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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.

Commercial Insurers Face Tough Times

Beyond the secular forces we described in our “Future of Insurance” series, more immediate and cyclical issues will be shaping the insurance executive agenda in 2016. Commercial insurers (including reinsurers) face tough times ahead, with underwriting margins that are being pressured by softening prices and a potentially volatile interest rate environment.

Recently, reserve releases, generally declining frequency and severity trends, as well as lower-than-average catastrophe losses have allowed commercial insurers to report generally strong underwriting results. However, redundant reserves are being (or have been) depleted, and the odds of a continued benign catastrophe environment are low. For example, one insurance executive recently observed, “The odds of this long of a lucky streak occurring is less than 1%.”

The commercial insurance market has, in recent years, had generally strong underwriting results, but this could change—potentially, very soon.

With varying degrees of focus, commercial P&C insurers have been mitigating the risk environment by taking a variety of strategic actions. In 2016 and beyond, they will need to accelerate their strategic efforts in four key areas: 1) core systems and data quality, 2) new products, pricing discipline and terms and conditions, 3) corporate development and 4) talent management.

Core systems and data quality

93% of insurance CEOs—a higher percentage than anywhere else in financial services—see data mining and analysis as more strategically important for their business than any other digital technology. Nevertheless, many commercial insurers operate with networks of legacy systems that complicate the timely extraction and analysis of data. This is no longer deemed acceptable, and leading insurers continue to transform their system environments as a result. Significantly, these transformations do not focus solely on specific systems for policy administration, claims, finance, etc.

To ensure timely quality data across the entire commercial P&C value chain, commercial insurers also focus on how the various systems are integrated with one another.

To put this into context, when a dollar of premium is collected, it not only “floats” across time until it is paid out in claims, it also “floats” across a variety of functions and their related systems: Billing systems process premium dollars; ceded reinsurance systems process treaty and facultative transactions; policy administration systems (PAS) process endorsement changes; claims systems process indemnity and expense payments.

Actuarial systems in disconnected data environments prevent the timely and efficient extraction and analysis of internal data and also complicate the focused and efficient use of external data, especially unstructured data. “Big data” is becoming increasingly popular considering the insights that insurers and reinsurers can derive from it. However, such insights only become actionable to the extent that companies can assess the external environment in the context of the internal environment—in other words, to the extent that big data can enhance (or otherwise inform) the internal data’s findings.

If all functional and systemic codes are not rationalized on an enterprise-wide basis, it is very difficult to efficiently accumulate and analyze data.

New products, pricing discipline and terms and conditions

Commercial insurers and reinsurers are not generally known as product innovators, but they can be. For example, as the profile of cyber-related risks increases, the need for cyber-related commercial insurance grows, thereby offering numerous opportunities for product innovation.

Because cyber is a relatively new exposure, frequency and severity data are nascent, therefore both pricing and risk accumulation models are in various stages of development. As a result, prescient insurers are carefully tracking and comparing their cyber pricing practices and coverage grants with those of key competitors. To be effective, such practices should be consistent with existing price, terms and conditions and monitoring processes. For example, tracking actual-to-expected premiums and rates is a common practice, which leading insurers perform regularly (i.e., at least quarterly, with monthly tracking common).

Insights from this kind of analysis apply to both new and existing products. The underwriting cycle is inherently a pricing phenomenon, and insurers and reinsurers that have greater and more timely product and pricing insights have a competitive advantage relative to those that do not. To explain, in addition to lower rates, the “soft” parts of the underwriting cycle tend to be characterized by the loosening of policy terms and conditions, which can erode profitability as quickly as inadequate prices. Therefore, the most competitive insurers and reinsurers carefully and continuously track the adequacy of policy terms and conditions. Recurring actuarial analyses and standardized reporting can monitor changes in pricing as well as in terms and conditions. However, identifying emerging underwriting risks is inherently qualitative. Therefore, this analysis can be time-consuming, especially for insurers with suboptimal PAS environments. However, almost all companies find the analysis well worth the effort.

Corporate Development

The combination of historically low interest rates, favorable frequency and severity trends and the relative lack of severe catastrophes has resulted in record policyholder surplus across P&C commercial insurance. Executives have a number of options on how to deploy surplus, one of which is corporate development.

Commonly, “corporate development” means mergers and acquisitions, but it can also encompass book purchases/rolls, renewal rights and runoff purchases. Determining the best option depends on many factors, including purchase price, competitive implications and an assessment of how the acquired assets and any related capabilities can complement or enhance existing underwriting capabilities.

Accordingly, some insurers are beginning to augment traditional due diligence processes (such as financial diligence, tax diligence and IT diligence) with underwriting-specific diligence to help ensure value realization over time.

If a corporate development opportunity offers underwriting capabilities that at least align to—and preferably enhance—existing capabilities, it can help facilitate a smooth integration, thereby mitigating underwriting risk (a key cycle management consideration).

Talent Management

For the most part, commercial underwriting decisions cannot be fully automated because they require judgment. Therefore, it is natural for underwriting talent to be a top priority. However, insurance executives have lamented that it is a major challenge for the industry to attract and retain knowledgeable personnel.

Two trends make commercial insurance talent management particularly challenging. First, experienced underwriters are leaving the industry. According to one study, “The number of employees aged 55 and over is 30% higher than any other industry—and that, coupled with retirements, means the industry needs to fill 400,000 positions by 2020.” Second, underwriting talent is relatively difficult to attract. For example, according to the Wall Street Journal, insurance ranks near the top of the list of least-desirable industries—according to recent graduates. The image of the insurance industry is that it is generally behind the times and offers little in terms of career development. Therefore, developing a performance-driven culture that enables the recruitment, development and retention of underwriting talent is more crucial than ever.

To help accomplish this, insurers should employ and should continuously assess tools and resources that educate and empower underwriters through all phases of their careers. This is important because the expectations in commercial underwriting are high, and the nature of the job requires a diverse range of skills (e.g., analytical, relational, sales, financial and risk). Furthermore, the best commercial underwriters are entrepreneurial, which employers should highlight as they recruit and manage their underwriting staffs.

Commercial insurers face a looming talent crunch and have to find ways to present themselves as—and actually be—a place where young people can have rewarding careers.

Implications

  • The relatively strong underwriting results of recent years are likely to soften in the coming year. Accordingly, commercial underwriters will need to accelerate their strategic efforts in:
  1. Core systems and data quality,
  2. New products, pricing discipline and terms and conditions,
  3. Corporate development
  4. Talent management
  • Core systems transformations go beyond individual system competencies. To ensure timely, quality data across the entire commercial P&C value chain, insurers also are focusing on how the various systems are integrated with each another to facilitate the timely and efficient extraction and analysis of internal data and the focused and efficient use of external data (especially unstructured data).
  • There are opportunities to create new products, but, to be profitable, insurers must exercise pricing discipline and must carefully and continuously track the adequacy of policy terms and conditions.
  • Current surplus levels have enabled insurers to invest in corporate development, and some insurers have augmented traditional due diligence processes (such as financial diligence, tax diligence and IT diligence) with underwriting-specific diligence to help promote value realization over time.
  • Commercial insurers have an aging workforce and are facing an impending talent crunch. Automation cannot replace the judgment that is required for effective underwriting. Therefore, it is vital for insurers to develop a performance-driven culture that enables the recruitment, development and retention of underwriting talent over time.