Operate — is applicable to a specific AI technology use case. 

Helping insurers identify upsell/cross-sell opportunities, for example, falls under sell more, while accelerating underwriting risk assessment could be categorized as managing risk better and enabling more efficient help desk support helps insurers cost less to operate. 

These are just a few examples of the value AI can bring insurers; AI use cases span categories such as product/actuarial, marketing, underwriting, customer service, billing, claims and compliance. Key use cases include: 

  • Deploying better pricing models. This machine learning use case chiefly falls in the domain of product owners and actuaries, as it applies to the area of predictive analytics. In this case, AI can help actuaries make better decisions when pricing products, thus managing risk better. 
  • Improving marketing effectiveness. This machine learning marketing use case involves using third-party or internal tools to analyze vast amounts of raw data and identify the media channels and marketing campaigns with the greatest reach and engagement levels. Here, big data analytics can help insurers sell more. 
  • Performing better property risk analysis. Using AI-powered photo analysis, underwriters can generate faster and more accurate roof damage estimates. Ultimately, this helps insurers manage risk better. 
  • Leveraging smart home assistants to deflect calls from call centers. Through a voice prompt to their smart home assistants, customers can get quotes, request policy changes and even start a home insurance claim thanks to AI-powered audio recognition. By offering another avenue to help answer customers’ FAQs, insurers free their call center employees to address more complex customer inquiries, decreasing operating costs. 
  • Increasing invoice processing speeds. Through use of text analysis and image recognition technology, AI can help billing staff eliminate error-prone human invoice handling. Using AI-powered form reading leads to greater process efficiencies, which lowers operating costs. 
  • Identifying and mitigating claims fraud. Here, machine learning can help identify potentially fraudulent claims faster. This processing speedup gives claims staff more time to focus on higher-value transactions and leads to better risk management. 
  • Enabling automatic handling of compliance requirements. Machine learning can help team members improve compliance and reporting by automatically handling complex compliance requirements. This results in lower operating costs as compliance staff can direct their attention to tasks requiring human review. 

See also: 4 Post-COVID-19 Trends for Insurers

The AI ecosystem is evolving quickly, with new technology applications emerging every day. We may soon even see further AI and ML processing speedups with the advent of quantum artificial intelligence and machine learning.  

Insurers should not invest in technology-driven projects; instead, governance should search for use-case-driven projects that most benefit the company. However, in the case of important emerging technologies — like AI and ML — it’s valuable to look for ways to deploy that technology and build up skill sets (and culture) within the organization. Additionally, many insurers have an innovation group whose sole purpose is to future-proof the organization by seeking out opportunities to deploy emerging technologies. In these cases, it’s important to refer to actual business use cases and elucidate the concrete value they provide to specific business units.

To learn more on this topic, check out Novarica’s brief, Artificial Intelligence Use Cases in Insurance.

Optimizing Care with AI in Workers Comp Claims

The expert panel explains how AI can:

  • let you identify the right provider for a case and steer the injured worker to that provider
  • help busy adjusters easily spot potentially troublesome cases and manage them better, from start to finish
  • continuously optimize your network of providers, so you can be sure to have the right provider working with the right worker at the right time.

This panel consists of: Gary Hagmueller, CEO of CLARA analytics; veteran adjuster Nicole Corey; and CLARA analytics Chief Medical Officer Paul Kim. The panel is moderated by Paul Carroll, Editor-in-Chief of Insurance Thought Leadership.

Don’t miss this free on demand panel discussion. Space is limited, so register today!


Presenters:

Gary Hagmueller

CEO, CLARA analytics

Dr. Paul Kim

Chief Medical Officer, CLARA analytics

Nicole Corey

Owner, California Work Comp Advocacy

Paul Carroll

Editor-in-Chief, Insurance Thought Leadership