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Four New Consumer-centric Business Models in Insurance

The insurance industry is changing and experts predict — nearly one-third of existing insurance models will disappear within this decade. The fierce competition, new opportunities with technologies like AI, and on top of that millennials’ changing preferences sum up to the call for more flexible and consumer-facing business models. Here are four new business models to set the insurance archetype.

Source: The Deloitte Global Millennial Survey 2019 

Social Good & Transparency as a Business Model

Currently, AI is being used to strengthen the capabilities and knowledge of insurers and not consumers, creating information asymmetry. But, the question is — for how long will the consumers accept being a victim of ignorance. 

A possible solution to this situation is bringing information transparency. It’s not like traditional insurers don’t share policy information with their customers. They do. However, lengthy policy documents and customers’ reliance on agents for information shadows the actual coverage, terms, etc. In a way, the information that customers receive becomes dependent on the agents’ knowledge and intentions.

Translating policy, terms and conditions documents into consumable bits of information with a clear distinction between what’s covered and what’s not will help in achieving transparency between insurers and customers.

For instance, Lemonade — the American Insurtech for renters and home insurance, disrupted the industry lately with their instant and transparent end-to-end insurance process. Their consumers are better aware of coverage and claims thanks to simplicity in the user experience. 

Moreover, Lemonade donates the unclaimed premiums to social causes their consumers care about. From its inception in 2015 to date, Lemonade has sold over 1.2 million policies, in complete transparency and all through their AI bot — Maya!

Nearly 46% of millennials are willing to make a positive impact on the society/community. Lemonade has partnered with 92 charities and has donated $8,46,849 from unclaimed premiums. Hence, the answer.

Similarly, Swedish InsurTech Hedvig has successfully deployed it’s “nice insurance” services, giving back 80% of the unclaimed premiums to charities chosen by the customers.

More insights on — millennials and their expectations from insurance ‘beyond’ convenience.

webinar: AI for data-driven Insurers

Join our Webinar — AI for Data-driven Insurers: Challenges, Opportunities & the Way Forward hosted by our CEO, Parag Sharma as he addresses Insurance business leaders and decision-makers on April 14, 2020.

B2B2C or API-based Model

When user acquisition is the top priority, B2B2C or API-based model comes into action. Also known as an open-source platform solution, this business model connects people and processes with technology infrastructure and assets to manage user interactions. 

In the API-based model, apart from traditional distribution channels, 3rd party apps also become a medium for customers to buy/access insurance policies. Automation plays a key role in this insurance model. Here, any other customer-centric digital application can install the API without manual/human intervention.

API-based Insurance Model Affinity Distribution Channel

For example, in January 2018, Allianz announced that it will offer parts of its Allianz Business System (ABS) to other insurance companies for free. Interested organizations can simply install the API (Application Programming Interface, which is nothing but a chunk of software that connects two different apps) and start selling Allianz policies to their customers.

Lemonade — after disrupting the insurance space through transparency, has now stepped into this model. In October 2017, the company launched its public API, allowing anyone to distribute Lemonade’s policies through their websites or apps.

“It takes years to pull together the licenses, capital, and technology needed to offer insurance instantly through an app, which is why it’s almost nonexistent. Today’s API launch changes that. Anyone with a slight familiarity with coding can now include these capabilities in their app, in a matter of hours.”

Shai Wininger, Co-founder, President & COO, Lemonade

P2P Insurance

Unclaimed premiums also contribute to conflicts between insurers and policyholders. What if a customer is not interested in donating to charity, unlike mentioned in the above case? 

Peer-to-Peer (P2P) insurance is perhaps an answer to eliminate premium settlement conflicts. It is also an emerging business model to access insurance coverage at lower costs than most of the traditional insurances. 

This insurance model pools the individuals who share at least one relation — friends, family, or interest (community/clubs) and it serves two-fold benefits-

  1. Every member knows other members, funds available, and claims initiated/processed. Therefore, irrespective of the information shared by the insurer, there’s a transparent collaboration among peers.
  2. Since the members know each other socially, there’s a negligible chance of fraudulent claims. For instance, in the US alone, insurance frauds amount to nearly $80 billion/year.

Also read – how behavioral psychology is fixing modern insurance claims

The notion of financial protection for the community has been prevalent in our societies since the 1600s. In the middle ages, the tradesmen followed the guild system (an association of craftsmen and merchants), where participants paid fees as a kind of insurance safety net. Though, the successful conceptualization of P2P insurance in the modern business models dates back to 2010 with German InsurTech — Friensurance. However, the P2P insurance model has credited the success to many more InsurTechs like Guevara, Axieme, TongJuBao (P2Pprotect), and PeerCover

Microinsurance

The greatest limiting factor for the success of microinsurance is distribution. For example, in the US, 18% of the premium represents the distribution cost, set aside marketing and advertising costs. Availability isn’t the issue for microinsurance. 

The new business model for microinsurance focuses on outreaching and distributing policies at scale. Workflow automation solutions like document processing, automated customer query resolution, etc. make microinsurance models more effective. 

  1. Aggregator model: Instead of traditional agents, retailers, utility or mobile network operators, etc. can be intermediaries for the distribution of microinsurance policies. They provide access to a very large consumer base and even more with free and freemium coverages. For example, Check24, a European aggregator together with HDI insurance developed AurumPROTECT that is available exclusively through aggregators channels. 
  2. Harnessing proxy insurance sales force: Banks have been the ideal partners to distribute microinsurance policies at scale for ages. But, for short-term policies, this is a good time to utilize the agents of other products to offer insurance as an ancillary product. For example, Ola — an Indian cab aggregator provides a number of travel-related microinsurance underwritten by Acko General Insurance. 

The Bottom Line

The effectiveness of each of these models drills down to the smart use of technology in their implementations. Moreover, most of these business models are automated, thus, eliminating additional human resources for implementations. For instance, in India, an agent can charge up to 20% of the premium amount as fees, which can reduce significantly if the distribution is automated. Investment in technology for automating operations is also worth it because it makes customer outreach simpler and faster. 

Also, read – 5 Front-office operations in Insurance you can automate with AI.

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Silent Drains: How Poor Data Observability Costs Enterprises Millions

Let’s rewind the clock for a moment. Thousands of years ago, humans had a simple way of keeping tabs on things—literally. They carved marks into clay tablets to track grain harvests or seal trade agreements. These ancient scribes kickstarted what would later become one of humanity’s greatest pursuits: organizing and understanding data. The journey of data began to take shape.

Now, here’s the kicker—we’ve gone from storing the data on clay to storing the data on the cloud, but one age-old problem still nags at us: How healthy is that data? Can we trust it?

Think about it. Records from centuries ago survived and still make sense today because someone cared enough to store them and keep them in good shape. That’s essentially what data observability does for our modern world. It’s like having a health monitor for your data systems, ensuring they’re reliable, accurate, and ready for action. And here are the times when data observability actually had more than a few wins in the real world and this is how it works

How Data Observability Works

Data observability involves monitoring, analyzing, and ensuring the health of your data systems in real-time. Here’s how it functions:

  1. Data Monitoring: Continuously tracks metrics like data volume, freshness, and schema consistency to spot anomalies early.
  2. Automated data Alerts: Notify teams of irregularities, such as unexpected data spikes or pipeline failures, before they escalate.
  3. Root Cause Analysis: Pinpoints the source of issues using lineage tracking, making problem-solving faster and more efficient.
  4. Proactive Maintenance: Predicts potential failures by analyzing historical trends, helping enterprises stay ahead of disruptions.
  5. Collaboration Tools: Bridges gaps between data engineering, analytics, and operations teams with a shared understanding of system health.

Real-World Wins with Data Observability

1. Preventing Retail Chaos

A global retailer was struggling with the complexities of scaling data operations across diverse regions, Faced with a vast and complex system, manual oversight became unsustainable. Rakuten provided data observability solutions by leveraging real-time monitoring and integrating ITSM solutions with a unified data health dashboard, the retailer was able to prevent costly downtime and ensure seamless data operations. The result? Enhanced data lineage tracking and reduced operational overhead.

2. Fixing Silent Pipeline Failures

Monte Carlo’s data observability solutions have saved organizations from silent data pipeline failures. For example, a Salesforce password expiry caused updates to stop in the salesforce_accounts_created table. Monte Carlo flagged the issue, allowing the team to resolve it before it caught the executive attention. Similarly, an authorization issue with Google Ads integrations was detected and fixed, avoiding significant data loss.

3. Forbes Optimizes Performance

To ensure its website performs optimally, Forbes turned to Datadog for data observability. Previously, siloed data and limited access slowed down troubleshooting. With Datadog, Forbes unified observability across teams, reducing homepage load times by 37% and maintaining operational efficiency during high-traffic events like Black Friday.

4. Lenovo Maintains Uptime

Lenovo leveraged observability, provided by Splunk, to monitor its infrastructure during critical periods. Despite a 300% increase in web traffic on Black Friday, Lenovo maintained 100% uptime and reduced mean time to resolution (MTTR) by 83%, ensuring a flawless user experience.

Why Every Enterprise Needs Data Observability Today

1. Prevent Costly Downtime

Data downtime can cost enterprises up to $9,000 per minute. Imagine a retail giant facing data pipeline failures during peak sales—inventory mismatches lead to missed opportunities and unhappy customers. Data observability proactively detects anomalies, like sudden drops in data volume, preventing disruptions before they escalate.

2. Boost Confidence in Data

Poor data quality costs the U.S. economy $3.1 trillion annually. For enterprises, accurate, observable data ensures reliable decision-making and better AI outcomes. For instance, an insurance company can avoid processing errors by identifying schema changes or inconsistencies in real-time.

3. Enhance Collaboration

When data pipelines fail, teams often waste hours diagnosing issues. Data observability simplifies this by providing clear insights into pipeline health, enabling seamless collaboration across data engineering, data analytics, and data operations teams. This reduces finger-pointing and accelerates problem-solving.

4. Stay Agile Amid Complexity

As enterprises scale, data sources multiply, making Data pipeline monitoring and data pipeline management more complex. Data observability acts as a compass, pinpointing where and why issues occur, allowing organizations to adapt quickly without compromising operational efficiency.

The Bigger Picture:

Are you relying on broken roads in your data metropolis, or are you ready to embrace a system that keeps your operations smooth and your outcomes predictable?

Just as humanity evolved from carving records on clay tablets to storing data in the cloud, the way we manage and interpret data must evolve too. Data observability is not just a tool for keeping your data clean; it’s a strategic necessity to future-proof your business in a world where insights are the cornerstone of success. 

At Mantra Labs, we understand this deeply. With our partnership with Rakuten, we empower enterprises with advanced data observability solutions tailored to their unique challenges. Let us help you turn your data into an invaluable asset that ensures smooth operations and drives impactful outcomes.

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