KEY TAKEAWAY:

--Innovation goals cannot be achieved with traditional, repetitive, paper-based processes. Insurers need to take five technology-based steps to accelerate innovation.

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Insurers, globally, are on the proverbial edge of disruption as they are constantly facing challenges due to changing customer needs, increased customers' expectations, heightened competition from insurtech firms and increasing regulatory requirements. Now, customers want to be onboarded not in days but in minutes. They are no longer willing to wait for weeks and months to settle claims. The pressure on insurance carriers and adjustors is mounting to accelerate product innovation. Maintaining the status quo is not going to work. 

They need to launch innovative products faster, transform the underwriting process, improve policy administration, speed claims processing and enhance risk compliance. These business goals cannot be achieved with traditional, repetitive, paper-based processes. 

Considering the well-known inertia of insurance companies and propensity to be slow about technology adoption, how can they fast-track innovation in the top gear and speed up go-to-market strategies?  

1. Accelerate time to market by partnering with technology services providers

Most insurance carriers don't have enough tech capability, but they can forge partnerships to capitalize on other firms' expertise in new-age technologies to expedite product innovation. For instance, such collaborations can be established with insurtech service providers to create fit-for-purpose customized solutions based on unique business requirements.

2. Adopt Advanced Products to Automate Processes

Insurers can take advantage of advanced technology platforms to develop fit-for-purpose products and bring them to market quickly.

With advanced policy management systems, insurers can automate time-consuming tasks such as underwriting and issuance. Brokers can develop feature-rich software for end-to-end policy management, client services, underwriting, claims processing and more. A customizable self-service portal can be developed to let brokers, intermediaries and agents automate receiving customer requests, creating quotes, guiding prospects/customers and selling policies. 

See also: From Vision to Product (Part 1)

3. Leverage Platform Development Accelerators 

Platform accelerators can fast-track innovation by enabling insurers to leverage pre-built functionalities, integrations and modernization capabilities. These accelerators also can provide consulting on products, on platform development and enhancement, on integration with other enterprise and third-party applications and on comprehensive implementation and support.

4. Take Advantage of New-Age Tech Services 

An adjustor client of ours used optical character recognition (OCR), machine learning (ML) and technologies such as Pytorch and Fast AI to correct a problem with bad data they were getting from surveyors and to quickly generate accurate reports on property damage. The company is now settling claims faster and generally providing better service.

Other tech services to take advantage of include:

Cloud

Migrating to the cloud offers a significant advantage over traditional environments in terms of the speed with which new capabilities, business features and products can be developed, tested and launched. Using application programming interfaces (APIs), insurers can quickly connect their systems with third-party providers, enabling them to access new functionality and data. Insurers can also easily host insurance platforms, facilitating the deployment of cloud-native applications.

Low Code

Low-code development services can allow insurers to rapidly build, test and deploy new products in underwriting, claims management and claims processing and policy management systems. Even small insurance companies can innovate faster with low code.  

Robotic Process Automation (RPA)

RPA can automate repetitive and rule-based tasks, such as data entry, validation and policy administration, minimizing manual errors and freeing employees to focus on essential tasks. RPA also improves process efficiency and accuracy, leading to faster product development and a more streamlined customer experience.  

5. Adopt Digital Modernization 

In the traditional insurance model, launching a product is very difficult. Insurers have to invest significant time and resources into designing the policy, as evaluating underwriting risks and deciding premiums may take considerable time. However, if the insurer has migrated to the cloud, they can leverage the power of data analytics and machine learning and automate the entire underwriting and pricing aspect. As a result, an insurer can cater to fast-changing consumer needs. 

The Crux

Factors such as regulation and product complexity have made insurers slow to innovate. However, technological advancements have reduced the barriers to entry, so insurers must step up their game on product innovation to retain their competitive edge and grow business. 

KEY TAKEAWAYS:

--Being data-driven is becoming more difficult because of the sheer volume and complexity of the data being generated. Data privacy concerns also complicate matters, as does the potential for bias in AI models.

--Organizations can take four steps to overcome the obstacles and tap into the benefits of an intense relationship with their data.

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With AI's potential to dramatically change how businesses operate and make decisions, becoming data-driven has never been more important. However, as technology advances and the world becomes increasingly connected, organizations are finding it more and more difficult to become truly data-driven. Organizations are collecting more and more data from consumers, but the processing and harnessing of this data stays limited.

Let’s explore some of the key challenges that organizations face in the age of AI and discuss potential solutions.

Challenge 1: Data Volume and Complexity

One of the main reasons that becoming data-driven is becoming more difficult is the sheer volume and complexity of data being generated. With the rise of IoT devices, social media and digital transactions, it’s estimated that by 2025, 175 zettabytes of data will be created annually. This exponential growth in data has made it increasingly difficult for organizations to process, analyze and draw insights from their data.

Moreover, the complexity of data has also increased. Unlike traditional structured data, which can be easily stored and analyzed in relational databases, the majority of the data generated today is unstructured or semi-structured. This includes data from social media, images, videos and natural language text. Processing and analyzing this type of data requires advanced techniques, such as machine learning and natural language processing (NLP), which can be resource-intensive and require specialized skills.

Challenge 2: Data Privacy and Security

Data privacy and security concerns have also grown in the age of AI. With data breaches and cyberattacks on the rise, organizations must navigate a complex landscape of regulations, such as the General Data Protection Regulation (GDPR) and the California Consumer Privacy Act (CCPA), to protect their customers’ data. These regulations impose strict requirements on how organizations collect, store and process personal data, adding complexity to becoming data-driven.

Moreover, as AI models become more sophisticated, they can inadvertently learn and reveal sensitive information about individuals. For example, machine learning models trained on large datasets have been found to memorize and leak details about individuals, such as their medical records or credit card numbers. As a result, organizations must be cautious when using AI to analyze their data and take steps to ensure that they protect sensitive information.

Challenge 3: Data Bias and Fairness

Another challenge is ensuring that data and models are free from bias and promote fairness. AI models learn from data, and if the data used to train these models is biased, the resulting predictions can also be biased. This can lead to unfair treatment of certain groups, such as when AI is used in hiring, lending or medical diagnosis.

Addressing data bias and ensuring fairness requires organizations to carefully curate their data, develop techniques to detect biases and apply methods to mitigate these biases. This process can be time-consuming and requires a deep understanding of both the data and the domain in which the AI model will be applied.

See also: Achieving a 'Logical Data Fabric'

Potential Solutions

Despite these challenges, organizations can still become data-driven in the age of AI. Some potential solutions include:

1. Investing in Data Infrastructure and Tools: By investing in scalable data infrastructure and tools, organizations can better manage and process the growing volume and complexity of data. This includes investing in cloud-based services, data and advanced analytics platforms that can handle both structured and unstructured data.
2. Fostering a Data-Driven Culture: Encouraging a data-driven culture can help to overcome some of the challenges associated with becoming data-driven in the age of AI. This involves promoting data literacy, providing training and education on data and AI, and encouraging collaboration among data scientists, domain experts and decision-makers.
3. Implementing Robust Data Governance: Establishing a robust data governance framework can help organizations tackle data privacy and security challenges. This includes implementing data encryption, access controls and regular audits to ensure compliance with data protection regulations.
4. Developing Ethical AI Guidelines: To address the challenges of data bias and fairness, organizations should develop ethical AI guidelines and best practices. This can include investing in research to detect and mitigate biases in AI models, incorporating fairness metrics into model evaluations and developing diverse and inclusive datasets.

Becoming data-driven in the age of AI presents a unique set of challenges. However, by investing in data infrastructure, fostering a data-driven culture, implementing robust data governance and addressing ethical considerations, organizations can navigate these challenges and harness the full potential of AI.

KEY TAKEAWAYS:

--IoT sensors can accurately forecast not only the likelihood of a flood event, but more importantly identify exactly which buildings or facilities will be affected. A combination of flood forecasts, nowcasts and real-time IoT data can let companies act before or during a flood event to mitigate the impact.

--The spread of IoT technology will likely increase the use of parametric insurance, with payments triggered by precise data from IoT sensors.

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Rising risks from flooding in every part of the U.S. will require an innovative response from insurers and risks managers, and the Internet of Things (IoT) is set to become key to how risks will be managed and mitigated.

Flood data facts 

Today, half of all U.S. economic losses from flood are due to pluvial flooding (Nature.com 2022). At the same time, current flash flood warnings lack actionable data, as they cover large geographies, which generate many false alarms, with a resultant lack of action. In addition, a threefold increase in damage to commercial property from climate-related risk is now predicted by risk analysts XDI 1000 by 2050. 

A recent study by the Pew Charitable Trusts found flooding in the U.S. is a year-round threat. (Flooding Is Nearly a Daily Occurrence Throughout the U.S.)

While flooding from large-scale extreme weather events such as hurricanes tends to grab headlines, they are only part of the nation’s flood story. The Trusts analyzed storm data and other events, including heavy rains and rapid snow and ice melt, which now cause varying degrees of flooding across the country, often inundating homes and businesses, compromising infrastructure and impairing local economies.

Pew’s analysis of the National Oceanic and Atmospheric Administration’s Storm Events Database, which includes reports from a variety of government and nongovernment sources, found that, since 2000, at least one flood occurred in the U.S. on nearly 300 days per year, on average. The NOAA database also shows that all 50 states and the District of Columbia were affected by flooding in 2021. 

See also: The New IoT Wave: Small Commercial

The knock-on effect

The flooding has led to a more cautious approach from underwriters to the coverage they offer, which has been made all the more difficult by reinsurers’ reluctance to provide capacity for natural catastrophe risks in the 1/1 and 1/4 renewals. Primary markets have had to raise their attachment points significantly, assuming more of the risks themselves. 

They are becoming far more deliberate in how they deploy capacity that is, in effect, unsupported. Those concerns are passed down to the policyholder in the shape of higher deductibles, lower limits and higher premiums.

As a result, we are seeing a tangible shift in focus away from simply post-event restoration and recovery to a system of robust risk management with prevention and mitigation pre-event at its heart.

IoT is the answer

To deliver such a strategy early identification will be key, and technology can and will play a significant role.

Policyholders, be they personal homeowners or businesses, need to be able to identify the threat of flooding before the event so they can put in place flood mitigation systems to reduce damage and loss of property.

For business, the ability to reduce the losses and the impact can be the difference between success and failure -- over 40% of businesses fail to reopen after suffering a flood event. And IoT sensors can accurately forecast not only the likelihood of a flood event, but more importantly identify exactly which buildings or facilities will be affected. A combination of flood forecasts, nowcasts and real-time IoT data can provide insights into flood risk, letting companies act before or during a flood event to mitigate the impact.

See also: The Intersection of IoT and Ecosystems

Plugging the protection gap

Insurers will be able to redesign how they can offer insurance cover, likely using more parametric products. Because parametric covers require the ability to access unequivocal independent data on which the payment can be triggered, it is where IoT sensors that can measure the height of water at a given point come into their own.

For businesses that have had challenges settling past flood claims, parametric insurance, backed by highly accurate IoT sensors, offers greater certainty about when and how claims are paid. 

It will even allow insurers to better understand their clients’ individual risk and offer parametric coverage where conventional insurance isn’t viable, while delivering accuracy in underwriting product structure and pricing.

All in all, IoT sensors enable insurers to work with their clients to mitigate and manage flood risks better, which should consequently help reduce the number of businesses for which a flood event spells failure, especially in hardening economic and financing conditions

KEY TAKEAWAYS:

--When a patient attempts to register online for the first time, they should be asked to capture a government-issued ID through their computer webcam or mobile device. The registrant should then be instructed to take a live selfie, which ensures that the person listed on the ID is the same as the person trying to open the account.

--After an account has been opened, medical offices and pharmacies can approve future treatment and prescription requests simply by requesting a new selfie of the patient.

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Being a chief information security officer (CISO) in today’s healthcare environment is increasingly challenging. The modern threat landscape forces healthcare CISOs to protect not only against malware and ransomware attacks but also to defend against more traditional vulnerabilities in legacy equipment. 

Between the headlines discussing ransomware groups and organized cybercrime, organizations may not have fraud prevention squarely on their radars. They should. Fraud costs the U.S. healthcare industry more than $50 billion annually, according to data from the National Health Care Anti-Fraud Association. 

Identity theft is at the root of the industry’s insurance fraud problems. When someone’s identity gets compromised, malicious actors can use that patient’s information to make fraudulent claims with providers. This not only harms the patient but also the healthcare organizations left to deal with the phony claims. 

To help combat this issue, healthcare can borrow countermeasures from the finance industry. The financial sector has turned to the Know Your Customer (KYC) process to verify the identity of their clients and help prevent financial crime. Similarly, healthcare providers can adopt Know Your Patient (KYP) processes to address the rising threat of insurance fraud. 

Knowing your patients

In the world of finance, the KYC process is a central component of the modern regulatory environment. KYC helps institutions verify their clients’ identities and ensure they are not being used in any money laundering activities. In confirming the client’s identity at the beginning of the customer journey — when that customer first attempts to open a bank account — organizations are able to keep malicious actors from gaining entry to their systems in the first place. 

The KYP process functions similarly in healthcare. KYP is designed to help eliminate fraud risk at the front of the patient experience by strengthening the onboarding process and verifying at the beginning of the journey that someone is who they claim to be. 

Considering the rate at which the healthcare industry is falling victim to fraud, now marks a good time for organizations to reevaluate their security protocols and explore the adoption of a KYP program. 

See also: How Synthetic Data Aids in Healthcare

Developing the program

Establishing an effective KYP program consists of a few key components in tandem with a continuing authentication process. Organizations must remember that identity verification cannot be a one-time event and necessitates a more elaborate approach. 

To begin the process, organizations need to verify that a patient matches up with their government-issued ID. When a patient attempts to register online for the first time, they will be asked to capture their ID (such as their driver’s license, passport or other form of ID) through their computer webcam or mobile device. The registrant will then be instructed to take a live selfie, which ensures that the person listed on the ID is the same as the person trying to open the account. The biometric template created at this step will be useful for future authentication.

Once the ID and selfie have been collected, organizations will need to determine whether the provided ID is legitimate and if the selfie matches the picture on the ID. 

There are a handful of warning signs that may indicate a particular ID is fraudulent or being misused. Fraud detection analytics can reveal if an individual has any potential history with, or active connections to, fraudulent activity. Additionally, minimum age requirements may affect a registrant’s ability to open an account. 

Depending on the outcome of these various checks on the government-issued ID and the biometrics of the individual, the KYP program will provide a verdict for the organization to permit or deny the registrant’s new account. If the patient’s ID correlates with the biometric results, the process is complete and the account will be opened. 

After an account has been opened, medical offices and pharmacies can approve future treatment and prescription requests simply by requesting a new selfie of the patient. With each selfie taken, a new biometric template is generated for comparison with the template that was initially captured at enrollment to authenticate the returning patient.  

See also: Why to Customize Employee Healthcare Plans

Building a stronger future

It can be difficult for modern healthcare organizations to strike the right balance when it comes to fraud deterrence. They want to make the identity verification process seamless while deterring fraud and reducing friction for actual patients who are merely attempting to seek care. 

A sophisticated KYP program is intended to help organizations find that balance by letting in the legitimate patients and simultaneously keeping fraudsters out. By employing identity verification and authentication, providers can quickly confirm real patients, adhere to regulations and help thwart costly instances of insurance fraud.

KEY TAKEAWAYS:

--With many traditional life insurance agents retiring, AI can step into the gap and present customers with the right proposal at the right time.

--AI-powered chatbots can also sweeten the customer experience by providing 24/7 customer service, answering questions and resolving issues in real time.

--Insurance agents can also use AI to refine their messaging process across different media channels, as well as to reduce fraud.

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Breakthroughs in machine learning and AI mean that insurers can tap into insights from vast troves of data and refine their operations on a more granular level than ever before. Consequently, insurance companies are finding new ways to enhance the customer journey, streamline operations and make better decisions. 

Machine learning and AI permit insurers to employ large data sets that would be a nightmare for humans to parse. According to Dror Katzov, CEO of Atidot, many insurance companies only use 20% to 30% of their data. There is much more that could be used to improve the customer experience, from digital policy delivery to better customer engagement and claims management.

AI can help insurers identify customer behavior and adjust product offerings accordingly. Just consider what it can do for life insurance. With many traditional life insurance agents retiring, there is an opportunity for AI to step into the gap and present customers with the right proposal at the right time. AI can continuously model the different situations that consumers face and provide better risk models to help insurers make savvier decisions, which opens up the means for more dynamic pricing. For example, an AI model could be trained to recognize if a person has started going to the gym and offer them a better insurance rate for adopting a positive lifestyle change.

AI-powered chatbots can also sweeten the customer experience by providing 24/7 customer service, answering questions and resolving issues in real time. This frees customer service agents to handle more complex issues. Moreover, AI can also personalize product offerings based on customer data, making it easier for insurers to provide the right coverage at the right price and to produce client-advocacy documents, such as cover letters, in much less time. 

Insurance agents can also use AI to refine their messaging process across different media channels. Jeff Root, managing partner at DigitalBGA, says the assistance offered by AI will obviate the need for many types of instructive classes. “You should never have to buy a course again,” Root says. “All the courses selling you Facebook ads for any sort of life insurance… you can get the information online through this [Chat GPT] AI bot.” Aside from helping businesses generate leads through social media platforms such as Facebook and Google, AI can also help businesses be more agile and responsive to changes in the market.

For ages, fraudulent claims have been the bugbear of the insurance industry. (Florida, for instance, has been a bastion of fraudulent claims – so much so that many insurers have exited the area.) AI may help to remedy this situation by improving fraud detection, enabling insurers to quickly identify suspicious claims and prevent fraud before it occurs. Additionally, AI can enhance risk management by providing more accurate pricing and underwriting decisions, resulting in better outcomes for both insurers and policyholders.

All that said, there are challenges associated with the adoption of AI in the insurance industry. It has long been a burden for the industry to establish trust with the customer; AI can help automate more processes, but human oversight is still essential. 

See also: Technology and the Agent of the Future

Ethical and legal considerations constitute the main challenges surrounding AI. There are valid concerns about how AI is used to price insurance policies and how it may lead to unfair discrimination against certain groups. The European Union's General Data Protection Regulation (GDPR) requires companies to explain their decision-making processes when using AI and to provide individuals with the right to contest decisions made by automated systems.

Another challenge is the potential for bias in algorithms. AI systems learn from the data sets they are fed, and if that data contains biases, the AI system will also be biased. For example, an AI system may learn that individuals living in a certain ZIP code are more likely to make fraudulent claims, leading to discriminatory pricing or coverage decisions.

Privacy is also a significant concern. As insurance companies collect and analyze large amounts of data, it's important that this data is stored securely and that customer privacy is protected. Steps must be taken to prevent data breaches and exposure of sensitive information.

Finally, the effectiveness of AI in insurance depends on the quality of data that is being used. If the data is incomplete, inaccurate or outdated, the AI system will not be able to make accurate predictions or provide useful insights. For instance, although ChatGPT–the most popular AI chatbot in the world today–can deliver fast results, there is always the possibility of it offering up erroneous information. On a positive note, Nvidia has developed a new way to keep AI from "hallucinating," i.e. offering up incorrect or inappropriate content.

AI's potential for innovation is exponential. AI can help insurance companies meaningfully and efficiently use more of their data, improve customer engagement and refine their targeting process. Nonetheless, businesses interested in using AI should closely monitor developments in the space and be cognizant of the challenges associated with its adoption. With scrupulous implementation and judicious oversight, AI can supercharge the insurance industry and provide better services to customers.

KEY TAKEAWAYS:

--Organizations should begin with an assessment involving nine questions, then explore each for opportunities for consistent improvement.

--Organizations don't need superhuman efforts to be innovative. They need small actions that can compound over time. It is much easier to adjust the course of a ship in small tweaks than to change directions drastically. The passengers aboard will also prefer avoiding brute directional changes. 

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Contrary to popular perception, innovation isn't about transformation. The most effective innovation will come from small changes that compound over time.

One of my favorite examples of how small changes can snowball into bigger results is the 1% rule. If you improve an area by 1% every day, you will double your results within 70 days. Try the math on your end!

If organizations improved their innovation efforts by 1% every weekday, they would transform their output in a little over three months. 

Relying on big hits is unstainable. Industries built on big hits are also highly susceptible to droughts. A perfect example is the movie industry, which needs blockbusters to make the economics work.

In this article, you will learn how your organization can start to look for those 1% improvements that will add up to big changes in your innovation efforts.

Making Innovation Tangible

One of the biggest challenges organizations face when tackling innovation is ambiguity. What does it even mean to become more innovative? Without clear definitions, you will end up with meaningless words on a random wall somewhere.

To help make innovation tangible, I created an assessment called the Innovation Reality Check (IRC) that any team can run in a few minutes. It is meant to help you pinpoint areas within your organization that could be improved to create more innovative decisions.

There are nine questions in the assessment. For each question, rate your team or organization on a scale of one to five, with one being never, two being rarely, three being sometimes, four being frequently and five being always. 

The questions are as follows:

1. We check a consistent list of broad categories where innovation can happen
2. We start with the end in mind and work backward, letting go of present day constraints
3. We track the speed of our decisions and work to improve it whenever relevant
4. We have a process that encourages ideas from anywhere in the organization
5. We have a decision-making process that minimizes bias and politics
6. We encourage healthy debate of ideas regardless of role or status
7. We provide sufficient support and resources to allow ideas to flourish
8. We review successes for future process improvements while ignoring failures
9. We reward the behaviors that lead to innovation and not just the result

Here are some guidelines for understanding your rating.

• 30 – 45 = you are a consistently innovating organization
• 15 – 29 = you are attempting to innovate, but several factors need to be improved
• 0 – 14 = you’re not spending enough time or resources thinking about innovation

The first step in any endeavor is to know in what direction you should head. The assessment gives you some initial ideas, and I will help you understand the possibilities of each question.

See also: Insurers Aren't Innovative? Think Again!

Deconstructing Innovation Into Next Steps

Each question contains endless opportunities for improvements and better decisions. Here are some examples for each one:

We check a consistent list of broad categories where innovation can happen

My favorite list of innovation areas comes from The Innovation Formula by Michael Robert and Alan Weiss. They identified 10 areas where innovative ideas can be found, if analyzed correctly. For each area, I provide a recent example, but try to come up with your own. 

• Unexpected Successes = PetSmart and similar companies 
• Unexpected Failures = Napalm
• Unexpected Events = Pandemic
• Process Weakness = Uber identified the weakness in hailing taxis
• Changes in Industry or Market Structure = GM using customer segmentation to overtake Ford
• High-Growth Business Areas = The surge of Baby Boomers who are retiring wealthy
• Converging Technologies = AI generative tech
• Demographic Changes = Declining birth rates and their second order effects
• Changes in Perception = Remote and hybrid work
• New Knowledge = mRNA vaccines

In your organization, you should explore multiple categories for innovation opportunities. You could do that regularly to ensure that you're not missing out on big trends and changes.

We start with the end in mind and work backward, letting go of present day constraints

If you start with the present, you're constrained by reality. Starbucks popularized their "third place" idea, but it no longer makes sense. If they focused on the present, they would try to make their in-store experience more appealing. Instead, they are working backward from their ideal future to arrive at a better take-out experience and even walk-throughs.

We track the speed of our decisions and work to improve it whenever relevant

I wrote about the magic of Decision Speed in the previous article I wrote for this publication. Faster decisions are almost always better.

We have a process that encourages ideas from anywhere in the organization

Management doesn't have a monopoly on good ideas. The doorman of a hotel has a great sense for how the customer experience could be improved. Are you doing enough to encourage these ideas?

We have a decision-making process that minimizes bias and politics

I once worked with a CEO who micro-managed everyone. Their decisions revolved around what would make him happy. You can see how this approach limits growth and innovation.

We encourage healthy debate of ideas regardless of role or status

Teams need to debate the status quo, profit centers and the future. If you don't do that, the competition and the market will be for you. Think of all the companies that never adjusted to the future and are now gone.

We provide sufficient support and resources to allow ideas to flourish

Strong ideas require strong action. If you want to foster innovation, you need to put resources behind it. That could mean a dedicated budget for trying things or just mean allowing employees discretionary times—like Google did with their 20% time.

We review successes for future process improvements while ignoring failures

I'm skeptical of post-mortems on failures. Gucci doesn't care why customers don't buy, but they do care why their best customers keep coming back. The reasons for failure can often be varied, but the reasons for success are limited and easily replicable.

We reward the behaviors that lead to innovation and not just the result

Innovation is about failure, and you need to reward those behaviors that will eventually lead to success. Consider creating awards for the best ideas, best execution and even best effort. Reward success and the roads that lead to it.

Don't think too hard about which area is the best for your business. Choose one and look for the 1% improvement. Once you're done, repeat that over and over again. Do it consistently, and you can completely change your culture and approach to innovation.

See also: When Regulation Offers Opportunity

Conclusion

Peter Drucker viewed innovation as a new way to view the universe. He envisioned a world where humans were no longer limited to scarce resources. Instead, they could literally create the future regardless of perceived limitations. 

Organizations don't need superhuman efforts to be innovative. They need small actions that can compound over time. It is much easier to adjust the course of a ship in small tweaks than to change directions drastically. The passengers aboard will also prefer avoiding brute directional changes. 

I leave you with this story: In 2013, the Defense Advanced Research Projects Agency (DARPA) gambled on a new approach for making vaccines. It awarded $25 million to a relative newcomer called Moderna, with the hopes that the small investment would one day pay off. Ten years later, Moderna's mRNA vaccines helped humanity move on from a global pandemic.

Breakthroughs don't always look like home runs. They are often just small steps in the right direction.

KEY TAKEAWAYS:

--Mobile teams asking for in-app feedback and acting on it are better able to keep customers active and engaged in their mobile channels. Closing the loop with dissatisfied customers is crucial.

--The success rate of selling new services to a customer you already have is 60% to 70% versus 5% to 20% for new customers. 

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In Alchemer's 2023 Mobile Customer Engagement Benchmark Report, insurance (auto, home, life, renters and pet) apps were much more popular than other finance apps (fintech and banking). At the same time, our data showed that consumers were happier with their fintech and banking apps than their insurance apps.

The report compiles data from more than 1.2 billion app installs from Alchemer Mobile customers and the consumers who use their apps. As it does every year, the report provides insights into mobile customer behavior and what gets people to act. 

Ratings, Reviews and the Differences Between iOS and Android

Across all industries, iOS users were generally happier with apps than Android users. According to Statista, as of March 2023, 83% of apps had a rating of less than three stars out of five. Research also shows that apps with four or more stars are 89% more likely to be downloaded.  

Retention

Insurance apps had lower-than-average retention rates in 2022. The category had 30-day retention of 51% (compared with 67% across all categories), 90-day retention of 41% (58% overall) and one-year retention of 24% (42% overall). However, when brands invested in asking consumers for reviews (such as with a Love Dialog – the “Do you love our app?” feature within Alchemer Mobile), 30-day retention grew to 75%.  

See also: 3 Great Apps for Insurance Agents

Customer Sentiment

Positive customer sentiment for insurance was 73%, above the overall benchmark of 64%. While the high cost of switching contributes to customer retention, it doesn’t keep sentiment high. Mobile teams asking for in-app feedback and acting on it are better able to keep customers active and engaged in their mobile channels, extending the brand’s reach and deepening each customer’s brand relationships. 

In-app Surveys

Insurance brands have room for improvement when conducting in-app surveys. Insurance apps’ average survey response rate was only 12% (the overall benchmark was 13%). When mobile teams used surveys presented with a Note – a message or invitation from the brand – to ensure customers were bought into the survey before presenting it, the results were impressive: The average response rate to Note-linked surveys was 56%. Insurance brands should experiment with various engagement strategies across target segments this year. Additionally, these brands will want to close the loop with people so customers know their feedback has been heard and changes to the app are being made as a result of their feedback. 

Engaging customers appears to be the key to success in 2023 and beyond. Engaging customers through surveys is one of the easiest ways to improve ratings and reviews. Insurance brands prompted 35% of customers for surveys and received an average interaction rate of 37%. When Notes were used to invite consumers to participate in a survey, the response rate jumped to 56%.

The Value of Risks

Even though Risks (those consumers who answered “No” to the question, “Do you love our app?”) may be unhappy with an app, retention in the insurance category is just a few percentage points better for Fans (those consumers who answer “Yes” to the question “Do you love our app?”). Retention was 77% for Fans versus 73% for Risks after 30 days. This means that even though people at risk are unhappy with the app, they’re invested in making the app work better for them or it’s too hard to switch insurance companies, so they continue to do business with the insurance company, even when they're not satisfied with the app. Consequently, closing the loop with these patrons not only lets them know you heard them, but when you make changes based on their feedback, they are much more likely to convert from Risks to Fans. 

Keep What You Have

Mobile app retention will remain a vital metric for mobile product owners and managers across the insurance industry. Because acquiring new customers can cost five times more than retaining existing ones, many mobile product owners are shifting their focus to keeping the customers they have. Additionally, the success rate of selling new services to a customer you already have is 60% to 70% versus 5% to 20% for new customers. 

Better understanding of customer churn will drive product owners and managers to use tools in 2023 to improve app retention. Features like Alchemer Mobile's Love Dialog and Fan Signals can help. Carefully monitoring customer emotion shifts will enable smart communicators to respond with messages, promotions and surveys to better seize the opportunity.

KEY TAKEAWAYS:

--Hackers have infiltrated companies' IT networks through their suppliers, such as in the infamous SolarWinds debacle.

--Quantum computing greatly increases the ability to break encryption, making attacks through the supply chain far easier.

--Even though quantum computing is still in its infancy, companies should take four steps now to prepare.

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Quantum computing holds so much potential for the world, but inevitably it will also introduce new risks to your business. Chief information security officers (CISOs) may have already started considering what quantum computing means for their own infrastructure, but one area is often overlooked: the supply chain.

Supply chain providers can unwittingly introduce security vulnerabilities into your organization. These vulnerabilities can be exploited by attackers and expose your company to security incidents, ransomware, an unwanted reputational hit or all the above and more. And the ability for quantum computers to break today's encryption algorithms that keep our data safe will only exacerbate the threat.

Sophisticated attackers, who have your company in their sights, may consider using a variety of attacks against your suppliers to compromise them, and ultimately you. Once your supplier is compromised, they become a steppingstone into your organization.

For example, a supply chain attack plan was used and executed to notorious perfection with the SolarWinds cyberattack. For those unfamiliar, in December 2020, it was discovered that the service supply chain of SolarWinds, a U.S.-based IT management software provider, had been compromised. SolarWinds is a key provider of software and services to companies around the globe. The attack resulted in the theft of sensitive data from numerous government agencies, technology companies and other organizations worldwide.

The SolarWinds compromise steps became a blueprint for other attacks:

• Initial Compromise: The attackers initially gained access to SolarWinds' software build environment, where they injected malicious code into a software update for the Orion platform, a widely used IT management tool.
• "Trojanized" Software Update: The malicious code, referred to as the Sunburst malware, was included in the Orion software update and was signed with legitimate SolarWinds digital certificates, making it difficult to detect.
• Distribution of Trojan Software: The compromised software update was then distributed to SolarWinds' customers. When the customers installed the update, Sunburst malware was installed on their systems, allowing the attackers to gain persistent access.
• Lateral Movement: Once inside the SolarWinds customers' compromised organizations, the attackers used various tactics, techniques and procedures (TTPs) to move laterally across their networks and gain access to sensitive data.

Quantum computers process information in a fundamentally different way than classical computers. Quantum computers use qubits, which can represent both 0 and 1 simultaneously, and, as a result, quantum computers' power grows exponentially in relation to the number of qubits linked together. The expectation is that, with this power, quantum computers will multiply the effectiveness of several supply chain attack vectors that are in use today, specifically:

• Brute Force Attacks: Quantum computers can perform certain types of calculations exponentially faster than today's classical computers. This means they can run possible combinations of keys or passwords in a fraction of the time it takes a classical computer, making the effort of "guessing the right combinations of keys or passwords until the correct one is found" much more efficient and effective.
• Password Attacks: Quantum computers can also be used to break password hashes, which are used to protect user passwords in many systems. Password hashes are vulnerable to quantum attacks using Grover's algorithm, which can be used to find the original password from the hash.

While quantum computers have the potential to break many of the currently used encryption algorithms, the technology is still in its infancy, and large-scale quantum computers capable of breaking encryption are not yet available. However, it is important for companies to be aware of the potential risks and to take steps today to protect against them, including adopting post-quantum cryptographic algorithms and taking a strong cybersecurity posture. This effort includes reviewing the software and services you consume within your company - your supply chain.

See also: The Challenge of Quantum Resilience

To that end, CISOs should consider four steps:

1. Risk Assessments: Do your third-party vendors and suppliers have a plan to address post-quantum cryptography (PQC)? Perhaps they have a robust and mature security program that includes PQC. Risk assessments should be comprehensive enough to understand security controls and the maturity of those controls. You are going to find companies at different stages on their cybersecurity journey, and understanding that maturity level as it relates to the services being consumed by your organization is one way to evaluate risk for your organization.
2. Supply Chain Requirements: Draw a line in the sand to mandate that the security and integrity of the products and services provided meet certain standards. For example, you can require a SOC 2 report from your provider. The SOC 2 report is centered on a service organization's IT controls. It's an attestation report in which certain internal controls have been designed and implemented, and those assertions are audited by a qualified CPA firm. This may not be readily achievable but should drive desired business behavior. The White House has issued executive orders that emulate this approach by directing new security standards with a focus on adoption of emerging technologies, including post-quantum encryption.
3. System and Communications Protection: Take the time to understand the what, where, how and by whom regarding a product or service being provided, and then discover the constituents in your organization using them. Knowing these potential threat vectors can better prepare defense and response models to protect the organization as post-quantum technologies advance.
4. Incident Response Planning: Create response plans that include procedures for security incidents involving third-party vendors or suppliers. Supplement your current plans by understanding how critical suppliers would notify you about a security incident. Streamline those communications with the right resources in your organization so you can respond quicker and take timely action against an attack.

With some effort and proper planning, you will be able to reduce the quantum risk to your organization and improve your ability to respond to a service supply chain threat. Providing context to the risk in your supply chain, including threats from post-quantum computing, demonstrates a high-level of acumen all CISOs should be delivering.

KEY TAKEAWAYS:

--In the last five years, calibrations during repairs for auto collisions have moved from the exception to almost the rule. Technology exists to ensure necessary calibrations are completed 100% of the time and are recorded for all relevant parties, to instill confidence in the repair.

--The wave we are witnessing with scanning is now seeing initial liftoff with calibrations. A more systematic look at the best way for each facility to execute and manage calibrations is needed to improve the customer experience, particularly with regard to quality and cycle time.

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Advanced Driver Assistance Systems (ADAS) have become an established feature in late-model vehicles, composed of an integrated suite of sensors, cameras, radars and more. Some of the earliest versions of ADAS appeared in the early 2000s with various forms of night vision, cruise control and lane departure warning systems.

The National Highway Traffic Safety Administration (NHTSA) led two major ADAS initiatives in the 2010s – the first, in 2014, required all new vehicles with a gross weight at or less than 10,000 pounds to include “rear visibility technology” by May 2018. This was followed in 2019 by the voluntary commitment by 20 automakers to equip all new vehicles with automatic emergency braking (AEB) by September 2022.

As the list of now-standard safety systems only continues to grow, it is complemented by a bevy of systems designed for the sole purpose of improving safety. (Figure 1)

Of course, as collisions continue to occur with great frequency, these systems must not only be repaired or replaced when damaged but also calibrated to ensure proper functionality before the vehicle returns to the road. ADAS adoption may have ramped up in a relatively short time, and corresponding collision repair needs may be a steeper ramp due, in part, to the sheer complexity of these new systems.

The Prevalence of Scans

Diagnostic scan procedures are becoming more frequent as part of the collision repair process. This is especially true for late-model vehicles that are more likely to come standard-equipped with ADAS technology and other safety features. Each quarter, the percentage of claims where at least one diagnostic scan is completed steadily rises. Only 3.3% of claims included a scan procedure in Q1 2017. By Q4 2022, 57% of claims included a scan, with vehicles less than four years old being scanned 65% of the time. (Figure 2)

Auto manufacturers either recommend or require that scans be completed pre-repair and following the completion of repairs. OEM-certified repair shops are required to perform scans with the OEM’s software, and many Multi-Shop Operators (MSOs) have a standard policy requiring pre- and post-repair scans, which means repairable vehicles should receive at least two scans per VIN number. This might explain why you’re more likely to see scans included in the first estimate rather than in subsequent supplements. (Figure 3)

In-process scans are an emerging trend and considered best practices among many of the industry’s top operators. In-process scans enable repair facilities to monitor the clearance, or even addition, of Diagnostic Trouble Codes (DTC) throughout the repair process, which can alleviate the need to backtrack and troubleshoot issues following the completion of repairs, not to mention ensuring thorough repairs and valuable repair cycle times, as well as other related costs.

CCC’s estimating data indicates that while an increased number of scans are included in the initial estimate, the subsequent scan to validate that error codes have been cleared or that additional trouble codes were initiated is largely absent from supplements.

See also: Auto Claims and Collision Repair: The Great Reset

Absence of a diagnostic scan charge doesn’t always mean that the vehicle wasn’t scanned. Based on anecdotal field evidence, an unknown percentage of vehicles were, in fact, scanned, yet the scan procedure was left off the estimate. This appears to be an opportunity for collision repair shops to not only highlight the thoroughness of their evaluation and work but also instill added confidence in consumers that their vehicle is safe to drive.

Absence of a corresponding scan and its documentation could be the types of red flags that supporting software will be able to identify in the future, thus reducing risks or omissions by shops.

Diagnostic scans are complemented by system calibrations, such as x/y-axis settings on cameras, horizontal/vertical specifications with radar and other procedures as documented by the manufacturer. Vehicle calibrations do not come without operational changes for repairers. Vehicle manufacturers have specific protocols that must be followed for systems to be calibrated to factory standards. These include:

• The space or environment where the calibration occurs
• Vehicle setup (including such details as a full tank of gas, specified tire pressure and an empty trunk)
• Camera aiming targets and stands (Figure 4)
• Specialized scan/calibration tools and
• The technical acumen needed to perform the calibration

Calibrations Catch Up

Today, repair shops without calibration capabilities must rely on outsourcing that work to local dealerships or competing repair shops or vendors. This practice adds to total cost, turn-around time and potentially diminished customer satisfaction, not to mention the added complexity in documenting work completed. It is apparent that scanning and calibrating vehicles is a major component in the continued evolution of repair shops and expansion of vehicle servicing capabilities.

The frequency of calibration procedures has not yet reached the level of scans but is steadily increasing. As of mid-year 2022, only 24% of current (or newer) and 19% of vehicles one to three years old had a calibration charge on the estimate. That number is sure to climb as ADAS feature become commonplace in the vehicle pool. (Figure 5)

Completing the proper diagnostics, determining which components might require calibration and reviewing the OEM repair procedures can help a repairer incorporate the calibration into the repair plan up front. These steps could help to avoid added costs and repair time identified later in supplemental phases of the repair process.

See also: Transforming Auto Claims Appraisals

Based on CCC estimate and supplement data, calibration line items are most likely to appear in the initial estimate or the first supplement. However, unlike scans, calibrations are showing up on a more frequent basis in supplements. (Figure 6)

The Future of Collision Repair

In the last five years, calibrations have moved from the exception to almost the rule. Technology exists to ensure necessary calibrations are completed 100% of the time and are recorded for all relevant parties, including the end-customer, as a means to instill confidence that a repair was completed.

The wave we are witnessing with scanning is now seeing initial liftoff with calibrations. A more systematic look at the best way for each facility to execute and manage calibrations is needed to improve the customer experience particularly with regard to quality and cycle time. Once again, adjustments in technology and process to ensure this transition happens efficiently are needed.

KEY TAKEAWAYS

--Organizations are increasingly turning to attack surface management (ASM), which can identify, monitor and mitigate vulnerabilities that can be targeted by malicious threat actors.

--Cyber threat intelligence (CTI) equips teams with invaluable insights into the motives and capabilities of cybercriminals, emerging tactics and intended targets. 

--But the potential threats that are identified can overwhelm security teams, so it's crucial to apply an ASM lens to refine CTI output and focus on those that are most relevant for an organization's unique attack surface.

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Cybersecurity Ventures estimates cybercrime will take a $10.5 trillion toll on the global economy by 2025. If it were measured as a country, the underground cybercriminal economy would be the third largest in the world after the U.S. and China. Amid the growing complexity and sophistication of malicious cyber threats, how can cyber defenders protect their organizations from falling victim to cyberattacks and keep their hard-earned profits from being diverted into the coffers of cybercriminal threat actors?

As the threat landscape evolves, organizations are increasingly turning to attack surface management (ASM) as an essential component of their cybersecurity program. ASM empowers security teams to identify, monitor and mitigate vulnerabilities across the attack surface -- including all known and unknown entry points -- that can be targeted by malicious threat actors.

While continuous monitoring of an organization's environment is critical to protecting its IT infrastructure, systems, and data -- ASM alone is not enough. Without real-time insight into the cybercriminal underground, ASM solutions cannot accurately identify at-risk assets or overall organizational threat exposure. This visibility gap hinders security teams from efficiently prioritizing the threats that pose the greatest risk -- costing more time and effort than resource-constrained teams can afford.

Like ASM, cyber threat intelligence (CTI) is considered indispensable within the organizational cyber defense arsenal. CTI equips teams with invaluable insights into the motives and capabilities of cybercriminal threat actors; emerging tactics, techniques and procedures (TTPs); and the intended targets for attacks. Many organizations have adeptly incorporated CTI within their cybersecurity programs to gain critical insights into their threat landscape and risk exposure.

However, when unfiltered and unscoped for organizational relevancy, the sheer volume of data can be overwhelming. Without the ability to refine this intelligence to focus on the threats and insights that matter most to their business, security teams are unable to cut through the noise -- potentially missing a looming threat that exposes their organization to attack.

By combining CTI with ASM, teams can optimize performance, with the internal context derived through ASM serving as a filtering mechanism for the vast volumes of threat intelligence data. In other words, applying an ASM lens to threat intelligence data refines CTI output to focus on the threats that hold the utmost relevance for the organization's unique attack surface.

When CTI and ASM work in unison, the combined solution empowers security teams to automate the monitoring and discovery of assets, facilitating the preemptive detection and mitigation of potential threats. This cohesive approach significantly strengthens the organization's security posture while optimizing the productivity of existing teams and resources.

ASM and CTI: A Cyber Defense Advantage

The benefits of integrating CTI with ASM go beyond protecting a company's financial position and brand. Consider the following additional areas where ASM and CTI deliver value:

Compliance: When combined with CTI, ASM solutions can help enterprises meet regulatory compliance requirements by delivering complete visibility of their risk exposure across network assets. This visibility enables governance, risk and compliance (GRC) teams to measure their compliance coverage, discover potential regulatory violations before attacks are carried out, undertake risk assessments and justify their decisions for vulnerability remediation.

Supply Chain Risk: ASM solutions equip security teams with the insight and automated capabilities to detect and manage all potential exposure points within the organizational network, including exposures through third-party partners and suppliers. By taking into account crucial internal context, such as the business criticality of each asset and real-time threat intelligence that indicates urgent risks, ASM enables security teams to swiftly prioritize remediation efforts and fortify the protection of both internal and external networks and assets.

Cloud Migration: Organizations' cloud migrations and rapid digitization efforts present significant challenges for organizations as they attempt to manage their growing attack surface and maintain robust cyber hygiene. By leveraging context-rich threat intelligence tailored for their unique organizational attack surface and environment, security teams can maintain constant vigilance in continually monitoring digital assets and addressing high-risk threats that target their cloud systems and applications.

Mergers and Acquisitions: In the context of M&A, the combined value of ASM and CTI extends to both pre-M&A cybersecurity due diligence, as well as post-M&A integration processes. During pre-merger cybersecurity due diligence, the integrated CTI and ASM solution enables security teams to thoroughly evaluate the cybersecurity posture of the target company. This assessment assists in identifying potential risks and exposures, allowing organizations to better assess the potential impact on sensitive data and overall risk posture before finalizing an acquisition or merger.

Following a merger or acquisition, the resulting expansion of their attack surface and heightened security risk pose a challenge in the post-M&A integration phase. By leveraging CTI and ASM, security teams gain complete visibility into known and unknown assets and the highest-risk threats targeting their systems and data. By adopting this combined approach, organizations can navigate the complex terrain of post-M&A cybersecurity, managing and mitigating threats to their systems and data.

See also: Why Hasn't Cyber Security Advanced?

Two Valuable Tools Are Even Better Together

While ASM and CTI play equally critical roles within any organization's cybersecurity arsenal, their true value can only be realized when they are harmoniously implemented together. By integrating ASM and CTI, security teams unlock a synergy that empowers them to identify, monitor and mitigate exposures across their unique attack surface and gain critical insights into the motives, capabilities and targets of cybercriminal threat actors.

This combination allows organizations to prioritize their efforts and focus on the threats and vulnerabilities that pose the most significant risk to their business, enabling them to defend against cyberattacks and protect their digital assets. The combined force of ASM and CTI serves as a force multiplier, strengthening the overall organizational cybersecurity posture and significantly reducing the risk of falling victim to malicious cybercriminals who seek to exploit the organization for financial gain.