Across the insurance industry, claims organizations have made significant progress in modernizing their core processing systems in the last several years. Typically, the objectives of these programs are to increase speed, improve accuracy and reduce risks in all phases of claims handling. Given that claims interactions are “moments of truth” in customer relationships, insurers have good reason to ensure that the experience for policyholders is smooth and satisfying at every step of the process. No matter where insurers are on this continuum, robotic process automation (RPA) can help them achieve their business objectives while leveraging existing technology and boosting returns on previous and current transformation investments. In seeking the best path forward, claims leaders will want to consider:

• Why robotics is well-suited for use in claims and how it complements other enabling technologies
• Key components of the business case and value proposition
• High-priority opportunities and common use cases for deploying RPA
• Applying the principles and techniques used by successful early adopters as they develop their own implementation approach

Why RPA? Why now? RPA involves the use of virtual workers, or software robots, to perform business tasks similar to human users. The main appeal for insurers is the ability to handle high-volume and complex data actions at exponentially greater speed than in the past. RPA is also notably flexible, which makes it both business-enabling and IT-friendly. It can be deployed alone or with other technologies across the claims value chain. For example, robotics can:

• Automate discrete tasks or activities
• Work in concert with other systems on transaction processing, data manipulation, communication and response triggering
• Facilitate straight-through or “no-touch” processing, working alongside analytics tool sets and other cognitive technologies, such as machine learning and natural language processing

The cost of entry for RPA in terms of financial commitment and deployment requirements is low, compared with other technologies. There is no disruptive “rip and replace” with RPA; proofs of concepts are straightforward to launch, which helps IT and business leaders get past their “not another technology" reluctance. And many benefits can be unlocked without large-scale process re-engineering. See also: Insurtech Presents Major Opportunities More than just overhauling the most routine administrative tasks, robotics creates capacity and expands the art of the possible in claims. While many assume robots simply replace human resources, RPA can – and should – be viewed as an enabler and a win-win for insurers and their workers. RPA ROI: building the business case A significant number of insurers have already implemented robotics, though few have done so at scale. ROI cycles for RPA can usually be measured in months rather than years. Most early adopters start with multiple functional “pilots” or proofs of concept that are completed in as little as 30 to 60 days. Broader, first-generation programs may take six to 12 months. Increased capacity and focus on high-value work: Robotics can free knowledge workers from the burden of routine reporting, documentation and maintenance tasks. Instead, they can focus on areas where they can provide the most value, such as managing exceptions and dealing with high-risk and complex claims. A common approach is to use RPA to support straight-through processing for claims under a certain dollar threshold. RPA may also be used to handle basic data entry tasks for claims of any amount. Industry research has found that turnaround times for these types of claims may be reduced as much as 75%–85%, with 50%–70% of repetitive tasks effectively eliminated. Higher quality and accuracy: Robots processing claims will no doubt be able to increase accuracy and reduce errors, whether related to sophisticated fraud or simple “fat-fingering,” for the vast majority of routine claims. Indeed, robots are uniquely qualified to assist quality assurance (QA) staff, given their ability to scan large quantities of data and transactions almost instantaneously. For example, RPA can help identify potentially fraudulent claims by flagging data outliers. Further, in the realm of compliance, RPA helps strengthen and streamline adherence to standard audit, risk, privacy and security policies and protocols. Increased scalability: RPA is a natural solution for insurers that need to add temporary capacity to deal with seasonal spikes in claims activity or after catastrophes. The virtual workforce can scale to peak loads without overtime and establish 24/7 processing. For example, RPA enables insurers to increase the amount of new loss intake capabilities without a corresponding increase in first notification of loss (FNOL) processing staff. The easy scalability also makes RPA a highly useful tool for insurers exploring shared services models for claims. Higher customer satisfaction: In identifying processes that can be automated, leaders should also look for opportunities to enrich the customer experience. Speed, accuracy, transparency and level of service are what matters most to claimants. RPA helps on all those fronts by allowing claims professionals to focus on the “art” of claims adjusting and customer experience, as opposed to the transactional aspects. RPA can also accelerate innovation programs in customer engagement and experience. Business rules can be configured directly into the robotics to align with customer expectations for personalization and timely communications. Strategic data usage: The quality gains and capacity improvements from RPA enable claims teams to shift from simply processing data to exploiting it for more accurate and timely reporting and insight generation. In this sense, RPA can actually be an empowering force, rather than a discouraging threat, to a claims workforce. RPA in action: where to start the journey The use of robots and automation can take many forms in claims, including both customer-facing and back-office functions and tasks. The following represent the most common and promising use cases across the industry:

1. 1. Streamlining vendor applications and estimating: Most current estimating processes require adjusters or others to rekey data from one form or system to another. Robotics along with enabling technology such as optical character recognition (OCR) can eliminate that duplicate effort by bridging the gap between claims systems, vendor apps and third-party estimating systems.
   2. Capturing and managing claimant data: RPA can be on the receiving end of claims submissions, especially those that typically include photos from customers. Robots can ensure the right information ends up in the right systems and attached to the right claims. As such, they ensure human representatives have the information they need to move claims forward and respond to customer inquiries. Customers who prefer self-service also benefit when submitted information is more readily accessible.
   3. Streamlining, automating and enhancing communications: Claimant communication remains a largely manual undertaking, requiring adjusters or other claims staff to initiate and, in some cases, monitor the process. RPA can help operationalize smart rules so the right letter (e.g., one required to be sent 30 days after a loss is reported) reaches the right claimant at the right time through the right channel. For instance, robots can pull data from claims submission forms and pre-populate letters that are typically housed in other systems and map distribution to customer preferences.
   4. Scanning, indexing and converting forms and data: RPA has proven especially proficient at pulling data from standard fields on medical bills, from claimant name and address, to provide information to coding details. Standard in name only, these forms are a common source of errors. Similarly, RPA can transfer and convert data across older claims systems that may be used by individual product lines or regions to newer enterprise systems.
   5. Validating payments: Conventional wisdom holds that 3-5% of claims payments are inaccurate, though no one knows for sure, given the difficulty and expense in auditing all claims. The key is robots’ ability to quickly and cost-effectively run QA on entire populations of forms and payments, rather than just a small sample. For example, rather than auditors discovering a $5,000 payment on a $500 settlement months after a customer has cashed the check, robots can flag the disparity beforehand. Further, they can help deliver the information and intelligence so that human analysts can investigate anomalies proactively.
   6. Customer-facing enhancements: RPA can alleviate the need for time-consuming and costly adjuster input by supporting customer-friendly apps for capturing photos of fender-bender car accidents and submitting all claims submission forms with just a few taps and swipes. Chatbots, another automation tool easily integrated with RPA, are already handling many routine communications tasks, including notifications of settlements and customer inquiries into claim status.
   7. Integrating other enabling technologies: RPA will become more prevalent, especially as claims groups adopt other enabling technologies. For instance, AI-powered bots will likely handle the inputs from drones conducting standard property inspections or surveying damage after catastrophic storms. Integrating RPA with machine learning and natural language processing (NLP) can enable the initiation of new claims and issue first notice of loss (FNOL) communications by scanning and analyzing unstructured communications, including emails from agents or even voice interactions. Robots will also be used widely in the real-time review of social media streams to assess claims severity and reduce fraud. RPA will receive and route advanced telematics data (including video imagery) that will be instantaneously captured during automobile accidents and downloaded from the cloud, automatically triggering an FNOL entry.

Suggested approach and lessons learned: following the leaders Significant numbers of insurers are already using RPA in their claims organizations. In designing the business case for robotics, claims leaders should seek an incremental approach, adopting more ambitious use cases once they have built momentum and demonstrated results through initial and targeted deployments. With RPA, there’s no need to try do too much too fast, which may be attractive for insurance executives seeking to minimize risk and disruption in their adoption of enabling technologies. Further, an incremental approach can help organizations overcome their natural wariness toward RPA in terms of its workforce impacts. See also: Robots and AI—It’s Just the Beginning   The following lessons learned come from early adopters: Target the opportunities: In developing a business case and tangible ROI model, specific tactical questions can lead to the right strategy as well as clarify the highest priorities for near-term automation. Finding answers may require a robust assessment of current capabilities and the completion of a cost-benefit analysis, given that the candidates for automation may number into the dozens. Engage IT early and often: To ensure a smooth implementation and integration with other systems, there are many important infrastructure, governance and security questions to address. IT leaders reluctant to deploy another technology in the claims “stack” should consider how RPA can support strategic platform upgrades and those mandated by regulatory change. Most RPA tools are product- and platform-agnostic and work with existing IT architecture. Find the right partner: External vendors and suppliers – including insurtechs, consultants and systems integrators – will be part of the solution, so it’s important to choose wisely. Beyond technical expertise, look for those firms with deep technical and operational claims knowledge, including a clear understanding of how it affects the customer experience. Don’t overlook the organizational factors: As with other “digital” initiatives, claims leaders must invest time and resources in education and, if necessary, evangelization regarding the use of RPA. The delicate matter of robots taking over jobs should be addressed, most likely in the context of the need to reskill claims workers, as the role will evolve to become more analytical and more focused on customer needs and the most complex claims. The bottom line: RPA is critical to the evolving claims process The time for adopting robotics in claims has come, due primarily to the compelling business case and imperative for claims leaders to enhance performance and contribute more value to the business. Robotics can serve as a foundation in supporting true, end-to-end automation when integrated with other advanced technologies, such as OCR, chatbots, machine learning and NLP. Indeed, as multiple early adopters have made clear, RPA is ready to help claims organizations advance and enhance outcomes in the digital era through increased automation, higher productivity and increased capacity and strategic focus for claims professionals. RPA is among the top enabling technologies insurers should consider adopting in claims, as well as other parts of the organization, due to:

• Low cost
• The path to ROI
• Manageable deployment requirements
• Flexible use cases

For the full report on which this article is based, click here.

From better risk visibility and faster claims processing to collectively fighting fraud, blockchain can provide comprehensive benefits across the insurance value chain. Blockchain implementation can enormously accelerate insurance transformation and steer the industry toward digital collaboration and interoperability. Permissioned blockchains deployed in insurance consortia yield comprehensive industry benefits across the value chain in three categories: (1) preventing fraud, (2) championing interoperability in multi-party processes and (3) facilitating consumer trust and ease of auditing through data transparency and immutability. Introduction Insurance is a multitrillion-dollar industry, but the workflow in brokering trust, insuring parties and reinsuring risk items today remains an expensive, slow and fraud-prone process. Although the digital age has inevitably brought about technological innovations, the centuries-old insurance industry seems to still be heavily drowning in paperwork and redundant manual procedures. Layered with the required collaboration from a multitude of parties needed to execute certain industry tasks like enforcing policies, processing claims, underwriting contract items or drawing up contracts, the insurance process remains far from transparent, coordinated or secure. Each new party engaged in a particular insurance transaction — be it insurer, reinsurer, broker, consumer or vendor — adds a compounding set of paperwork and potential for fraud, cyber attack, lost data, misinterpretation and human error. Challenges arise in verifying this data without breaching trust, so auditing is a widely used process to ensure consistency and accuracy. But even still, trust is at an all-time low, according to a recent Edelman industry poll. The current insurance industry landscape in a snapshot:

• The insurance industry is widely known to be slow in adopting technology and is behind digitally.
• Legacy systems have perpetuated a closed-off insurance information environment with data silos and resulting operational inefficiencies. These gaps of knowledge between insurance stakeholders are exploitable.
• In terms of fraud and fraud prevention spending, the numbers are unfortunately astronomical. In addition, human error also finds its place wherever manual entry and paperwork is involved.

The insurance industry epitomizes a blockchain use case. Adoption of blockchain as a standard system of industry transaction can improve collaboration between market participants and streamline market operations — freeing billions of dollars in capital otherwise spent on auditing and administrative costs, lost in fraud or frozen in collateral as a result of low risk visibility. A blockchain is a permanent and immutable ledger of transactional records distributed across a network of participants in a decentralized manner. This network can be unknown and completely decentralized (i.e. bitcoin), or known with permissioned access (consortium). Blockchain’s system of hashing a new transaction by cryptographically tying its metadata to previous transactions gives the ledger its immutable nature — where the entire history of transaction is transparent, available and indelible. Blockchain’s mechanism of arriving at consensus with no central authority allows for the decentralization of data — where no central party can control or manipulate information. This is attractive to many applications that interact with sensitive data; because there is no central authority, DDoS (distributed denial of service) attacks are futile. Blockchain is typically well-suited for environments where transactional records must be time-stamped, immutable, trust-worthy, shared and readily available. These characteristics lead blockchain to be very desirable across the industry spectrum as:

• A trusted repository of accurate, transparent and updated data with comprehensive read/write access controls
• An effective measure against fraud, data manipulation and human data input error
• A champion of interoperability between data systems, thus an enabler of more efficient collaborative processes
• A facilitator of trust between parties that may have competing interests, different incentives or separate data compliance standards; a mechanism for cross-boundary and cross-industry collaboration on workflows; an eliminator of the need for intermediaries as a trusted central authority
• An efficient provider of quick and accurate auditing

Within the context of insurance, these features not found in traditional databases have great potential to effectively empower operational efficiency, trusted collaboration, transparency and fraud prevention. As a result, blockchain can help insurers and other insurance stakeholders reduce overhead spending, decrease margins and regain consumer trust. Blockchain can drive the insurance industry shift toward digitizing industry processes, encouraging cross-industry collaboration for visibility and compliance and collectively fighting fraud. Paired with additional emerging technologies such as IoT and smart sensors, blockchain can be a facilitator for increased automation in capturing and acting on claims data, analyzing risk more thoroughly and streamlining payment processing. Let's dive into some areas of impact: Reinsurance & underwriting Streamline Reinsurance and Underwriting Times In reinsurance, each risk in a contract requires individual underwriting — and in many cases, insurers engage with multiple reinsurance parties to secure the best negotiation for each contract item. Each institution has its own data system and standards — and these differences in process can lead to discrepancies in interpretation of the contract. Thus, currently, reinsurance and insurance institutions need to constantly engage in reconciling their books to ensure consistency in interpretation for each individual claim. In sum, the complexity of different data systems and consequent wrangling between multiple third parties to secure individual risk reinsurance leaves the reinsurance process slow, expensive and subject to misinterpretation. Blockchain technology should be leveraged in the reinsurance process to increase interoperability. With a shared digital ledger, no longer can there be the discrepancy in data format, process and standards that currently plague the industry. A permissioned blockchain ledger can be used to streamline communication, flow of information and data sharing between insurers and reinsurers as an available and trusted repository of contract information. This becomes a faster, more efficient and less-risky process as data related to loss records, asset ownership or transaction histories is recorded on a blockchain that is trusted to be authentic and up-to-date. Access to this information can be heavily permissioned with granular access controls, with exhaustive rules governing read and write capabilities per user. Reinsurers can query a blockchain to retrieve updated, real-time and trusted information rather than rely on a centralized insurance institution to report on data relevant to items (i.e. losses or transfer of ownership). This can massively expedite underwriting times. The risk transfer process is delicate: Insurers need to ensure they are appropriately rebalancing capital exposures against specific risks and be confident and calculated in offloading their contracts. The newfound visibility from participating in a permissioned blockchain ledger provides confidence and flexibility in moving capital to other areas of business, as well as a more accurate and expedited risk assessment. If blockchain is leveraged to provide more visibility into risk information, reinsurers can more accurately and confidently take on the calculated risk. Fraud Detection & Prevention The total cost of insurance fraud (non-health insurance) is estimated to be more than $40 billion per year. That means insurance fraud costs the average U.S. family between $400 and $700 per year in the form of increased premiums. The lack of interoperability within the insurance industry doesn’t just kill efficiency — it also hinders progress toward the digital collaboration required to identify patterns, trends and known actors in preventing fraud. These gaps in visibility leave consistent vulnerabilities for fraudulent activity, where brokers can pocket premiums, individuals can make multiple claims for the same loss or capital can illegally move offshore. The centralization of data within the four walls of each institution leaves little room for the industry to collectively fight these common insurance crimes. Blockchain implementation could support this needed coordination, while also providing granular access controls to ensure data security. As an immutable ledger decentralized among all parties in the insurance process, blockchain closes the paperwork gaps and bridges the data silos, allowing for fewer potential areas for exploitation. Blockchain within a consortium of insurance entities could facilitate the sharing of fraudulent claims for heightened visibility into known actors and for better preparedness. Blockchain provides validation and verification on an unalterable ledger, which can be leveraged for the identification of duplicate transactions, repeat actions by suspicious parties, fraudulent movement of funds across borders and more. Pairing this technology with machine learning would make an excellent fraud detection strategy. Blockchain can also be used as a ledger to track ownership of assets through digital certificates, and then be queried to validate their authenticity, ownership and provenance. This can reduce counterfeiting while also improving the efficiency of the entire claims management process. As a shared, transparent and decentralized ledger, blockchain will inevitably discourage future attempts at fraud, as the opportunity for exploitation is smaller and the potential for detection greater. Less fraud = higher margins = cheaper premiums for consumers. A win-win situation, indeed. Claims processing Improve Claims Processing for Property and Casualty Insurance Processing a claim in today’s insurance environment is a complex, multi-party task. To evaluate and process an insurance claim, insurers, regulators and third parties (like a private healthcare institution or an auto repair shop) need to coordinate and arrive at consensus across a host of data points. For example, a car accident between two drivers necessitates a loss assessment that assembles information from an asset database, weather statistics, credit reports, inspections providers, authorities report and other sources. Each driver’s insurance company likely collects and analyzes this data in an entirely different system and process. Because each entity has its own data standards and processing technique, the claims process typically involves significant manual data re-entry and duplication across the value chain. This not only increases needless redundancies and inefficiency, but also widens the opportunity for human error and even fraud. A distributed ledger can be used by insurers and third parties to digitally access and update data relevant to claims for a faster, more secure and less error-prone claims management process. Blockchain facilitates the interoperability needed for this level of collaboration without the associated risk of DDos attacks or falsified transactions. This level of visibility is not only advantageous for institutional efficiency and accuracy but also helps consumers firmly trust in the fairness of the claims process. Paired with streaming data sources, such as sensors, mobile phones or IoT technologies, blockchain can also help significantly streamline a claims submission, reduce settlement time and reduce loss adjuster costs. Adding to the auto wreck example above, an IoT sensor in one of the cars involved could automatically initiate a claim with the necessary reference data. A smart contract could automate coverage confirmation and consequent settlement payouts with programmable code — with essentially no human intervention along the entire payment process. While digital contracts like this exist already, the benefits of a blockchain-power smart contract lies in its transparency and credibility. Auditing & Trust Immutability for Efficient Auditing; Trust Auditors evaluate scores of ledgers — both online and offline — to reconcile reports and data spanning multiple locations and years. Needless to say, the process to ensure consistency and reliability in transactions and generate a compliance certification is lengthy and complicated. Digital signatures, sequences of events and actors of a particular transaction can be easily and efficiently audited if those events were to be recorded on a blockchain ledger. Institutions need to simply add access for an auditing party to their relevant permissioned blockchains. Blockchain immutability and finality guarantees the integrity of the entire transaction history, all in one place. Companies like Docsmore have announced pilot programs for recorded signatures on a blockchain. Identity Management Increase security and share-ability of identity information With recent, massive data breaches from some of the largest institutions over the past few years, improving the security of personal data — and thus customer trust — should be the forefront initiative for insurance institutions. Manual data entry — often repeated — should be replaced with a better, decentralized system with no single point of failure Blockchain is a perfect tool for sharing identity information while ensuring the privacy of consumers. Specifically, KYC (Know your Customer) and AML (Anti-Money Laundering) laws require institutions onboarding new clients to go through expensive and comprehensive steps to ensure compliance with these laws. This is traditionally accomplished internally, with multiple ledgers resulting in multiple certified identity versions across the entire insurance network. However, blockchain technology could provide a secure, distributed ledger for network participants to engage in cross-institutional client verification for KYC/AML compliance. In addition, a simple query of the blockchain can reproduce an immutable history of identity data, making regular compliance checkups and monitoring for changes an easy and inexpensive process. Query permissions could be set in place to ensure that consumer privacy is protected and that access to information is appropriately handled. The distributed nature of the blockchain ledger is also attractive for storing sensitive data — like identify information — because it limits the viability of DDos attacks. This standardization in identity management would require collaboration from not only the insurance industry, but also governments, tax authorities, bureaus, banks and other financial corporations. However, the savings for all would be well worth the coordination. Asset Management Tracking assets along a supply chain As demonstrated comprehensively in our previous blog post, insurance fraud can be prevented when assets along a supply chain are verifiably tracked with blockchain finality. Auditing becomes a breeze, and risk provenance can be proven for better estimates, faster claims processing and a reduction in fraudulent underwriting. See also: Blockchain: the Next Big Wave?   Where FlureeDB fits in As an enabler of consortium blockchains, FlureeDB can provide a single source of truth for harmonized insurance data to be stored, queried and transacted with blockchain characteristics. Data-Centric —Most blockchains operate on the “business logic” tier, where enterprises still need to push data and metadata related to blockchain transactions to a static, centralized legacy system. FlureeDB brings blockchain to the data tier — allowing for an entire database to be distributed across its network. Network participants can query at will and know they have the full data set.

Modern Database Characteristics for Enterprises —FlureeDB is first and foremost a powerful database with familiar, SQL-like syntax. Any development team would be able to set up a blockchain database without having to learn a complex set of new skills. With modern database characteristics like ACID compliance, a RESTful API and a graph-style query structure, FlureeDB is optimized to meet traditional enterprise requirements.

Granular Permission Logic for Access Control —Because insurance information is stored in a decentralized manner as one record, granular and highly functional access/permission models are essential to protecting data security. FlureeDB uniquely builds permission information (both read and write) directly into application data at the most granular of levels. This simple and flexible approach to data accessibility lends itself perfectly to blockchain environments — where a distributed ledger is shared across third parties in a network. Companies using FlureeDB can even hand a customer or vendor a direct line of access to the database without needing to use multiple API endpoints — queries only return the information for which a particular user has explicit read access. Blockchain Immutability —FlureeDB builds every transaction into a block within an immutable, append-only blockchain. This allows for massive auditing savings. Holding a complete and indelible history of transactions also enables institutions to throw highly advanced analytical queries to return increased visibility into practices like fraud prevention measures, internal compliance validation checks or risk assessments. Time Travel —“Time travel” is enabled by the blockchain’s immutable history: Queries can be issued at any point in time, empowering an application to reproduce any instance of the database with no extra development effort. This capability strongly reduces waste in development time and allows for apps to “rewind” to any database state with ease. Composite Consensus —With varying relationships and diverse data, insurers need to partition information to be read by only the appropriate parties. FlureeDB allows data to be segmented onto multiple databases — both publicly and privately held — but join together to query as one set from an application point of view. This means a singular application dealing with insurers, reinsurers, third parties and consumers can keep private information out-of-sight, but still leverage blockchain without having to figure out multiple integrations. Conclusion Blockchain technology, its believers, its vendors and its growth in adoption won’t wipe out the $40 billion-plus fraud, nor will it “fix” the insurance industry in one fell swoop. Such silver bullet claims are overzealous. But blockchain does pose unique characteristics that should be included in the discussion for industry transformation. Blockchain — simply in its very existence — won’t disrupt anything unless it is leveraged by and collaborated on within the insurance industry and with its secondary players and its technological partners. Brokers shouldn’t be paralyzed by blockchain’s potential to disintermediate their industry, but should rather embrace and harness its value to drive costs down and remain competitive. The few entities that take the bold step forward to early adoption will be rewarded with consumer trust, lower margins and larger market share. Now is the time for industry leaders to drive a sweeping transformational agenda with digital collaboration as the key theme and blockchain as the key mechanism.

You know you have a great product or service. And you may have lots of facts and figures and benefits to back up why you're the best. But just throwing data at potential customers (even if it's truly impressive data) won't move them to buy. People don't respond to logic. They respond to emotion. That's why you'd better get good at storytelling—fast. Stories create emotion, and emotion is what people remember. Stories help you engage and, more importantly, teach your audience. If you don't tell a good story, your message will be lost in the media jungle. Google processes more than 3.8 million searches per minute. That's a lot of people looking for answers. This is happening because the way people buy has changed. People no longer respond to outbound tactics like spamming and cold calling. Instead, they research products and services and find what they're looking for on their own. The message for companies is clear: You must provide lots and lots of content that's engaging and persuasive enough to pull in readers and win their business. This is called inbound marketing, and it's the way businesses today "get found"—by helping, educating and entertaining prospects with valuable, relevant and consistent content. Content pulls customers through the four stages of HubSpot's Inbound Marketing Methodology: Attract, Convert, Close and Delight. In other words, you create and share content—through blog posts, emails, videos, case studies, guides, etc.—that attracts the right people to your site, converts them into leads, helps close them into customers and delights them so they'll become promoters of your brand. Your goal is to make a human connection, and storytelling is how you do this. It's about resonating with people who need your help and guidance. A well-crafted story helps you create contrast between choices. It helps prospects make sense of the decision they're about to make, whether it's deciding on a product or service or making a purchase. See also: To Shape the Future, Write Its History   Here are some tips for discovering the story you want to share with the world. First, know what your story is not. It's not data and assertions about ROI. It's not just your business's history. It's also not cliché, and it's not what everyone else is saying. Sure, you may think you provide the best customer service in your industry, but that's not your story. Storytelling is about standing out, not blending in. Focus on your why.  Ex-advertising executive and author Simon Sinek is known for his Golden Circle concept. The Golden Circle is all about starting with why. Sinek says most people communicate by starting with what they do and eventually work their way back to talk about how and why they do what they do. But unique and successful companies like Apple or Google communicate with an "inside-out" type of thinking. They start with the why and only then do they talk about the how and what portions of what they do. Know your characters.  All stories have characters. With content marketing, the people—or characters—are your readers. Good storytelling can't happen without valuing and understanding your audience and responding to their wants and needs. When potential customers can get the answers to their questions and see themselves as characters in your story, they'll be more likely to use your product or service and experience the happy ending you offer. Choose your point of view.  While keeping your buyer persona in mind, you should also determine the point of view your story will have. Will it be first person, second person or third person? There's no right or wrong option. It will depend on your buyer persona, the story you're trying to tell and the format of the story. In the first-person point of view, the character is you. When you say, "I saw this," or, "I learned that," you're speaking in the first person. This type of language is more confessional. It can help you establish a personal connection with the reader or build authority. Try using first person when there's a known person, an author, behind the content. This could work for a blog post, video or even an e-book if the author is noted. In the second-person point of view, the character is your audience. It's when you say things like, "You'll see," or, "You'll learn." When using "you" language, it's important to understand your buyer personas and know their pain points and goals. Tell the story in a way that shows empathy. The third person is the "he said/she said" type of language. Case studies about your customers are a good example of using the third-person point of view. These stories can be fictional or nonfictional. Present, and resolve, your conflict. Once you know who the characters are for your story, it's important to understand the conflict they face. If your story lacks conflict, you're probably not telling a story. Instead, you're telling a pitch, a tagline, a unique selling point or a plain statement. This approach won't resonate with your audience, and from a content marketing perspective it won't get you views, shares, conversions or customers. You need to understand the buyer's journey and the conflicts they might face at each stage. What problems are your buyer personas facing in the awareness stage? Those are the conflicts that should be in your story. Wistia is a good example. It is a brand that provides professional video hosting. Its purpose is to empower everybody to get more out of video, and all of its content and storytelling—which is done through funny, engaging educational videos along with blog posts, guides, help articles and webinars—circles back to this purpose. One blog post is titled "Improve Your Audio: How to Reduce Echo in Your Video." In this case, the reader's battle with echo is the conflict, and it's stated right there in the headline. The rest of the blog post explains how to resolve the conflict. Finally, get to the resolution.  Where there's conflict, your audience will naturally want some sort of resolution. It should wrap up the story but should also clearly call your audience to action. It should fulfill the story's purpose. For content marketing, a resolution could be next steps or even a call to action for more content. Either way, don't leave the audience hanging. Find a way to connect to your audience on an emotional level.  TOMS is a slip-on shoe company that focuses on spreading social good. Here is its powerful story: Everyone needs shoes, but not everyone has the money to pay for them. So, with each product you purchase, TOMS will donate a pair of shoes to a child in need. This strikes an emotional chord with their audience and compels them to buy. This is an example of how a shoe retailer created a much bigger story that makes their customers feel like they're changing the world by simply purchasing a pair of shoes. And they've sold more than 75 million pairs of shoes, which means they've also given over 75 million pairs of shoes to children in need. Find a way to infuse your story into every piece of content you create. Storytelling is the perfect way to help readers begin the journey from stranger to customer, and it can deepen your relationship with your existing clients. Remember, people want and need to feel connected. If you tell the right story, you can capture their attention, connect with them emotionally, and win their loyalty. Justin Champion is the author of "Inbound Content: A Step-by-Step Guide to Doing Content Marketing the Inbound Way," which can be ordered here. 

The 2018 hurricane season opened with the arrival of subtropical storm Alberto on the coast of Florida. Natural disasters such as these regularly imperil human lives and trillions of dollars of infrastructure. Although we can’t stop them, we can limit their financial repercussions through the development of more accurate predictions based on an updated approach to modeling catastrophic risk. The Flawed Assumption Stationarity is the name for the concept of data remaining unchanged—or stationary—over time. When applied to climate science, it refers to the assumption that the earth’s climate is not changing. The vast majority of climate scientists believe the stationarity assumption is incorrect, and any approaches based on this assumption are fundamentally flawed. Yet traditional catastrophic climate risk models are built on the assumption of stationarity. They project the future based on past statistics and the assumption of a static climate. Insurers actually use this approach with reasonable success for regional, national and international insurance policy portfolios. However, when stationarity is applied to risk analyses for specific structures or large commercial properties, the model breaks down. Localized Assets The problem is that risks to localized assets are not homogeneous across regions and properties. Localized predictions require data that accounts for the dynamics of the local environment. Those dynamics include not only a changing climate but human-engineered alterations, as well. Simply breaking ground for a new building affects potential flooding scenarios. To accurately assess and mitigate potential risk, developers, municipalities and insurance companies need models for the individual block and street and are not constrained by stationarity. Creating a dynamic model that collects and analyzes data with such localized resolution is not a simple matter of “downscaling” old methods. It requires a different strategy and discipline, with single-site analysis as a core objective. See also: Role of Big Data in Fighting Climate Risk   Risk Modeling Reimagined Incorporating natural and human-architected factors in a dynamic, integrated model is fundamental to an asset-focused solution that delivers accurate, actionable information. Such a solution requires comprehensive and current data, powerful big data analytics and a flexible design that can easily incorporate new modeling techniques as they become available. At Jupiter Intelligence, our solution is built on a cloud-based platform designed specifically for the rigors of climate analysis and links data, probabilistic and scenario-based models and advanced validation. ClimateScore runs multiple models based on a changing climate, such as weather research and forecasting. ClimateScore’s models are continuously fine-tuned using petabytes of constantly refreshed data from millions of ground-based and orbital sensors. Novel machine learning techniques reduce local biases of scientific simulations and help the system continually improve as new observations become available. Forgoing stationarity and adding the flexibility of a cloud model, current data from multiple sources and state-of-the-art analytics, machine learning and artificial intelligence technology produces asset-level predictions that are accurate from two hours to 50 years in the future. Case Study: Miami Understanding how developed Miami’s coast has become with localized data down to the individual block and street can help insurance companies, municipalities and developers assess the potential risk and determine cost-effective solutions. Engineering firms need this data to evaluate the potential effects of flooding at a particular site and simulate how effective individual coastal protection measures are in protecting properties and neighborhoods from flooding over the life of these structures. Public agencies also need this granularity to figure out how vulnerable their assets (ports, airports, transit, waste water treatment and drinking water facilities) are to a changing climate. Similarly, private entities want to assess exposed assets (substations, buildings, generators and data centers) and critical systems that may need to be redesigned to handle changing conditions. One critical condition to evaluate is the capacity of the electrical grid to handle peak demand during longer and more intense heat waves. See also: Low-Risk Doesn’t Mean No-Risk  New Risk-Transfer Mechanisms Stationarity-based catastrophic risk models were never intended to assess risks to specific assets. To mitigate asset-level risk, all aspects of the private sector, as well as government bodies at the international, national and local levels, must make informed decisions based on accurate, current, highly localized data. Property values, liability risk and lives are at stake. With dynamic models, current data and modern analytics, mitigating risk is feasible. This type of information resource also will support new risk transfer mechanisms, including private insurance—and help reform obsolete mitigation strategies. This article was originally published at Brink News, here.

There’s been plenty of discussion in all sorts of forums about how artificial intelligence (AI) could undermine jobs in many big industries. AI can certainly improve efficiency and productivity. The big benefit of this technology, however, is its ability to enhance the performance of workers. AI has huge potential to assist workers in all facets of the insurance industry. Underwriting, especially, offers great opportunities for workers and intelligent machines to collaborate.  Underwriters are having to contend with a multitude of new risks, many of them highly complex and unfamiliar. What’s more, underwriters must also manage an abundance of new data sources. These additional streams of data are certainly a boon for insurers. They can provide sophisticated insights, often in real time, into a wide variety of risks. Managing this deluge of information, however, is becoming increasingly challenging. Recent advances in AI can help underwriters manage their increasingly complex workloads as well as improve their decision-making. Our research shows that most insurers have been slow to apply AI to their underwriting processes. They still rely heavily on large teams of underwriting professionals. Unfortunately, many of these underwriters spend much of their time performing mundane tasks such as manually entering data into online applications. We found that most underwriters spend less than half their time processing core information. Furthermore, less than a quarter of their time is spent selling or engaging with brokers. See also: Innovation Imperatives in the Digital Age   AI can help underwriters work far smarter. It can free them to focus on high-value activities and help them make faster, more accurate decisions. Already around 33% of insurers are starting to systematically harness the data they receive from multiple sources. By using AI applications such as intelligent data solutions, these organizations could gather and organize structured and unstructured data from a wide range of internal and external sources. The data could then be aligned according to the requirements of the insurers’ underwriters so they could quickly assess its importance. About a quarter of the insurers we surveyed are implementing intelligent processes in their organizations. By applying such AI processes to their underwriting, these insurers could improve substantially the performance of key workers. Such applications could include:

• Using cognitive robotics to sort and address basic service requests from agents.
• Deploying intelligent agents to respond to queries from agents and customers and provide them with basic information.
• Introducing self-adjusting win-probability calculators that help underwriters prioritize their tasks.
• Employing smart anomaly-detection systems that identify changes in renewal requests that might require an underwriter’s attention.
• Applying an intelligent demand-analysis system to identify potential new products.

These applications are all likely to enhance an insurer’s underwriting capability. As the applications constantly learn and improve with experience, their contribution will increase substantially. See also: Cloud Takes a Starring Role   In my next blog post, I’ll discuss how insurers should prepare their workforces for the introduction of AI. Until then, take some time to look at these links below.

• Accenture Technology Vision for Insurance 2018
• Realizing the full value of AI
• Model behavior. Nothing artificial.

The case for omni-channel capabilities is compelling. We are in an age where customers have many options for communicating. And the companies that can best provide them with capabilities to connect anytime, anywhere, via any method will increasingly be the winners. A recent personal experience highlighted both the potential and the pitfalls of providing omni-channel capabilities. Offering a variety of options can lead to increased customer engagement and retention, but poor execution can have the opposite effect.

On a recent day, bad weather was causing havoc for airlines and travelers. In the space of a few hours, I had four different itineraries due to delayed, canceled and re-routed flights. As a frequent flyer, I regularly rely on automated alerts regarding flight status, extensively use the airline app and leverage the call center to address significant changes or problems. Along the way, I also get confirmations or alert messages via e-mail and check the flight boards at the airport. All of these are great options for the company and customer to communicate. When the system works well, it is quite useful and gives me a leg up on other travelers with advance notice on changes.

Unfortunately, it does not always work well. On this challenging day, the various channels were hopelessly out of sync. I was getting automated calls from the airline giving me flight updates for flights that I was no longer booked on. The airline app indicated that I was still booked on yet a different (earlier) flight. I received e-mails about flight changes that had already been superseded by new flight changes. It was confusing, to say the least, and a frustrating customer experience.

See also: A Management Guide to Omni-Channel

This example goes to show that near real-time synchronization is not always good enough. Actions do not need to be updated across all channels within seconds, but delays of 30, 60 or 120 minutes are unacceptable. You may be asking what this has to do with insurance, an industry that typically has interactions with customers only a few times a year. The answer is: If you are going to pursue true omni-channel operations, the system needs to work – and it needs to be real-time.

As the world becomes more digital and more connected, the frequency of interactions with customers will increase dramatically. Smart homes/buildings, wearable devices, connected vehicles and other rapidly emerging solutions offer great potential for the insurance industry. However, one implication is that the omni-channel environment will become even more complex, and the demands for real-time actions will increase. Imagine reacting to an alert from a smart home device regarding an overflowing sump pump. Not only is quick action required, it must also be synchronized with the homeowner’s app and any phone calls to or from agents or the insurer. Communicating frequently with customers to partner in risk management, improved health and well-being and financial management is the future of the industry. And those customers will want to communicate in every way imaginable (including talking to live human beings).

This is a cautionary tale. One can imagine the kinds of scenarios just described regarding travel delays if they were applied to insurance customers’ problems – and the same or similar negative effects that such disastrous communication could have on them. Omni-channel capabilities will have to be coordinated in context and in time to provide true customer satisfaction.

There’s a lot of it about. Insurtech and technology, that is. New ways of doing stuff. Breaking traditional distribution models and deconstructing established supply chains. Who could not be excited? But there’s another side to this coin, and that’s the issue of established practice. Insurance isn’t a new gig, like telematics, but something that’s been around for three centuries. Some might argue even longer, as there are records of even the ancient Egyptians sharing and aggregating risk. Protecting the few by collaborating with the many. Over the centuries, insurance hasn’t been an easy ride. What do we mean by appropriate compensation, or, in insurance parlance, by the principle of indemnity? How to deal with those at fault, or, in insurance language, the matter of subrogation. See also: Where Will Unicorn of Insurtech Appear?

But in the old way of doing things, we all knew where we stood. Insurance contracts had evolved over decades, and where there had been differences in interpretation the legal system had sorted things out for us. There was a sort of certainty and framework to our business and a more certain relationship, even if the topic of trust remains contentious -- the level of trust between policyholders and carriers has always been low, despite a degree of contractual certainty.

Now, here we are in a Brave New World of insurance. Things will never be the same because of technology, the experts say. Some say insurtech is mainly just about new distribution channels, customer management and operational efficiency, but that leaves the rest of the insurance proposition.

It feels like we're throwing a ball onto a sports field and asking the two competing teams to sort out the rules for themselves.

Will there be winners and losers? Of course. The winners will be the legal profession, which will spend years, perhaps, discussing where the liability for death rests as a result of a driverless vehicle incident. Was it the manufacturer - as a product liability issue? Was it the occupant of the vehicle - extending the concept of occupiers liability? Was it the system administrator, which ran the system and which surely must be involved somehow? Maybe even the victims themselves: "Don’t you know you need to be more careful, with all these unmanned gadgets all around us?’"

We can’t all just contract out of responsibility. The proverbial buck must rest somewhere.

Think forward a few decades. Let’s accept that the insurance industry will have been re-engineered and reimagined, with robots, chatbots and wobots. Let’s assume that physical risk is calculated in a more granular way and that underwriting risk management is absolutely aligned to the risk appetite of a carrier. And we have somehow managed to be proactive, to have better responsiveness to climatic change and everything else. And ubiquitous devices provide us with bottomless barrels of information, from which our systems draw insight through advanced analytics.

See also: 3-Step Approach to Big Data Analytics

Someone, somewhere, will need to address the question -- what does all this mean contractually to the insurance industry? After, all isn’t insurance just no more than a contract, between two parties? Or was that concept somehow lost, somewhere inside the Innovation Hub, or among the bits and bytes of technology?

Isn’t it time that someone slowed the momentum of change and had a real hard think about the legal implications for insurance?

At a time when innovators are trying to start with customers—not with our old ways of doing business—and work backward to what products, processes and systems should be, J.D. Power reports that the customer experience with auto insurers has made marked progress. Satisfaction with auto insurers has actually risen even though prices have been climbing steadily.

What's the secret?

J.D. Power cites increased digital interaction with customers, especially for monthly billing. The firm says: "Customer satisfaction is at its highest when customers take care of transactions themselves and save the high-value interactions for live channels." The firm says that 73% of those customers surveyed said they wanted to verify payment receipt digitally, that 70% wanted to pay digitally and that 66% wanted to order proof of insurance cards digitally.

Underscoring the interest in more digital interactions, J.D. Power says that 10% of those surveyed said they participated in usage-based insurance programs, up from 8% in the surveys last year and the year before.

In general, the firm says customers credit auto insurers with being better able to interact via multiple channels, ranging from a face-to-face meeting with an agent to a fully digital transaction executed directly with the insurer, and appreciate the "omni-channel" approach.

The firm concludes: "The auto insurers that increase customer satisfaction across all facets of the customer experience make price just one part of the overall relationship.” (The full summary is here: http://www.jdpower.com/press-releases/jd-power-2018-us-auto-insurance-study)

My take:

The point about self-service is key. It may seem counterintuitive that customers want you to do less and them to do more for themselves, but, let's face it, companies aren't much fun to deal with. Customers are told they need to provide their account number, understand many things about your processes, correct errors that companies make in data entry, listen to bad music or obnoxious sales pitches if they've called in and are on hold, etc. Who needs it? Customers in all industries have consistently shown that they'd rather handle interactions digitally while sitting in front of the TV or listening to music. So, help your customers help you by having them take as much work as possible off your plate.

J.D. Power sounds a bit too optimistic to me both about how much progress auto insurers have made and about how much more loyal customers will be despite rising prices. It's still tough out there, and insurers have a long way to go.

But progress is progress, and we should all celebrate gains when we see them.

Have a great week.

Paul Carroll
Editor-in-Chief

Based on the trend toward value-based health insurance and reimbursement, health benefit plans are being designed to reduce barriers to maintaining and improving health and to promote higher-value healthcare services. Value-based reimbursement requires providers to track and report a host of adverse events and population health measures, including biometrics, patient engagement and other measures required to demonstrate quality performance. Unlike the traditional fee-for-service model, value-based care attempts to align incentives of providers to deliver the right care in the right setting in lieu of maximizing the revenue of each encounter by delivering more services. Providers receive incentives to use standardized, evidence-based medicine, engage patients, upgrade health IT and use more advanced data analytics to optimize their clinical and financial performance. When patients receive more coordinated, appropriate and effective care, providers are rewarded. Accessing Care Quality and Safety Data Plan sponsors and their benefits consultants or brokers who advise them need access to information about care cost, along with the quality and safety performance of those hospitals and physicians delivering care to their plan members. See also: Taming of the Skew in Healthcare Data   Quality measures are essential in optimizing the benefits of value-based models for all stakeholders. Success for all stakeholders depends upon how well healthcare providers can manage quality of care within tighter financial parameters. This presents an opportunity for benefits consultants and brokers who are well-positioned to act as trusted advisers in educating and defining how best to design benefits plans to optimize for value. As educators and advocates, they can guide plan sponsors toward partners who will help them evaluate provider quality and safety. Research shows that many U.S. employers that offer health insurance to employees are unfamiliar with objective metrics of health plan quality information. This gives benefits consultants and brokers an opportunity to outline the advantages of evaluating hospital quality to ensure that plan designs and benefits options include only high-quality hospitals and physicians who provide services at the lowest costs and encourage plan members through incentives to avail themselves to this narrower group of providers. The Challenge: Hospital Ranking Variability The significant challenge is the prevalence of numerous hospital quality rating methodologies. Even the slightest differences in adjustment methodology, data source, time period and inclusion/exclusion rules can produce differences in the hospital or physician ratings. This variation makes it more difficult for hospitals and physicians to prioritize and improve the quality of care delivered. For instance, hospital ranking organizations, such as U.S. News & World Report, Healthgrades, Centers for Medicare and Medicaid Services (CMS) and Leapfrog, reflect substantially different results, fostering confusion to those less literate in healthcare analytics. In 2016, CMS gave 102 hospitals its top rating of five stars, but only a few of those were considered as the nation’s best by private ratings sources such as U.S. News & World Report or viewed as the most elite within the medical profession. First-tier academic journals like JAMA expressed deep concern about the lack of academic credibility in the methods used to assess performance and aggregate the conclusions into a single rating across many different measures. Plan sponsors and their benefits consultants or brokers must educate themselves on assessing provider quality. While there is a myriad of rating services, many do not include elements essential to a precise and comprehensive assessment of providers. See also: Healthcare Data: The Art and the Science   Ratings approaches that use reputation or self-reported data should be considered less reliable than objective outcomes measures using patient level claims data. Additionally, hospital overall surveys or patient reported outcomes do not offer a valid basis for comparison. It is also not possible to use a single outcome measure – for example, risk-adjusted mortality -- as a proxy for all outcomes like complications or readmissions because provider performance varies widely across measures. For a comprehensive assessment, all available measures should be incorporated for a specific clinical category. Lastly, aggregating outcomes data into composite scores must be scientifically sound. As more employers seek greater value for their healthcare dollars, and as benefits consultants and brokers continue to pursue opportunities to help them reduce the upward cost spiral, quality ratings are an important first step toward realizing these goals and advancing the quest for improved employee health.

One of the outcomes of economic and technological changes has been the rise of on-demand insurance products, offered both by insurtech startups and incumbents alike. This includes products with continuous underwriting attributes, microinsurance products and insurance offerings for workers in the gig economy. These offerings aren’t typically grouped together, but they share an on-demand aspect that wasn’t required or technologically possible in the past. Continuous underwriting refers to the use of regularly updated (and possibly real-time) policyholder data to rapidly determine consumer risk and adjust policy terms and prices accordingly, as opposed to traditional term-based updates and renewals. Some forms of continuous underwriting have been around for a long time (example: pay as you go Workers’ Comp, with monthly updates based on submitted payroll) but now has applications to many lines. Microinsurance refers to coverage of smaller risks via rapid underwriting; including on-demand products like travel or event insurance, renters’ insurance broken out for specific high-value household items or pay-per-mile auto coverage. Gig economy insurance is most familiar to those outside the insurance space: as more and more freelance and “gig” opportunities like Uber and Postmates emerge, carriers are developing products to keep these independent contractors covered in a part-personal, part-commercial hybrid coverage. See also: On-Demand Insurance: What’s at Stake   While these three arenas of modern insurance might seem disparate in their final forms, they are emerging today due to a new consumer-focused approach to product definition and the connected technology necessary to allow a real-time approach. This foundation for all of them is built on three pillars: Data: On-demand insurance requires data, if not in real time then something close to it. If insurers are only getting updates as to policyholder risks and scheduled items after an end-of-term audit, then only a traditional approach will work. But as connected technologies and the Internet of Things have created a continuing pipeline of data, a new approach emerges. Insurers now have the ability to tap into discrete data points about coverages times and risks in an automated fashion, including: When is someone driving their car for Uber vs. for personal use? When is a business stocking high amounts of valuable goods? What is monthly payroll for workers’ comp? Product: It’s not enough to have access to the data. Insurers can’t just adjust rates on the fly. Instead, they need to take a consumer-first approach to modeling their insurance product. This means the restructuring and sale of a product with a variable pricing agreement and a flexible term. Done properly, this will allow the insurer to have the most insight into the collective risk and allow the consumer to have a transparent product that covers them for exactly what they need when they need it. Systems: Just because the data is available and the business has rethought the product structures doesn’t mean the infrastructure will be able to support it. On-demand products mean real-time web service calls and at least some component of automated underwriting decisions. Variable rates mean a rating engine that can calculate new rates on the fly based on updated risk info as well as a billing system that can adapt to variable billing amounts and dates. Without flexible and agile core systems, an insurer can’t roll out new products that behave in nontraditional ways. Insurers may be able to make progress with an on-demand offering even if they only have one or two of these pillars. Workers’ comp insurers, for example, have offered pay-as-you-go for a long time via manual form submission. But to make new products viable for a mass audience—and to compete with the consumer-driven ethos of Silicon Valley startups—automated data needs to be simple and convenient to turn on and off. This might take the form of a mobile app with a button to turn a microinsurance product on or off or perhaps the form of an automated data feed to a third-party system like payroll. Conversely, all three pillars are valuable to an insurer even if it hasn’t fully embraced an on-demand approach to their products. See also: Reinsurance: Dying… or in a Golden Age? Real-time data allows an insurer to understand its overall risk profile at any given moment and to make decisions and new sales and renewals. If, for example, you are selling a commercial liability policy and have up-to-date info about a business’ risks, it’s helpful even if individual policy pricing isn’t affected. In fact, this is how automotive telematics typically works: Auto insurers are gathering masses of data that demonstrates real-time risk and driving behavior, but they aren’t using it to do continuous underwriting/rating. Likewise, rethinking a product structure to take a more consumer-focused approach can happen even within the constraints of traditional insurance offerings or without real-time data. And, obviously, having modern and flexible core systems allows new product rollouts, better automation and digital interactions regardless of what products are sold. New insurance products like microinsurance and continuous underwriting aren’t just about gathering data or having a modern core system. Rather, they are based on a multi-faceted approach: understanding risk in a semi-real-time way; selling a different type of product; and having the core systems to handle it.