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Practical Uses of RPA for Insurance

In the fast-moving world of insurtech, new technologies such as robotic process automation (RPA), intelligent automation, artificial intelligence (AI) and machine learning are making it easy for insurers to dream about transforming processes. However, too often they get lost on how to put those innovative technologies into use. For a risk-averse industry, we’re seeing more insurers open to using these modern technologies to improve processes and ultimately better serve their insured – the way they expect to be.

Many insurers are building on proven and integral technology platforms, including content services and core insurance platforms, to include more modern solutions that will help them further streamline operations. By combining capture, workflow, integrations and RPA, insurers can take some of the tedious tasks out of their employees’ workload and automate those processes, leveraging a “digital worker” to replicate redundant and manual tasks.

For example, take a loss-run request process – which one of our customers completely transformed using capture, workflow and RPA.

Capture

Intake processes are often tedious because there are too many manual steps. Without standardization around the process, it is inefficient and doesn’t provide reliable metrics. To continue to move critical information forward, data needs to quickly and accurately get to the right people – where and when they need it. Many content services applications offer multiple ways to capture data and instantly digitize documents, including emails, PDFs and Office documents, and connect them to key processes. This ensures data is digital from the beginning and throughout the lifecycle. Once imported and classified, insurers can create a standard way to kick off processes, drive additional efficiencies, enable performance metrics to identify trends and better assess internal resources. For loss run requests, once the request is made – whether by email or through the insurer’s portal – integrations with the content services platform can capture the request to officially initiate the process.

See also: The 5 Top Trends in AI and RPA  

Workflow

A workflow automation tool is an excellent way to help keep processes digital by electronically routing information to the appropriate person at the right step in the process. Additionally, because information is electronic, it is easier to monitor the status of items by incorporating real-time notifications. Within the loss-run request process, employees can use electronic workflows to take captured information and run that data through the applicable channels to get the claims history reports needed to make an informed underwriting decision. After the insurer receives those claims history reports, they can analyze how many claims were made, what types of claims were made and the financial impact of those claims.

RPA

RPA, intelligent automation, AI and machine learning are making it easier to take advantage of digital workers to further streamline processes and achieve greater efficiency. For the loss-run request process, once information is digitally captured, indexed and put through a workflow queue, the workflow can tell the digital worker to take indexed keywords and run them through third-party websites to gather any hits for the loss-run history. Once those are available, the digital worker can open the reports, download them from the website and upload them into the content services platform. There, the workflow process is finalized and the requestor, or agent, can access the report and make a decision. It took a digital worker three to five minutes to complete each item, saving more than 20 hours per day on run-loss automation requests, according to a customer using a combination of content services and RPA technology.

The entire loss-run request process was simplified down to nine steps:

  1. Index data for transaction type, market and policy number in content services platform
  2. Navigate to market web portal based on market keyword
  3. Log into market portal
  4. Navigate to “Request Loss Run”
  5. Enter policy number and select submit
  6. Retrieve loss run report
  7. Save loss run report
  8. Import loss run report to content services platform
  9. Send report through final workflow steps

Building innovative solutions on proven technologies, like content services platforms, allows insurers to continue to evolve and modernize, as well as keep pace with the expectations of their clients. With any new solution, an organization needs to evaluate the best way to implement the technology into is business processes to ensure it helps them achieve greater efficiency and improve customer service. For RPA and intelligent automation it’s often easiest to incorporate and leverage these solutions in processes that:

  • Have a minimum number of steps
  • Are highly repetitive
  • Aren’t super complex
  • Have a quantifiable value

See also: 3 Ways RPA Enables Growth  

Understanding the ins and outs of each of your processes is the first essential step to know where new technologies like RPA, intelligent automation, AI and machine learning will best benefit. Once implemented, employees become more productive and can focus on higher-value tasks to deliver faster and better service to their prospects and customers.

What’s Beyond Robotic Process Automation

The insurance industry, which relies heavily on repeatable processes, is embracing robotic process automation (RPA). Gartner projects that global RPA software spending will reach $2.4 billion in 2022. But organizations need to understand that RPA is a primitive technology. And it represents only a small part of what’s needed to scale and enable straight-through processing.

What businesses need for end-to-end automation is an integrated automation platform (IAP).

RPA Is Very Basic – And Does Not Know How to Learn

Bots based on RPA can open spreadsheets and databases, copy data between programs, compare entries and perform other routine tasks, the Boston Consulting Group says.

But BCG adds that “RPA is a Band-Aid.” The firm explains that RPA can lead to a proliferation of spot fixes that threaten IT architecture and integrity.

BCG also notes that RPA bots don’t get smarter with time and experience. “When rules conflict with reality or when unexpected events occur,” the firm says, “a human needs to intervene.”

As a Result, RPA Greatly Limits What Organizations Can Automate

Insurance and risk automation companies currently use RPA as a data transport layer. That involves taking data from structured input sources and bringing that data to a target application by employing robot software.

This is a simple task that doesn’t involve any exception handling. But there’s far more to do.

Insurance companies and other organizations also need to analyze, contextualize, enrich, read and understand their data. However, insurance and risk processes are often complex and involve using data from various varied, unstructured formats and sources.

See also: Next Big Thing: Robotic Process Automation  

With unstructured data come multiple exceptions, which require cognitive ability and intelligence to bring out meaning and insights. This is where RPA fails.

RPA falls short because it is hard-coded and rules-driven. RPA is unable to scale and adapt to these more complex unstructured processes. When organizations need to use unstructured data – which has not been prepared or contextualized, or changes in target applications and sources – to power their automation efforts, RPA-based bots just don’t work.

That’s Why Now Is the Time for Insurance Organizations to Embrace IAP

To enjoy the benefits of straight-through processing, businesses need RPA, data availability and data usability. Yet RPA does not deliver these last two functions. Meanwhile, IAP does it all.

The data availability feature of an IAP solution ensures the data is made available and is accessible for automation. It includes technologies such as document classification and indexing, image pre-processing and machine vision for digitization.

Data usability – which an IAP solution also supports – makes sure the available data is ready for business processes. It prepares the data using business rules; data certainty; enrichment lookup; and natural language generation, modeling and processing.

IAPs Bring All the Automation Functions Businesses Need Together

Businesses can buy point solutions from separate vendors to address each of these functions. But working with multiple companies and systems needlessly creates complexity. It entails multiple contracts and integration efforts. And it leads to finger pointing when problems arise.

See also: How to Automate Your Automation  

That’s why insurance and risk management companies are looking to IAPs. They automate end-to-end business processes quickly, easily and in a scalable manner.

With IAPs, insurance companies can read and interpret data from unstructured documents – whether those documents are printed or handwritten, inferred or image data. Organizations using IAPs benefit from automation processes that grow smarter over time. And businesses that implement IAP solutions can leverage multiple technologies to drive data velocity to enable optimal business and customer outcomes faster.

5 Emerging Trends for Insurance in 2020

When it comes to implementing new technology, the insurance industry is rarely considered an early adopter. However, insurance companies have been taking early strides, somewhat in a migratory manner, to adapt to technology advances to help better run operations, improve underwriting and risk management, enhance customer offerings and services and profitably grow the business. Taking into account this early progress, we look to 2020 and several trends in the industry that have begun to take shape and will accelerate this coming year:

From RPA to IPA

Whether stemming from insurance carrier frustration that basic robotic process automation (RPA) — bots mimicking human tasks — hasn’t produced savings relative to carrier aspirations, or from insurance carriers’ increased understanding of machine learning (ML) and artificial intelligence (AI) capabilities, the industry will see an increase in “intelligent” process automation (IPA) that is more robust and combines the bot with learning, evaluative and decision-making capabilities for greater impact. This shift will be driven by carriers looking for higher business returns by solving a wider range of problems in the manual activity value chain with automation.

From Point Solutions to Digital Ecosystems

While today’s “exploration era” in insurance — characterized by new technology proofs of concept, use of point-solution providers and insurtech accelerators — has generated some progress and buzz, it comes with a down side. Single-solution or shiny software objects that address an individual problem or portion of the business will soon become too confusing and difficult to manage, actually creating a gridlock in carrier movement to true transformation. The fact is that no single solution can bring about transformation on its own and will instead require a sum-of-the-parts approach managed in a smart ecosystem. Similar to a conductor’s role in astutely incorporating the needed instrument — which in and of itself can only perform one thing, as a trumpet can only make trumpet noises — so too will orchestrated digital ecosystems begin to take priority as carriers look at enterprise platform solutions versus traditional bolt-on approaches.

See also: 3 Phases to Digital Transformation  

From Data Warehousing to Data-in-My-House

As an anonymous poet once said, “It is a great day when one discovers the beauty that lies within oneself.” So, too, will carriers be focused on unlocking the value of their own information that has accumulated over time. The focus on data infrastructure, lakes and warehouses now takes aim at using the very data that has been collected or can be mined — particularly the plethora of historical in-house data that has been generated by the carrier itself in the execution of risk evaluation, providing coverage, taking losses, servicing inquiries, etc. Content management systems and capabilities will start to transform into intelligent management systems with outputs infused into future-facing decisions and actions. Using AI and content mining capabilities to convert traditional in-house “flat” files — policy, risk and loss reports, correspondence, etc. – into usable insight, combined with the continued use of outside data and emerging sources (such as the Internet of Things), will enable carriers to take a significant step in becoming analytics-driven businesses.

From Digital Customer Experience to Digital Risk Management

While the term “digital” is used — and even overused — in a variety of contexts, many would agree that the digital movement was and is centered on digitizing the customer experience. Making things easy for the customer, creating experiences that will keep them coming back, and identifying customer service as a top priority are all common objectives in insurance, and a great deal of digital emphasis is placed on these initiatives. However, the heartbeat of an insurance company is effective risk management — and quite often, the most reluctant to join the digital parade are chief underwriting officers (CUOs), not because they’re grumpy progress-stompers, but because they want to ensure that good risks are put on the books and that underwriting disciplines and philosophies are upheld. Not enough digital ambitions have been focused on the CUO world, and that is where digital convincing needs to occur to bring them on board and excite them about digital. As a result, while digital customer experience will remain a priority, emphasis will broaden toward using digital technologies — be it AI, data analytics or risk assessment technologies — for a better underwriting result. Digitizing phases of the underwriting process to optimize underwriting time capacities and drive consistency of risk assessment and decision-making will be more in focus, adjacent to making customers happy.

From Call Centers to Intelligent Customer Interaction Centers

Customer servicing enabled by natural language processing, AI and voice assistants, such as Alexa or Siri, will become more common. This customer call automation, combined with web and email channel automation technology, will move carriers toward omnichannel customer interaction management that is driven by technology engines. This shift will be driven by carriers looking for efficiencies in workforce management, faster customer issue resolution and tracking of customer interaction data to improve products and services.

Given these other trends, carriers will be looking more to an outside perspective —outside of the insurance industry, outside of traditional insurance approaches and outside of traditional insurance vendors and suppliers. Insurance companies move as somewhat a pod, and, historically, the benchmarks of what constitutes progress and advancement has been focused on others in the pod. Over the next year, we’ll see a shift toward the new benchmark, which is now the broader world, other industries and the digital economy being built outside of insurance. This is the economy customers of insurance carriers are experiencing in their worlds — whether they are individual or commercial buyers of insurance — and their expectation of what insurance should be or should look like is shaped by these outside forces. As a result, insurance carriers will need to rely more and more on partners in 2020 who may not be traditional vendor insiders, but outsiders who have helped create digital ecosystems in other industries and enabled digitally born companies.

3 Technologies That Transform Insurance

The combination of artificial intelligence (AI), robotic processing automation and predictive data analytics is fundamentally redefining how businesses operate, how consumers engage with brands and, indeed, how we go about our daily lives. The field of insurance is no exception. Outlined here are three ways smart technology is affecting insurance, with a focus on identifying lessons learned and defining specific keys to success.

Back Office Robotic Process Automation

The impact of rules-based robotic process automation (RPA) on insurance operations has been well-documented. RPA tools are driving efficiency and productivity gains in generic back-office functions such as F&A and HR, and insurers are tackling processes related to claims administration and account management.

One key challenge is scalability. In many cases, concept initiatives have failed to gain traction, resulting in isolated pockets of automation that yield limited benefit. In others, overly ambitious enterprise-wide projects struggle with boil-the-ocean syndrome. A well-defined center of excellence (CoE) model that develops and documents best practices and then propagates them across different business units has proven effective.

Another critical lesson has been the importance of CIO involvement. This was lacking in many early RPA projects. For one thing, because RPA tools focus on process and business functions rather than programming skills, CIOs often weren’t interested. Business unit heads, moreover, feared that CIO involvement would lead to bureaucratic logjams and derail aggressive adoption schedules. Practice has shown, however, that CIO oversight is essential, to avoid both general shadow IT problems as well as specific interoperability, stability and security issues related to RPA functionality.

See also: Using Technology to Enhance Your Agency  

Leading early adopters have also continually pushed the envelope of automation levels. In a claims processing environment, 70% of claims may be simple and straightforward, and therefore ideally suited to an RPA application. At the other end of the spectrum, 5% to 10% of claims are complicated and unusual, and therefore require a human’s expertise and judgment to evaluate. While doable, automating these complex outlier claims isn’t cost-effective. The challenge then becomes to focus on the remaining 20% to 25% of claims. By analyzing the frequency of different types of claims, insurers can identify cases where the time and effort needed to configure a bot will yield a return.

Applying Cognitive Capabilities to RPA

RPA has delivered impressive benefits to insurance operations in terms of cost reduction, accuracy and auditability. That said, the tools are limited to the specific if/then rules they’re configured to follow. If a bot encounters a scenario that doesn’t align with what it’s been taught, it gets stuck.

More advanced cognitive systems apply pattern recognition to analyze unstructured data to identify key words and phrases in context. This promises to take insurance automation to the next level. While an RPA bot can extract a specific piece of data such as a policy number from a specific form, it can’t interpret underwriting rules or aberrations from a form on which data is unstructured and organized differently.

A cognitive application, meanwhile, can scan documents of various types and formats and apply machine logic and learning to identify relevant data in spite of discrepancies in how the data is structured or presented. This allows people to focus on policy/claim exceptions rather than formatting issues. More specifically, by injecting cognitive applications into operational workflows at key “intelligent gates,” insurers can more easily identify aberrations in unstructured data and highlight the policies and claims that require further human involvement.

IoT, AI and Insurance Underwriting

The combination of Internet of Things (IoT) and artificial intelligence will have perhaps the most transformational impact on insurance. By deploying networks of smart, connected IoT sensors, insurers can collect and analyze volumes of data at the point of critical business activity. Leveraging the pattern recognition and predictive analytics powers of AI, meanwhile, creates insights that insurers can use to refine actuarial tables and improve the rules of underwriting.

Consider these examples:

  • Sensors in vehicles ranging from commercial trucks to passenger cars monitor and document speed and driver behavior. Insurers can analyze data to calculate accident probabilities of safe vs. risky drivers over time. Based on those calculations, premiums could be adjusted. Smart sensors and cameras can also detect drowsy drivers or erratic behavior, triggering alarms.
  • Smart home technology that monitors suspicious activity and automatically shuts off water pumps in the event of a burst pipe can lead to lower homeowner policy costs, particularly for premium coverage such as insuring valuable artwork from theft and damage.
  • Pharmacies that store and transport medicines can deploy temperature and humidity monitors to ensure that supplies stay within required guidelines. Reducing the risk of tainted medicine reaching consumers could reduce liability risk.
  • Smart video analytics can determine wear and tear of roofs, oil pipe damage from foliage and animal migration and levels of water and soil contamination. Such insights enable corrective action before catastrophes strike and reduce the level of unforeseen risk for underwriters.
  • By monitoring pressure or fluid flow in an oil pipeline, sensors can trigger shut-off valves if limits are exceeded, thereby preventing costly environmental damage.

Innovative insurers are exploring how to deploy these capabilities into policy formulation. For instance, customers who adopt the technology could qualify for discounts. (Given the privacy issues surrounding driver monitoring, the voluntary aspect would seemingly be critical for auto insurance policies.) Another option: Insurers team up with technology partners to offer smart sensor services, thereby helping policyholders while creating revenue streams.

See also: Smart Home = Smart Insurer!  

The combination of sensor array and intelligent technology is refining underwriting and claims payment. Insurers can tune actuarial tables and pricing models to cover potential losses before they occur, as well as avoid incidents by advising policy owners to take corrective actions. In other circumstances, sensors can take action on their own.

Ultimately, these capabilities will enable insurers to fundamentally redefine their operational and customer engagement models.

3 Ways to Better Leverage AI

Insurance company executives are being pressured by board discussions, distribution channel partners and customer service requirements to more aggressively leverage the “shiny objects” that insurtech offers. Artificial intelligence (AI) is one of insurtech’s brightest contributions, and it seems natural for insurers to use advances in AI — including machine learning (ML), natural language processing (NLP) and robotic process automation (RPA) — to leapfrog competitors.

Unfortunately, not every insurer is ready for AI or able to take full advantage of the opportunities in this category of emerging technologies. There are, however, several ways insurers can prepare and evolve to a position of strength from which AI can make a strategic business impact.

1. Ditch Dirty Data

For a variety of reasons, insurers tend to have a good amount of “dirty” data, rife with inconsistent formats or standards, incomplete conversions resulting from merger and acquisition activity and data transfer from paper files. A proliferation of dirty data can put insurers in the untenable position of sacrificing whatever valuable intelligence may exist in historical files to a “Day Forward” strategy.

Insurers looking to prioritize AI projects must invest in cleansing bad data and improving data mastery. Those efforts will naturally include improving access to, and use of, both structured and unstructured data. The “magic” of AI gives the impression this technology is a silver bullet capable of maximizing the value of the unstructured data prevalent in handwritten forms, PDFs, images, email and text messages and social data, which increasingly inundate insurer workflows. However, the organization of clean and available data is a precursor to AI implementation.

See also: AI and Results-Driven Innovation  

A recent report by Eric Weisberg and Mitch Wein of Novarica, “MDM in Insurance: Expansion and Key Issues,” details the need for insurers to invest more heavily in improving data mastery and hiring for positions such as chief data officer or data scientist, instead of purely tech talent. “Insurers are placing a priority on data initiatives to support their predictive modeling and AI programs,” Weisberg and Wein wrote. “High data quality is imperative for digitization where data is being exposed to outside parties. Existing and emerging data regulations are also driving a need for improved data governance. Chief data officers and multi-tiered data governance organizations are becoming more prevalent as data is increasingly being treated as an asset. Challenges exist with organization, resourcing, process and funding that can stymie the results of well-intentioned data programs.”

2. Cultivate the Right Culture

The fast-evolving nature of technology often means insurers are in a fluid state of decision-making about deployment. As innovation further penetrates traditional industry settings and transforms basic processes and products, insurers must decide if the organization’s culture and leadership are truly capable of committing to the journey of transformation, let alone arriving at the destination.

New tools, such as AI solutions, will demand new skills of managers who have built careers leading and inspiring people, and who understand the importance of change management to the organization. So, sorting out the boardroom and operational priorities of the CFO and CIO, or the VP of IT and COO, can help ensure solid business cases and implementation strategy for innovation — such as AI initiatives.

3. Prioritize the Policyholder

In addition to cleansing dirty data and strengthening internal change management, preparing to better leverage AI should include a re-prioritization around the policyholder and the customer experience. Insurers need more customer-centric processes from the ground up and a reinvention of existing products and processes that treat the policy as an attribute of the customer instead of the other way around. Customer acquisition is notoriously expensive, and insurers face the additional challenge of relating an age-old industry and product to a new generation of consumers. To be successful, the gap between old and new, and between company and customer, must be narrowed substantially.

AI can aid such efforts through innovations such as natural language processing (NLP), which recognize information included in voice conversations or recordings and then quickly and accurately deliver relevant policy files or information. Chatbots can also improve the speed of customer service interactions, and ultimately the speed at which policyholder concerns are resolved. Claims service is good example of a process in which insurers are already starting to see the benefits of incorporating AI solutions, and are using this technology to do everything from reporting first notice of loss (FNOL) to initiating claim processing, or even deploying an adjuster, if necessary.

See also: Future of Claims: Automation, Empathy  

Defining Goals

As insurers prioritize spending on AI initiatives and implementations, the danger is ignoring persistent shortfalls in important areas — such as data mastery, operations and even underwriting. And, it is important to recognize that innovation implemented in the form of an AI solution alone is not, and never will be, a viable strategy. AI can be an enabler of a strategy. But without clearly defined goals and a flexible operating model capable of supporting an evolving and demanding policyholder portfolio, even a successful AI implementation can end up as no more than a footnote.

Process automation, machine learning and other types of AI initiatives will continue to make for compelling business cases. To realize full potential and benefits, those tools should be focused on winning clients and implementing accessible, 24/7 customer service and operationally optimizating to support competitive differentiation. Cost savings from AI will typically flow as a by-product. But, without leadership, champions who embrace and drive change and organizational data mastery, the AI tools will be underused and unlikely to fulfill the promise of growth and service excellence.