Tag Archives: STP

Leveraging AI in Commercial Insurance

Softening prices, little or no organic growth and increased competition have characterized most of the commercial insurance environment in recent years. These factors and a relatively benign cat environment continue to attract new types of capital providers (e.g., hedge funds, pension funds, foreign investors, capital markets) looking to diversify their investment portfolios with uncorrelated insurance assets.

Limited organic growth opportunities also have led to a broad consolidation of distributors, with an increasingly large number of private equity-backed brokers looking for short-term gains and opportunities to reduce systemic inefficiency. In turn, this has led to significant carrier investments in automation to facilitate effective and efficient straight-through processing (STP).

More specific responses to market conditions from commercial insurance constituents include:

  • Distributor response – Distributors are increasingly looking for ways to (1) negotiate more aggressively on individual transactions (e.g., appetite exceptions, non-standard terms and conditions, pricing), (2) operate more efficiently (e.g., customized processes, only partial completion of applications) and (3) exert their bargaining power to gain higher commissions and other sources of revenue (e.g., access to market intelligence).

In addition, brokers are becoming increasingly organized. They are looking to 1) reduce the number of carriers with whom they place business in favor of ones that have a broad underwriting appetite and are easy to do business with and 2) exit the service arena, especially on small commercial accounts where margins are already extremely thin.

  • Carrier response – Carriers are intensifying their efforts to compete for a “top three” position with distributors by attempting to (1) be easier to do business with (both in terms of technology and personal relationships), (2) increase product specialization and related underwriting expertise, (3) increase their appetite for more hazardous risk and 4) (as a less favored option) lower rates and pricing.

Although more and more carriers have invested in automated underwriting and pricing, broker/agent expectations are only increasing. They not only want to clearly understand a carrier’s underwriting appetite, they also want to get near-real-time quotes on the majority of standard risks without extensive manual data entry on their side.

For now, carriers have avoided being “spread-sheeted” by using proprietary agent portals to increase ease of business interactions, rather than directly integrating with agency management systems and comparative raters. Distributors have not yet increased their demands for the latter two, recognizing that they could lead to a commission squeeze or even losing their appointment if the portability of their book declines with a given carrier.

  • Customer response – Last but not least, customers’ behaviors and expectations are changing, too. They are becoming more comfortable researching business insurance online, and expect their shopping experience to reflect what they see in personal insurance. However, they are still turning to an agent (whether digitally or in person) to confirm their purchase decision and complete the deal. This is especially the case when businesses mature and risk management becomes more critical to their success.

See also: Seriously? Artificial Intelligence?  

As all this has been happening, artificial intelligence (AI) has matured significantly, demonstrating that it can markedly improve existing STP. We describe below the AI technologies – including robotic process automation, natural language processing and machine learning – that can increase commercial insurance’s efficiency and effectiveness and thereby benefit investors, distributors and carriers themselves.

Availability and access to large volumes of data, increasing processing power, cloud computing, open-source software and advances in algorithms have fueled the rise of AI from academic curiosity to commercial viability.

The next generation of straight-through processing

Although many carriers are already heavily automated, their initial focus has largely been on automated underwriting and pricing. This has left considerable manual intervention in the issuance process, post-bind audits and other downstream transactions. All of these can be streamlined to further drive down costs. Once carriers move to truly mechanized underwriting, the next step will be to embed third-party data feeds and advanced analytics to drive straight-through processing (STP) of risks.

For example, imagine a small business owner being able to enter just four pieces of information (e.g., business name, business address and owner’s name and DOB) on a policy application and receiving a real-time business insurance quote with the option to immediately purchase and electronically receive policy documents. Furthermore, imagine this approach having no impact on underwriting quality or manual back-end processing requirements for the carrier. Integrating AI techniques and additional internal and external data sources into small business processing have the potential to make this a reality.

A combination of leveraging internal data from prior quotes and policies, integrating external structured data feeds and mining a business’s website and social media presence could provide carriers with enough information to determine a business’s operations, applicable class codes, property details, employment and payroll and other key risk characteristics to underwrite and price low-complexity risks. In cases where more information is needed, dynamic question sets with user-friendly inputs could streamline the application process without sacrificing underwriting quality.

How AI can improve straight-through processing

In addition to immediate cost improvements, commercial carriers
that leverage internal and external data resources and apply AI to commercial processing can benefit from reduced turn-around time, better and more consistent decision-making and improved agent/customer satisfaction.

The carriers that are the first to adopt the latest in AI-enabled straight-through processing will be preferred by their existing agencies, as well as be able to pursue alternative distribution channels that feature a more streamlined, user-friendly acquisition process that accommodates less sophisticated users.

Some of the most promising AI techniques that can help insurers improve STP include:

  • Robotic process automation (RPA) is an area of AI that could increase STP efficiency and bring down costs at acceptable level of increased risk. RPA automates data entry, third-party data integration, form filling and data validation. More advanced process-mining techniques use machine learning to infer business processes from transaction logs, web and call center logs, email, and workflow logs. They profile the time it takes for different steps of the quote-to-issue process to be fulfilled and, to streamline the process, plot a distribution that enables the identification of outliers. They also track exceptions, and the reasons for them, thereby enabling greater efficiency. RPA is also tracking conformance and compliance with established standards, thereby leading to more consistent and compliant service delivery.
  • Machine learning is building routing logic and underwriting-related models. For example, a detailed analysis of a commercial book of business over time can identify the need for no- touch, medium-touch or high-touch interaction models. This categorization enables better routing across multi-segment (i.e., small commercial, middle market and large commercial) insurers. In addition, machine learning can inform a wide variety of predictive models.
  • Using open source technology, PwC has built natural language processing engines that continuously evaluate a large number of news and social media sources and report on key concepts.

Commercial insurers and brokers can use this ontology of “key concepts” to traverse the output, identify drivers of specific risks and refer to articles related to these risks. By indicating the relevance of articles (e.g., via a thumbs up or thumbs down) insurers can “train” the natural language engine to look for specific sources and type of articles. As the system learns over time, it can graph trending topics, the sectors and companies associated with certain risks and the underlying impacts if the risks develop adversely. We also have built a question-answer engine that allows risk experts to make natural language inquiries and retrieve relevant reports and documents to conduct further analysis. With natural language generation, the engine also can create risk profiles for senior management’s consumption.

See also: 10 Trends at Heart of Insurtech Revolution  

By coupling deep learning systems with natural language processing, PwC has been able to create powerful risk analysis enablers that enhance and speed up emerging risk analyses. When analyzing text from news sources or social media sources, the system needs to understand the context under which certain words are used. For example, a common word like “run” has more than 645 meanings according to the Oxford English Dictionary. “Deep Learning” or neural network-based machine learning systems can actually capture the context of words within sentences, sentences within documents and documents within a collection of documents.

In closing, even with their increased focus on ease of doing business, there is still much room for carriers to improve. There currently is a clear opportunity for prescient and active carriers to separate themselves from the pack, but doing so will require a competitive mindset that has not traditionally defined the industry. Small and medium commercial carriers must find ways to improve their cost structures to compete profitably in the long term. AI-enabled solutions offer some of the most promising ways to do this.

Implications

  • New investors in the commercial insurance market are increasingly looking for short-term gains and greater efficiencies from the industry.
  • Moreover, distributors are looking for greater ease of doing business with commercial carriers and have demonstrated a willingness to favor the ones that can meet their expectations.
  • Commercial carriers have automated quoting in an attempt to facilitate effective straight-through processing. This has increased efficiencies, which has benefited investors and helped improve the distributor experience.

However, many manual processes and inefficiencies still remain. Once carriers move to truly mechanized underwriting, the next step will be to embed third-party data feeds and advanced analytics to drive straight through processing of risks. Recent developments in artificial intelligence (AI) can help carriers do this.

What Does Success Look Like?

It seems every press release you read, every case study in the news, every session at industry conferences and every webinar on tap for the next six months will at some point mention the 100% implementation success rate of the vendor involved. That fact, in and of itself, throws serious shade on what really constitutes implementation success and dilutes the impact or validity of the concept as a whole, but should it?

Depending on where a person sits, implementation success can mean different things and may include different elements, technologies or metrics. Implementation success is therefore often qualified by varying criteria that are completely dependent on the role of the individual in the project or the company. To truly guarantee implementation success, all perspectives and perceptions must be considered and incorporated.

For the CEO, it’s all about the big picture. Sure, nearly all CEOs want an increased ability to process new business and grow the company organically, but time and again individuals in this role will focus on these key questions:

  1. Did we implement what we set out to implement?
  2. How will this implementation affect our ability to modify existing products or launch new ones?
  3. Does this implementation support our construction of a future-ready technology environment?

For the CFO, everyone instantly assumes a successful implementation is simply about being on-time and on-budget, and while those factors are definitely important, CFOs additionally want to know:

  1. What is the maintenance and licensing like on this new technology product, and how does it affect our total cost of ownership (TCO)?
  2. Does this implementation make other downstream or supporting systems obsolete, requiring the company to make additional technology investments in the coming year(s)?
  3. Does this implementation allow the company to retire existing legacy systems and recognize cost savings in maintenance and support of these systems?
  4. Is support or the professional services required to implement changes included in the initial contract price, or is it an additional, and continuing, charge?

For the CIO, data conversion is a crucial, yet truly not sexy, part of the package that allows one system to be turned off and the other turned on, so to speak. It is important to understand that while CIOs are often thought to have the most interesting, cutting-edge piece of the insurance technology puzzle, these individuals are not easily distracted by solutions, tools and gadgets that turn out to be little more than bright, shiny objects. Questions CIO typically focus on when measuring implementation success include:

  1. Does my internal team have the expertise today to maintain the new solution, including making simple changes without deep technology programming expertise or the ability to create and implement custom coding?
  2. Will I be able to easily integrate emerging technologies as the need arises?
  3. What is the upgrade path for this solution that will clearly demonstrate my company is not implementing legacy?

Other players, including the company’s heads of claims, underwriting and customer service, are counting on achieving a certain percentage of straight-through processing (STP), decreasing the time from first notice of loss (FNOL) to claim resolution, and still others are rabid about mobile access and self-service capability delivered via a portal. Alternatively, FAIR Plans, for example, are less concerned about growth and bottom line profits, but instead are focused on increasing internal efficiency and delivering a top-quality customer experience. Different strokes for different folks.

So, maybe it’s time to acknowledge that the magical middle ground that will make everyone happy likely doesn’t exist. It’s back to the old saying that it’s impossible “to make all of the people happy all of the time.” The trick is knowing which stakeholders’ happiness is on the nice-to-have list and which is on the must-have list. Keep in mind, there are degrees of happiness, and incorporating even small pieces of capability can be important when it means validating stakeholders’ priorities and implying broader ownership across the enterprise.

Ultimately, what composes implementation success is unique to each company and should be well-defined for each company before the start of the project. All projects should have a well-defined set of expected outcomes from both business and technology that need to be achieved to have that project defined as a successful delivery. While budget and schedule can be a part of the objectives, they should not be the primary drivers. A successful implementation is one the delivers the required business and technology outcomes.

When the core system implementation itself is done right, with the right partners and a well-defined set of objectives, it leaves room for peripheral goals to be achieved at the same time with a faster ROI and the ability to get back to the business of insurance.