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Dark Data in Insurance | Accenture SHINING A LIGHT ON DARK DATA A new approach to data extraction, ingestion and analysis for the insurance industry A perspective from Financial Services Technology Advisory Automation is here to stay. Businesses are no longer just talking about automation or experimenting with automation; they are embedding automation as part of their long-term strategic objectives. It is estimated that spending on automation Once unlocked, Dark Data not only permits (artificial intelligence and cognitive computing) greater benefits from automation, it also feeds will more than double from today’s levels to and enriches the models and analytics that create $97.9 billion in 2023.1 For insurers, looking ahead sales opportunities, measure risk, and drive the big question is how to automate at scale. business decisions. This creates a secondary value stream that can differentiate a business in the One of the great benefits of automation is in data-driven financial services market—particularly helping companies realize Straight-Through in the information-intensive insurance sector. It Processing (STP). This is key to streamlining also creates the opportunity to reassign people end-to-end processes, such as claims processing, resources to higher value activities and initiatives by reducing human intervention. Historically, STP that support the insurer’s growth agenda. has been considered an unrealistic objective, with businesses opting for human-in-the-loop and In this paper, we will explain the challenges partial process automation. This is due primarily posed by Dark Data and present insurance- to the limiting factor of “Dark Data”: data that industry-specific solutions. Subsequent papers the business possesses but cannot access, like will look at solutions applicable to the banking photographs and handwritten documents. With and capital markets sectors. new approaches to data extraction, and by leveraging a suite of powerful technologies like artificial intelligence, we are now in a position to unlock Dark Data and allow businesses to pursue STP, realizing the power of automation. 2 SHINING A LIGHT ON DARK DATA STRAIGHT-THROUGH PROCESSING IN INSURANCE The benefits of intelligent automation have For example, leading segment software become particularly clear in the insurance provider UI Path, Inc., suggests targeting only industry. The availability of technology that 70 percent of a process for automation, stating can cut costs, reduce error rates, improve that it is usually necessary to “leave some customer experience and speed of service, steps of a process to human intervention.”2 support compliance, and reduce monotonous tasks for human operators—while avoiding While partial process automation can simplify the prohibitive cost of legacy system changes deployment, the manual interventions and or migrations—has made intelligent automation handoffs required introduce waste into the a “must-have” in the toolbox of any insurer. process (in terms of time, cost, and accuracy), eroding the business case for automation Traditionally, insurers have automated technology. Only when a process is automated individual, simple, stable processes with straight-through are the full benefits of the predictable inputs and predictable outputs technology realized. By automating entire to avoid the complexity of automating across processes end-to-end and providing a no-touch the lifecycle of an account, policy, or claim. or low-touch approach to operations, handoffs However, this approach has necessitated are eradicated, further diminishing the risk of the inclusion of partial process automation, error, reducing handling time, and, as there will no with handoffs between human operators longer be time spent ingesting or creating manual and robots through various mechanisms. handoffs, creating efficiencies in processing capacity and significant improvements in the customer experience. Figure 1. Straight-Through Processing Benefits in Claims Oa fficiency Pr Automa enefit ample Accenture’ xperience, non-cor C H F Adjuster 0%-6 6000 , F E FTE C Supervisor versight. 0% F A C H Super T s T A T A T A % 25 40 30 30 N oce 60 50 activities 75 16 40 60 70 70 40 50 FTE Actual STP Actual STP Actual STP reinve Le Le Le Pactices Pactices Pactices 0M USD NC Activities C Activities FTE S Accentur ea A NEW APPROACH TO DATA EXTRACTION, INGESTION AND ANALYSIS FOR THE INSURANCE INDUSTRY 3 THE DARK DATA CHALLENGE In insurance, automation clearly provides great efficiency gains when it is used to realize STP. I Oa s Yet many insurers (and vendors) shy from targeting no-touch automations. One of the primary roadblocks is the issue of “Dark Data.” Up to 75% of a corporation’s data may be Dark Data.3 20% 45% 66% Dark Data is data that an organization has but handwritten unstructured quality cannot make use of. This includes both data that is not currently accessible to human or automated operators, as well as data that is I r O s accessible, is used by humans, but cannot be directly interrogated by systems and automation technologies. All forms of Dark Data can present roadblocks to STP. 25% 69% 41% Consider the example of a photograph that handwritten unstructured quality shows the scene of a car accident, as well as capturing the license plate, color, and severity Source: Based upon Accenture’s experience and work of damage to the vehicle. There may be a street in this area sign in the image indicating the location of the accident, and the image may show whether it Dark Data in insurance can take many is day or night, and whether the road is wet or forms, including: dry. This information on its own could be enough for an insurance company to open a claim Structured Documents and produce an initial estimate of damages, determine whether to dispute the claim, and Structured forms are used in insurance assess the probability of fraud. However, because to gather information in a way that is easy for that data is locked inside a photograph and is people to digest and identify key information. not presented in a structured format, it is not This information is often presented in tables or captured by the business; the image is indexed boxes with single-word descriptors as the only for future reference and the data is left on the reference for what is contained in that field. shelf forever. 4 SHINING A LIGHT ON DARK DATA Unstructured Data Because critical elements of information about people, property, and policies are locked inside Unstructured data is everywhere in the data that cannot be readily accessed by bots insurance industry. Photographs, witness or systems, automation primarily takes place statements, legal documents, and medical downstream from where the data first touches records presented as part of an insurance the organization. Automation is implemented claim, file notes, or even transcripts of call only once a human operator has had the chance center conversations all constitute unstructured to touch the data and transform it into a structured data. This data generally presents itself as format. That time spent manipulating and written blocks of text with no clear indication interpreting data increases the risk of manual of where certain key information is held. error and erodes any benefit that the downstream automation can produce. • Handwritten Documents. Of the structured forms received, businesses can expect that Businesses have employed several methods between 20 and 25 percent will be handwritten. to work around the blockage caused by Dark Data, Handwriting has posed one of the greatest including the implementation of bots triggered by challenges to optical character recognition manually produced, templated emails, portals (OCR) technology for many years, with or applications for customers to fill in as their complexity driven by the fact that every person first point-of-contact, or complex and convoluted has their own style of handwriting. As a result, business rules to extract and route information. writing rules to differentiate one person’s “o” Each method has substantial downsides; from another person’s “0” has been impossible for example, manual triggers produce waste to date. and run the risk of potential errors, portals • Emails. Emails are one of the highest volume require a change in customer behavior that types of communication in every business, may be unwelcome and unwanted, and business used both internally for day-to-day process rules are often imperfect and inflexible, creating management, and externally for communications errors and more manual work to fix. with customers. The time spent manually reading, classifying, routing, recording, and Businesses often turn to human workers to unlock extracting information from emails is extensive. their Dark Data because there is no single tool or vendor in the marketplace that can solve for • Voice. The conversations that agents and all types of Dark Data. For example, many vendors call center operators have with customers offer OCR, but few can accurately extract text are important. Not only do they help build from low quality, unstructured, or handwritten a relationship, but they also involve the oral documents. Similarly, many offer transcription transmission of a significant amount of data. services, but few can offer highly accurate, fully Commonly, call center operators, claims automated, speaker-specific transcription and handlers, or other employees make short-hand associated call center analytics. And many offer notes in customer files denoting what has image recognition, but few can offer the flexibility been discussed on a call, but this high-level required to solve specific business problems. information only skims the surface of the Finding a tool that works well for a given problem data available during these calls. is hard; finding a tool that works well for every • Images. Images and videos represent the next problem has, up to now, been impossible. generation of largely untapped data sources. Due to the challenges presented by Dark Data, insurers have largely shied away from pursuing the generation and receipt of image-based data.
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