Tag Archives: fraud

Can Insurers Stop Financial Crimes? Yes

What makes it difficult to detect and prevent fraud within an insurance firm is also what might make fraud attractive to criminals: The low number of transactions in insurance provide few tracks for tracing financial crimes. Outside of premium payments and claim submissions, insurance customers engage in relatively few transactions (compared with banking customers) from which companies can build and test anti-money laundering models on their own. And, while insurance is a heavily regulated industry, it has been relatively ignored when it comes to its anti-money laundering practices in comparison with the attention regulators give to financial institutions. For these reasons, some fear that an annuity account, for example, might be just the place a nefarious character would park funds as part of a larger money laundering scheme.

The Next Focus for Regulators

Immediately following the financial crisis of 2008, regulators were laser-focused on big banks’ policies and procedures for deterring financial crimes. Those that didn’t comply with the U.S. Patriot Act and Bank Secrecy Act were hit with hefty fines. No wonder: It is estimated that almost 70% of illicit finance flows through legitimate financial institutions, while less than 1% of global trade is seized and frozen. Regulators are now turning their attention to non-traditional banks like Western Union (which expects to pay compliance-related charges of up to 4% of its revenue in 2017) and PayPal (which in 2015 agreed to pay $7.7 million to the Treasury Department’s Office of Foreign Assets Control for sanctions violations). Insurance companies feel they are the next industry to receive the attention of examiners and are acting to comply with know your customer (KYC) and anti-money laundering (AML) rules.

At stake for insurers is not just large penalties if a regulatory agency feels that anti-money laundering policies don’t meet expectations. Risk to reputation is of top concern to insurers, which understand that it takes only nanoseconds for customers to find an alternative carrier or for investors to learn on social media that their institution was used in organized crime or, worse yet, funding for terrorist activities. A regulator’s ability to directly affect an insurer’s bottom line is also a major threat. A regulator could, for example, hamper the insurer’s expansion efforts, preventing it from entering a market or from acquiring a business because it lacks the right safety controls.

See also: Cognitive Computing: Taming Big Data  

How Insurers Can Mitigate Money-Laundering Activities

To avoid this, I recommend to my clients that they focus on evaluating the entire AML and KYC function across the enterprise, cleaning and enriching the data that firms already have and bolstering AML efforts with outside expertise.

Clean and Enrich Your Data

The availability of high-quality data that is meaningful and predictive lays the foundation for an effective financial crimes prevention strategy. This critical first step is often overlooked and no easy feat for the typical insurance carrier that operates in silos and segregates information within different systems and lines of business.

Before investing in new tools and technology, partner with data remediation experts to assess the quality, completeness and predictive power of the customer profile data and fill in missing data to ensure that KYC and AML systems work effectively.

Establish a Consistent, Enterprise-Level Customer Onboarding, KYC and AML Process

Regardless of the many products and channels your insurance company offers, you need to establish a single, consistent process for monitoring, evaluating and onboarding customers.

Many insurance companies bring in an IT partner to assess their AML and KYC policies and procedures, as well as how technology can be leveraged to improve effectiveness. The right partner will help you define an onboarding strategy with a strong customer experience component and establish the roles and responsibilities for different lines of defense. This includes the agents who capture the customer information and onboarding; the financial crimes unit that monitors transactions and customer behavior; and the internal audit group, which ensures all policies and procedures are followed and measures their effectiveness at preventing financial crimes.

In addition, with clean data and an enterprise-level AML process, you’re ready to customize off-the-shelf generic AML models with observed client performance, data from public sources and third-party data feeds for the industry.

Look Outside Your Industry for AML Expertise

Insurers can learn a lot from compliance experts in other industries, such as banking, law enforcement and the public sector. Your recruitment efforts should focus on building financial crime teams with people from these sectors.

Find opportunities to share stories and best practices with compliance professionals outside your industry. Attend conferences focused on financial crime and regulation where the attendee list includes both banks and insurance firms.

See also: Big Data? How About Quality Data?  

Insurers whose AML strategy is built on meaningful and predictive customer data and that create a culture of compliance that permeates all areas of the company, will succeed at strengthening their mandated AML/KYC functions. While these changes can’t happen overnight, by pulling in expertise from outside the industry insurance companies can make great strides toward protecting their assets from fraudulent activities.

How Connected Data Can Help Stop Fraud

Insurance companies with legacy systems can find it extremely challenging to bring their data together because of different data formats and system access methods. They might have multiple sources of content with similar information for customers, claims, agents, books of business — information that, like in most organizations, was acquired over time or resulted from a merger. They might have mainframes and relational systems, and then they bring in third-party data. Add the fact that the insurance agency is invested in digital transformation, and you realize that the insurer’s relationship with the customer is changing. The relationship with the customer used to be managed by the agents. Now, there’s a desire to manage customers more directly by the actual insurers and bring in that data. The complexity of the underlying data sources and the data they want to bring together makes this difficult.

The challenge is trying to move all the data into some sort of central, unified location, but insurers are not able to do it at the scale that they would like. There are many attributes related to customers and policies and claims. So, instead of bringing all that data together and asking all the questions that insurers would like to ask, they cherry pick three or four. They spend a lot of time writing extract, transform and load programs, as well as other data processing pipelines, to move data from the source systems into some sort of target schema. So, the day to day is a lot of gnarling, churn, programming and data movement to answer a slimmer portion of the entire question set that companies would like to ask of the data.

See also: Workplace Wearables: New Use of Big Data  

Modeling Data to Detect Fraud

When it comes to fraud indicators, there are many signs that can be identified by the relationships in the data. For example, on a policy application for insurance, there are phone numbers, addresses and the relationship to an agent or an organization who sold the policy to the individual. If someone gets a policy with one agent and then tries to get a similar policy with a different agent, the applicant could be shopping around for the best deal or the agent could be trying to give someone a policy he doesn’t need. But relational databases typically aren’t good at highlighting these types of issues.

In addition, while some things that are more easily modeled as a graph, the hierarchical data in insurance is typically put into rows and columns and tabular format. For example, in insurance, a book of business can belong to an organization or an agent, but an organization can have agents, which can have a book of business. It’s a recursive model. If you want to understand the relationships and examine some sort of policy tied to them, the analysis can get very complex. But when you put the data into a graph, where you have it modeled as entities and relationships, you can quickly pattern match to see who are the individuals and agents who have a relationship to a policy or application.

A person should only have one type of relationship to a certain type of policy. When you compare and quickly visualize and see this person has two relationships to two policies that are similar, you can ask, “Why?” You can very quickly tease out that there is something there. If the pattern doesn’t match, the issue is quick and easy for you to identify.

There is a similar scenario for agents. Agents can sell certain policies and not others. When you model the data as a graph, you can say this agent has an inappropriate relationship to a policy. A one-line, simple query can expose the agents who are engaging in this type of behavior. Also, when you have that visualization of their relationship to the policies they are and aren’t allowed to write, an actual physical pattern emerges of those relationships, where it gets easy to identify and spot who is up to nefarious or questionable activities.

Using Data to Prevent Fraud

There is a lot of complexity in these organizations and in how agents, customers and the insurers interact. If an insurance organization were going to start a modernization project around fraud investigation and fraud prevention, it should leverage the technology that allows it to quickly manage information as a graph.

Property graphs are very adaptive; they are additive. Traditional data integration requires that you must understand all your sources and all the attributes before you begin. Then you come up with the schema to encapsulate all the data, and that’s what the proposition is. This encapsulation takes years, and no one ever hits the target because business sources and targets change. With graph technology, you can start to rapidly connect just the data you need as you need it and continue to append and add to those graphs to create a rich view of the data landscape. With a graph, you can start to tease out things and use the relationships where addresses, phone numbers and emails become things unto themselves related to a person, policy or a claim.

See also: 5 Key Effects From AI and Data Science  

The reason you want to do these types of things is because you can quickly start loading hundreds of thousands of policies and claims and applications into the system, and you can start to look at things like shared addresses, shared numbers and shared email addresses. Very quickly, you can start to see who is up to legitimate activity and who is up to illegitimate activity. There are indicators regarding things like a phone number. Fraudsters tend to use the same phone number for all fields of any policy applications. When you load these applications together and examine at scale, you’ll see in the data that no one else has a relationship to the phone number the fraudsters have used. But it’s common to see people share phones in a home or office when they’re not engaged in fraud. You can tease out those relationships, as well.

Another example is address information. When you look at policy applications, the person’s address shouldn’t necessarily be the same as the employer’s address or the agent’s address. There is value in having the entities and relationships to model, so you can quickly identify who has the appropriate relationships to which entities. You can see if someone is even a policy holder, if the person has any relationship with the agent, if the person has the same address as the agent’s, etc. When you load all the data into the system, relationships allow you to quickly see the behaviors between the transactions. This is one of the key benefits of working with connected data.

How Insurance and Blockchain Fit

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.

How AI Will Transform Insurance Claims

If you’re like most people, calling an insurance company isn’t among your favorite activities. That’s because the insurance industry is one of the least innovative areas for customer experience. Customers typically come away from their interactions dissatisfied. However, things are definitely changing, and artificial intelligence is playing a large role. The fast-growing technology has the potential to disrupt the entire industry and greatly improve the insurance customer experience.

Artificial Intelligence in the Claims Process

The insurance agency is notorious for its outdated processes. Filing a claim often looks the same today as it did decades ago because the industry isn’t consistently leveraging new technologies that are available. If an employee is busy or on vacation, a claims request could sit still until the right person is back. The outdated processes make it harder for agents by increasing the workload and forcing them to work with antiquated systems and frustrated customers.

However, AI can be applied to improve the claims process. Claims currently are touched by multiple employees. However, a new process of “touchless” claims doesn’t require any human intervention. This process uses artificial intelligence and other technology to report the claim, capture damage, audit the system and communicate with the customer. The potential here is huge, as the process could allow clients the chance to file claims without having to wade through red tape.

See also: Strategist’s Guide to Artificial Intelligence  

Companies that have already automated some aspects of their claims process have seen a significant reduction in processing times and quality. AI-powered claims could also fight against one of the most costly elements of the insurance industry: fraudulent claims, which cost the industry more than $40 billion a year. Instead of relying on humans to manually comb through reports to catch inaccurate claims, AI algorithms can identify patterns in the data and recognize when something is fraudulent.

Future of AI and Insurance

The industry is definitely ripe for AI disruption. Customers expect to be able to interact with companies through modern technology; a recent survey found that 74% of consumers say they would be happy to get computer-generated insurance advice.

Many insurance companies are already using artificial intelligence to some degree, and the number of companies following in their footsteps is sure to increase dramatically over the coming years. Artificial Intelligence has never been less expensive or more accessible, which means most companies don’t have a reason not to adopt it in at least some form.

Chatbots

Chatbots work through messaging apps that many customers already have on their phones, which makes them a natural next step in customer interaction. To truly be effective, chatbots must have natural language processing and sentiment analysis so they can understand what customers are really asking. Effective chatbots can process concerns that are either typed or spoken from customers and provide personalized service. In the insurance space, chatbots can be used to answer basic questions and resolve claims, as well as sell products, address leads or make sure customers are properly covered by their insurance.

Marketing and Underwriting

Insurance is a competitive market, so a strong marketing strategy is vital. Traditionally, insurance companies used blanket methods like cold calling customers, but today’s customers expect personalized sales tactics. AI can pull in customer data to create a full profile that can be used to offer only relevant insurance products and remember a customer’s preferences.

Instead of spending valuable time and money on the underwriting process, which typically includes invasive questions and surveys about to dictate premiums, artificial intelligence could automate the entire process. Bots could potentially scan a customer’s social profile to gather information and find trends and patterns. For example, someone who has a healthy lifestyle and a steady job may be able to be connected to being a safer driver, which could lower insurance premiums. AI can analyze data better than humans to more accurately predict each customer’s risk, thereby providing customers with the right amount of insurance and companies with protection from risky customers.

Data

Insurance is driven by data, and it has a huge effect on the company’s bottom line and the satisfaction of the customer. A recent study found that nearly 80% of insurance executives believe artificial intelligence will revolutionize the way insurers gain information from their customers, with more than half saying the biggest benefit is being able to leverage better data for improved insights into the customers.

See also: Seriously? Artificial Intelligence?  

Telematics, or wireless communication of data back to an organization, is expected to be a huge area of growth for insurance. Many insurance companies already offer discounts to customers who transmit their driving data back to the company. Telematics and artificial intelligence can take this one step further by recognizing GPS patterns with the data, inferring road and traffic conditions and even predicting and helping avoid accidents, which could potentially lead to fewer claims to process and safer and more satisfied customers.

The insurance industry has long been bogged down by outdated practices. However, the combination of a new wave of thinking and newly developed artificial intelligence technology has the potential to completely change the customer experience to provide great service in a way that resonates with modern customers.

You can find more on this subject in More Is More: How the Best Companies Go Farther and Work Harder to Create Knock-Your-Socks-Off Customer Experiences.

When Customers Lie, We Learn

As the insurance industry pushes ahead into the next decade, adapting to change and, in some cases, leading change, those committed to the industry and its purpose face an underlying tension that they all wish would just go away. They wish the industry were not so hated by the public.

While “hate” is a strong word and potentially offensive, insurance definitely ranks toward the bottom relative to respect when compared with other industries, products and services.

In my personal quest to end this problem, I find that you can learn a lot by examining the worst of the possible behaviors—that is, insurance fraud.

Recently, my colleagues at Maddock Douglas and I engaged in some conversations with both LexisNexis and Swiss Re on this subject. Both organizations have some interesting lenses to look through. In fact, the three companies will be doing a webinar to share these views on Aug. 10.

See also: Happy Producers, Happy Customers  

First and foremost, it was surprising to hear how big the problem of fraud actually is. LexisNexis Risk Solutions, through a variety of sources, has reported that fraud costs the insurance industry more than $80 billion a year. There are many flavors of fraud, ranging from out-and-out intent to steal money from insurance companies all the way to “little white lies.” The ability to detect and size that behavior is very helpful in creating realistic expectations around costs and isolating areas for improvement.

I find the white lies more intriguing and potentially helpful in understanding the problem because they are more widespread and harder to detect. I suspect that some of that behavior stems from a lack of understanding about how insurance systems work, with consumers not necessarily realizing that “misbehaving” has a cost.

Swiss Re has some interesting insights about the behavior, as well. They’ve leveraged behavioral economics to learn that the context, order and style in which we ask basic underwriting questions can make a big difference in the truthfulness and accuracy of answers.

I believe there is yet another lens that we can put on this challenge: social norms. Insurance is a social construct; however, we treat it like a product. Social constructs—such as electricity, cable TV, public transportation, public parks, schools and community resources—are shared. The behavior of a few with respect to those shared constructs affects the many. It is the many who have responsibility for their preservation, especially when they start to break down.

However, that big picture is often lost after those social constructs age way past the people who invented them. So perhaps it’s time to reinject social norms into the insurance conversation, helping the public see how their behavior affects others.

Some great role models for this type of shift would be:

  • The Keep America Beautiful campaign from the 1970s
  • Mothers Against Drunk Driving
  • Quit-smoking campaigns

On the surface, it would seem like the common denominator is a giant advertising expenditure. While that may be true, there is another common denominator we can learn from—that is, a social label.

Keep America Beautiful, best known for the crying American Indian, is the campaign that also created the term “litterbug.” “Don’t be a litterbug.” Litterbugs are socially unacceptable. But, prior to the invention of that term, it was socially acceptable to dump litter at a traffic light out your car window.

Mothers Against Drunk Driving (M.A.D.D.) created the concept of the designated driver. The “DD” is a hero to those who like to have a good time but want to stay safe.

Quit-smoking campaigns created the concept of “secondhand smoke.” Yes, scientifically proven, but, more important, the concept brought into focus the innocent victims, often children or nonsmoking co-workers. The result was ordinances and family rules banning smoking in many locations.

Is there such a social label that can be created around “little white insurance lies”? The label could cast out the villains, glorify the heroes or spotlight a victim. My colleagues and I have done a little brainstorming on this subject, and there are some interesting ideas we will talk about during the webinar.

See also: How to Get Broader View of Customers  

The bigger question for the industry is whether this labeling is a job for a single brand or a coalition. In my opinion, either is possible with the right passion to make a difference.

While the past examples all had large ad expenditures behind them, they were all invented before the internet existed, when ad spending was the lever that brands and coalitions would pull to raise awareness. However, today we have unlimited access to technology and social connectivity.

Think about the success of the Ice Bucket Challenge. This social campaign raised $115 million for ALS research in just a few months. Why? It created millions of heroes. It let people, for a moment, feel what it was like to be a victim and then do something about it, either through raising awareness or raising money. Those who didn’t participate were, socially, some level of villain. After all, couldn’t you sacrifice your comfort for a few seconds, or kick in a few dollars, for a good cause?

Wow. Hmmm.

Do you have any thoughts about how we could create that sweet spot within insurance? If so, I would love to hear.