Tag Archives: straight-through processing

Straight-Through Processing in 2021

Straight-through processing (STP) is becoming more common in insurance underwriting and claims, especially in personal lines, individual life and small commercial. Adoption rates in more complex lines and in claims processes remain relatively low, so STP is by no means universal across the industry. However, it’s likely to remain a priority for insurers seeking to improve the ease of doing business for their distribution partners and create more convenient customer experiences for their policyholders.

Generally speaking, STP refers to the ability for insurer systems to automatically process transactions without manual intervention or input. The system ingests data digitally and completes the transaction based on decisions governed by algorithms, including predictive models and simple business rules. STP offers insurers benefits in speed, consistency, productivity and application throughput while making the customer experience quicker and more convenient.

Technological Factors

Technological improvements over the past two decades have been a major factor in the rise of STP. The most fundamental of these has been the internet, which has both enabled the connectivity underlying STP as well as shaped consumer expectations for user experience and speed.

More recently, modern insurance core systems with the ability to automatically adjudicate applications based on configured business rules, combined with modeling capabilities that can capture underwriting factors and predict outcomes, have made it possible to process applications without human oversight.

Insurers also now have access to a wealth of high-quality, third-party data, which can enable pre-fill, eliminate unnecessary questions or inform insurers of potential risk factors. AI and machine learning capabilities to refine algorithms and flag potential fraud have also contributed to insurers’ confidence in STP.

STP in Underwriting

STP in underwriting is most common in personal lines and individual life—lines that are under cost pressures and, increasingly, sold online. More than 80% of insurers selling these lines have at least some level of automated underwriting, and many personal lines insurers process straight through more than three-quarters of the time.

Large commercial and specialty lines typically don’t have high rates of STP, because these lines are generally sold through agents and brokers. Even where insurers have supported some level of automation (for example, via portals with rating components), most policies aren’t written straight through. Instead, insurers are focusing on distribution connectivity, which is itself a prerequisite for effective STP.

See also: The Digital Journey in Commercial Lines

Small commercial and workers’ compensation lines occupy a middle point. Many of these products still require manual underwriting, but they’re also seeing increasing direct sales activity, often directed at niche market segments. Carefully defining sales targets in this way allows insurers to facilitate STP for these lines, as they can design tailored products governed by specific business rules that rule out more complex risk scenarios.

STP in Claims and Digital Claims Payments

For most insurers, though, STP in claims is fairly uncommon. Nearly 60% of insurers have no STP in this area. On average, fewer than 10% of claims are processed straight through in any line. It’s most common in personal lines and (for payouts) in annuities.

Claims STP is likely to become more common, especially in personal lines and individual life, as insurers continue to improve their core system capabilities and as the availability and quality of third-party data improves. Where coverage limits are relatively low, insurers can increase their levels of automation to create faster and more convenient claims processes. 

Insurers have achieved more substantial automation in digital claims payments. A third to half of insurers process and deliver claims payments digitally (depending on the line of business), and 10% to 20% of insurers do so most of the time. Digital payments are likely to be a priority area for insurers. Since COVID-19 forced many insurers to send some workers into the office to print and mail checks, manual and paper processes of all kinds are under intense scrutiny.

The STP “Sweet Spot”

Generally, STP is most effective when four factors apply to a particular line of business or transaction:

  • Risks are well understood, which makes modeling easier
  • Data is easily accessible and generally reliable
  • Speed is at a premium to be competitive
  • Margins are thin, so productivity and throughput drive profitability

Figuring out where to enable STP isn’t always a question of looking at specific lines or products and determining whether these factors apply. Insurers can also use these principles to design new products, especially for direct distribution—for example, by defining the allowable risk profile for a particular product more narrowly so it’s limited to the cases that are most likely to be profitable.

The Future of STP

While the industry as a whole is trending toward greater automation, most insurance will never be completely straight through; there will always be some complex claims scenarios or unusual risks that will require human intervention and review. That itself is part of STP’s value, though: When technology handles the easy processes, humans have more capacity to focus on higher-value work.

See also: Insurance Outlook for 2021

Enabling STP has an upside for those human actors, as well. Investing in better data creates resources human underwriters can use, and better connectivity eases integration and improves ease of doing business for distribution partners.

Even just the process of implementing STP can have benefits. Creating the business rule framework or algorithm to adjudicate an application—or even figuring out if a particular process can be done straight through—requires insurers to examine their workflows, understand what really matters and justify what is done and why. That can lead to process and product improvements that wouldn’t have surfaced otherwise, as legacy mindsets can hide in all kinds of places.

For more on STP, please see Novarica’s recent report, Straight-Through Processing in Underwriting and Claims.

4 Connectivity Trends to Watch in 2021

A primary goal of insurtech is to simplify and automate the insurance lifecycle — reducing time-consuming manual tasks, improving the agent experience and addressing potential client risks. One of the best ways is to increase the free flow of information at all points in the distribution channel.

2020 was the catalyst for huge advances in connectivity, largely due to the shift to work-from-home. In 2021, four particular areas will experience accelerated growth:

Real automation of commercial submissions will arrive.

The process of quoting and binding commercial lines has a lot of catching up to do in terms of workflow efficiencies compared with personal lines. Commercial insurance continues to suffer from outdated manual data entry procedures and an abundance of unnecessary paperwork. One-to-one quoting with individual carriers is a labor-intensive process, and high-volume, low-revenue risks typically require the same amount of time and effort as larger, more profitable opportunities. 

But there’s good news on the horizon in 2021. This year, the industry can expect general availability of robust, comprehensive insurtech platforms that truly automate commercial submissions. These platforms speed up the process for both agents and clients by pulling end-insured information directly from the AMS and filling as much as 80% of most submission forms without a single click. In turn, these solutions deliver structured, more error-free data to the carrier for accurate, bindable quoting.

Commercial submission automation also allows agents to generate quotes from multiple carriers in near-real time. Consumers have long appreciated quote comparisons as a way to make more informed decisions. By bringing that capability to the commercial side, customers and agents can collaborate on coverage options and reach purchase decisions far more quickly.

Carriers will facilitate better data exchanges.

Look for carriers to expand their data sharing initiatives in 2021. With a more seamless connection among carrier, agency and insured, service will become more immediate, more personal and more competitive.

As connections go deeper into core business platforms, actionable insights grow. For example, if an end-insured makes a change in a policy (like adding a vehicle or a driver), an alert from the carrier could immediately be pushed to the AMS. This alert would not only offer the agent the opportunity to touch base with the customer, but it also eliminates the need for agents to reach out frequently to the carrier for updates.

Integration of third-party data will accelerate, as well, though the industry is in the early stages in the commercial space. The aim is for third-party data to facilitate collaboration across multiple activities such as identifying class codes and linking those to risk, streamlining the underwriting process and optimizing submission flows. The goal is to improve quoting speed and accuracy for commercial lines through third-party data integrations and, eventually, a single application programming interface (API), similar to what is already in place for personal lines.

See also: How COVID Alters Consumer Demands

Single sign-on will make carrier credentialing easier.

For independent agencies, usernames and passwords for carrier websites can be a major concern. The problem is evident when the number of employees is multiplied by the number of carriers; every username and password must be tracked and accounted for. 

When an employee leaves, credentials must be removed or changed — a time-consuming process that can also pose security risks if credentials are overlooked. Onboarding new employees requires provisioning dozens of credentials — also a time-consuming task. Over a year’s time, hundreds of hours can be wasted agency-wide simply due to carrier sign-ons.

Single sign-on (SSO) technology is beginning to solve the problem. SSO creates a single, secure agent identity that is acceptable to all carriers. Some AMS and rating systems already offer SSO, but, where the solution isn’t available in an existing platform, users can look to the non-profit organization ID Federation for an alternative. Expect to see SSO gain wider adoption in 2021 as it produces fewer username/password resets, reduces hassle for agents and increases operational efficiencies. 

We’ll see an improvement in straight-through processing.

Lastly, this year we expect the independent agency channel for both personal lines and commercial lines to see more functionality on straight-through processing with carrier partners. 

In other words, while we’ve been involved over the years with Rate Call 1 and Rate Call 2 (rating and quoting), some carriers are beginning to provide more functionality in their APIs to allow direct binding in the agency’s quoting applications. The benefit to the agency is a single workflow for rate-quote-bind. Carriers benefit from being seen as easy to do business with while providing a major competitive differentiator in the channel. This capability won’t be pervasive through the industry, but there appears to be more acceptance of the process from the carrier side than in the past. As a result, we hope we’ll see more carriers start to think about how they’re approaching this as a potential competitive advantage in the channel as well as the captive and direct markets.

The result: a better-connected insurance distribution channel.

SSO, automated commercial submissions, carrier data-sharing and better straight-through processing will be the most visible connectivity developments in 2021, but not the only ones. In a business defined by personal relationships, connecting well on a virtual basis will be more than a change — it will be a requirement for long-term success. And in a world where connectivity is constantly widening and deepening across industries, insurance workflows, both commercial and personal, have a major opportunity to benefit from modernization and in turn help carriers and agencies increase profitability and better serve their clients.

3 Techs to Personalize Claims Processing

Claims is a people business – virtually every claims executive I have ever met believes this. If you have ever been in a vehicle accident, experienced damage to your home or business, or been injured in a work-related incident, one of the first things that comes to mind is: I need to talk to someone who can assure me that I have insurance coverage and that there will be resources, both financial and technical, to make me whole again. This reaction is a human one and is not likely to go away. Many claims organizations have tried to maintain staffing levels to ensure a human connection is available to all. However, this is expensive, and claims organizations are already experiencing a shortage of individuals to fill critical claims roles.

Claims executives are at a crossroads, and many questions arise. How do we maintain 1:1 people interactions and simultaneously manage skills gaps and expenses? Then there are digital expectations from all parties to the claim – insureds, claimants, distributors, service providers – how are those expectations met? Given all these weighty challenges, many claims decision-makers relate to the phrase: “There’s a light at the end of the tunnel, and it’s a train coming the other way.” But, for many claims organizations, the reality is that the digital train that is coming can provide answers to the people challenges they face.

See also: How Work Culture Affects Claims Process  

SMA’s recent research report, Claims Transformation: New Paths Forward for Reporting,  Verification and Communications, explores emerging technologies and trends in claims operations. Relative to the people business theme, there are several areas of innovation where concerns, expectations and answers merge.

  • Self-reporting via photo and video. Apps that facilitate the insured or claimant in providing visual representation of damage will speed the claim along versus waiting for an adjustor or inspector to do the same thing. Faster settlement clearly meets consumer expectations. Additionally, precious claims resources are preserved for more complex claims.
  • Self-reported photos and videos along with AI analysis. The resulting outcomes from AI analysis can facilitate the next-generation of straight through processing (STP), ultimately going well past the current glass and towing claims STP, as things such as machine learning evolve over time. Again, shorter time to settlement with little or no claims adjustor involvement – a win-win.
  • Telemedicine and digital health platforms blend consumer-accessed, personalized information with a collaborative environment for adjustors, service providers, medical professionals and other concerned parties. These technologies blend useful, self-service information with human access at the moment of need.

These are just a few examples of the technologies that claims organizations have at their disposal to transform processes and operations. The previously mentioned SMA research report covers many other areas.

Make no mistake, balancing when to insert adjustors into processes and when technology can facilitate desired outcomes is not easy to accomplish. One of the key success factors is to look at claims processes from the outside in. This is not intuitive for claims organizations that have spent entire careers managing the challenges and intricacies of the adjustment process with an internal lens to meet corporate compliance goals and tangential department needs within a regulatory framework that can be daunting.  However, looking at claims processes from the consumer perspective – outside in – can suggest ways of execution that fulfill the need for the customer to be compensated for their loss in the fastest way possible or to find the clearest path to wellness. Happily, these outcomes also preserve human claims resources for when an individual really needs it.

See also: The Best Workers’ Comp Claims Teams  

The technology vs. human paradigm will continue to change, probably forever. However, claims is one of the areas within insurance where expert adjustor skills can truly make a meaningful difference for individual outcomes. But the definitions will continue to change, and the challenge for claims executives will be to continually assess processes through a different lens. Optimistically, the light in the tunnel will be a source of inspiration.

3 Ways to Optimize Predictive Analytics

A few years ago, simply applying predictive analytics to insurers’ underwriting practice was enough to gain a competitive edge against the large portion of the market that was still operating with traditional methods. That ship has sailed with increased adoption of analytics, raising the stakes for companies that once enjoyed a first mover advantage. Currently, 60% of insurers have welcomed predictive analytics into decision-making and underwriting processes, and research continues to show correlation between predictive analytics integration in the property & casualty industry and improvement to top and bottom lines. Companies that view analytics as a necessary commodity for modern underwriting instead of the centerpiece to their decision making will find themselves falling short of their competition. The biggest differences between the winners and losers in analytics today is equal parts ideological and technical.

In its recently published ROI study, Valen Analytics observed 20 insurance companies, representing $1.8 billion in premium, and compared their loss ratios and premium growth against the industry. The study showed that data-driven insurers consistently outperformed the market on both metrics.

  • Between 2012 and 2017, the industry saw its loss ratios improve by 18 points, whereas these 20 carriers averaged improvements that were nearly twice that (loss ratios improved by 35 points).
  • Between 2012 and 2017, industry-wide premium grew 18% on average, while the carriers studied grew by 53%.

For the first time since its inception, the ROI study isolated the impact of applied analytics on insurers with concerning loss ratios: those whose loss ratio were greater than 60%. This group of insurers saw loss ratios improve to market average within 12 months, and then outperform the market with each subsequent year. These results underscore the value of predictive analytics in insurance.

See also: 3-Step Approach to Big Data Analytics  

Below are three best practices that the insurers studied have implemented to draw the most value from their predictive analytics programs:

Empower underwriters

The considerably positive findings of Valen’s study do not imply that predictive analytics should replace traditional underwriters. Instead, research suggests that predictive analytics tools should aid traditional insurance writers. This year’s study found that underwriter performance improves 3x when they combine predictive analytics with expertise. A well-implemented analytics solution helps underwriters leverage powerful data that they wouldn’t be able to otherwise, and underwriters provide the expertise to make the final decision. In other words, an insurance underwriter’s wealth of knowledge and contextual expertise is a largely irreplaceable asset. Underwriters know the critical variances between the price suggested by the analytics model and the historical habits of a policyholder and can incorporate this information into their decisions. Thus, predictive analytics usage augments an underwriter’s decision-making process rather than supplements it.

Streamline the workflow

Predictive analytics enable insurers to accurately align price to risk exposure, helping underwriters price policies within the context of an insurer’s risk appetite, and oftentimes allowing insurers to implement straight-through-processing (STP) for specific types of risk. In doing so, insurers can eliminate the need for underwriters to be heavily involved in certain decisions and allow them to focus on the decisions that will have the greatest impact to a book of business. This, again, leverages the expertise of an underwriter.

Incorporate the right data

Insurers that have incorporated a consortium of anonymized data into their model-building initiatives tend to be better-positioned for growth. This additional information can be crucial to initiatives like expansion across states or business classes, often by identifying risks that might fall in a blind spot of institutional knowledge. In other cases, the incorporation of consortium data will eliminate sample bias in an existing book of business. For instance, an insurer that’s relied heavily on its expertise in knowing how to underwrite low-risk construction accounts in one state to build a data set that determines good risks in a new state will risk overfitting the model, essentially giving it too high a standard. This will leave an insurer vulnerable to underpricing risky accounts without third party data to balance the scales. Consortium data increases the predictive power of models and helped the group in our ROI study of analytically inclined insurers grow premium last year, even as the market declined.

See also: Global Trend Map No. 5: Analytics and AI  

For the third consecutive year, Valen’s ROI study has identified just how much value applied analytics can add to insurers. The carriers that have leveraged analytics and consortium data and empowered their underwriters have realized significant advantages over competitors to improve both profitability and growth.

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.