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10 Takeaways from the World InsurTech Report 2019

6 minutes, 6 seconds read

The insurance market dynamics are changing rapidly. While a connected ecosystem is the need of the time, agility and new business models are a way through. The current edition of the World InsurTech Report (WITR) emphasizes on developing synergies between Insurers and InsurTechs for the success of the future insurance marketplace. Here are 10 key takeaways from WITR 2019.

Insurance Business Process Improvements

Tech giants like Alibaba, Amazon, Apple, Facebook, and Google are entering the Insurance space with enormous customer data. Moreover, customers (nearly 30%) are responding positively to buying insurance products from BigTech firms, according to the World Insurance Report 2018. WITR proposes the following business process improvement for Insurers to remain market-fit.

#1 Partnerships with Insurtechs, Financial Institutions and Industry Players

90% of InsurTechs and 70% of Incumbents believe partnerships are crucial. And these partnerships are not confined only to the insurance sector. These can include collaboration with financial, technology, healthcare, travel, transportation, hospitality, retail, and more. 

Partnerships - world InsurTech Report 2019
The diagram illustrates the Insurance and InsurTechs’ level of willingness for partnerships – World InsurTech Report 2019

Baloise Insurance partnered with Swiss bank BLKB, and Swiss online insurance broker Anivo to develop a flexible and scalable digital insurance platform with B2C integration. The product released as Bancassurance 2.0 achieved a hit ratio of 50% for video-chat advisory sessions; more than 90% of customers rated the experience as good or very good. 

Partnerships can also bring compound insurance products, which otherwise seems impossible. For example, Swiss Re and French cybersecurity InsurTech firm OZON together, launched CyberSolution 360°. It is a risk management solution combining insurance and cyber-attack protection services for small and medium-sized enterprises.

#2 Adopting New Business Models

Not only Insurers, but also customers approve of new insurance models. For instance, 41% of customers are ready to consider usage-based insurance and 37% are willing to explore on-demand coverage. To meet the coverage gaps, offer convenience and personalization, Insurers are adopting the following new business models.

  1. Usage-based model for as-you-go coverage/premiums for a customer’s potential risky behaviour.
  2. On-demand model for cost-effective requirement-based coverage.
  3. Parametric insurance for covering uninsured risks, based on an objective-triggering event.
  4. Microinsurance services with low-premium packages.

#3 Aligning Strategies with the Future Insurance Marketplace

An insurance marketplace is a viable solution to support a broad spectrum of customer demands. It can also offer coverage for emerging risks and can deliver easy-access compound offerings from individual players of the insurance, manufacturing, and technology ecosystem.

For example, Friday, a Berlin-based startup, launched in 2017, offers digital automotive insurance with kilometre-based billing, flexible tenure, and paperless administration. With telematics support from BMW CarData, Automotive services from ATU, car-rental marketplace Drivy, and distribution channel from Friendsurance, Friday offers customer-centric insurance products.

“The insurance marketplace of the future will provide data and insights about customers that the industry never had before. This will allow firms to design a product closer to customers’ needs and, more importantly, offer them the product when they need it!”

Stephen Barnham, Asia CIO, MetLife

#4 Building an Integrated Ecosystem

As aggregators, OEMs (Original Equipment Manufacturers), policy management apps, and third parties enter the insurance value chain, an integrated insurance ecosystem can smoothen the overall functioning. 

For instance, digital integration with aggregators and third parties can broaden the Insurers’ distribution channel. Partnering with OEMs can help them with real-time customer data. Further, APIs, cloud-based storage, and blockchain can foster the insurance ecosystem with data security and transparency.

Technology Implementation Partners- World InsurTech Report 2019
An overview of digitally integrated ecosystem – World InsurTech Report 2019

#5 Being an Inventive Insurer

Inventive Insurers are the ones who have strategically updated their product portfolios, operating models, and distribution methods. They are realistic about their competencies. By identifying their distinct capabilities and partnering with other players to bridge their competency gap, Inventive Insurers can deliver an end-to-end product to the customers.

The World InsurTech Report 2019 defines the competencies of Inventive Insurers as follows –

  1. Capable of making business processes more intelligent, efficient, and effective using AI, automation, and analytics.
  2. Creating new scalable products with shorter development cycles.
  3. Enabling seamless integration with new data sources and distribution models.
  4. Offering value-added services to the customers.

Product Innovations

The tech-savvy customers are seeking easy-to-understand products with the facility of direct online purchases. Even leading Insurer like Berkshire Hathaway’s Insurance Group – BiBerk launched ‘THREE’ – only three pages long product covering workers’ compensation, liability, property, and auto to catch the pace. The drift is towards the following new insurance products.

#6 Bundling Financial and Non-financial offerings

An insurance package comprising both financial and non-financial products can expand an Insurer’s products portfolio, giving a competitive edge. It can also help in pitching new prospects. Bundling products and services will increase customer touchpoints and can help insurers identify their needs more effectively.

Bundling financial and non-financial services: World InsurTech Report 2019

For example, Homeflix insurance provides renters and homeowners insurance to its core. In addition to insurance coverage, it also offers concierge maintenance services like plumbing and electricity. The company also plans home delivery, babysitting, and cleaning services next.

#7 Tailored Products

Traditional insurance policies don’t fit today’s desire for add-on services, personalization, and flexible offerings. The World Insurance Report 2019 survey found that more than 75% of B2B customers and 85% of retail policyholders believe they’re not covered against the emerging risks.

Being aware of the need for customized products, 84% of Insurers and 80% of InsurTechs say they are focusing on “developing new offerings.”

#8 Products that Engage and Educate Customers

Gamification, video-chat sessions, and social media are promising channels for engaging with customers and educating them about risks and their need for coverage. Healthy interactions with customers through their preferred channels can boost sales.

“Insurers should focus on providing user friendly, transparent information via digital channels, allowing customers to make an informed decision. This will be critical not only for upselling, but also for attracting more new-generation customers, who are tech savvy and want to make faster product decisions.”

Jas Maggu, CEO, Galaxy.AI

Operational Improvements

For operational success- understanding customer preferences, conceptualizing new products portfolio, partnerships, and an effective go-to-market strategy is crucial. Fundamental shifts in the current operational models towards experience-driven solutions, strategic use of data, partnerships, and shared ownership of assets portray emerging trends. 

#9 Embracing Digital Agility

70% of insurers and 85% of InsurTechs believe a lack of technological readiness is a critical concern.

The more quickly Insurers implement initiatives, the closer they will be to achieve the digital maturity and hence actively participate in the connected ecosystem. The agile digital infrastructure demands real-time data gathering and analytics and automation of complex processes.

It will also lead to product agility. Insurers can offer new products at a faster pace and with reduced GTM (go-to-market) time, they can gain a competitive advantage. 

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.

#10 Automating Processes

Not only claims processing and underwriting, but much more insurance back and front-office operations can also be automated. Automation brings two-fold benefit to the insurers. One- mundane tasks are carried by machines, speeding the processes and freeing humans for sophisticated work. The other benefit lies in enhanced accuracy. 

For example, AIA Hongkong has improved claims processing time by 40% through AI-driven ICR techniques and intelligent process automation. 

Read claims automation case study: How AIA Hong Kong saves 60% through claims automation.

Deutsche Familienversicherung (DFV) provides a digital automated platform for property and supplementary health insurance. It can process the transactions in real-time enabling customers to file claims and receive feedback immediately. Moreover, policyholders can engage with the firm via several digital channels, including Amazon Alexa.

Source: World InsurTech Report 2019

InsurTech Report 2019: Summing-up

  1. Scope of business process improvements through partnerships, devising new business models, embracing insurance marketplace, building an integrated ecosystem, and being an inventive insurer.
  2. Introducing innovative products that are tailor-made and educate customers about potential risks; bundling financial and non-financial offerings.
  3. Operational improvement through automation and digital agility.

We’re AI-first products and solutions firm for the new-age digital insurer recognized among the InsurTech100 for pioneering the transformation of the global insurance industry. Drop us a line at hello@mantralabsglobal.com to know more about our offerings.

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Machines That Make Up Facts? Stopping AI Hallucinations with Reliable Systems

There was a time when people truly believed that humans only used 10% of their brains, so much so that it fueled Hollywood Movies and self-help personas promising untapped genius. The truth? Neuroscientists have long debunked this myth, proving that nearly all parts of our brain are active, even when we’re at rest. Now, imagine AI doing the same, providing information that is untrue, except unlike us, it doesn’t have a moment of self-doubt. That’s the bizarre and sometimes dangerous world of AI hallucinations.

AI hallucinations aren’t just funny errors; they’re a real and growing issue in AI-generated misinformation. So why do they happen, and how do we build reliable AI systems that don’t confidently mislead us? Let’s dive in.

Why Do AI Hallucinations Happen?

AI hallucinations happen when models generate errors due to incomplete, biased, or conflicting data. Other reasons include:

  • Human oversight: AI mirrors human biases and errors in training data, leading to AI’s false information
  • Lack of reasoning: Unlike humans, AI doesn’t “think” critically—it generates predictions based on patterns.

But beyond these, what if AI is too creative for its own good?

‘Creativity Gone Rogue’: When AI’s Imagination Runs Wild

AI doesn’t dream, but sometimes it gets ‘too creative’—spinning plausible-sounding stories that are basically AI-generated fake data with zero factual basis. Take the case of Meta’s Galactica, an AI model designed to generate scientific papers. It confidently fabricated entire studies with fake references, leading Meta to shut it down in three days.

This raises the question: Should AI be designed to be ‘less creative’ when AI trustworthiness matters?

The Overconfidence Problem

Ever heard the phrase, “Be confident, but not overconfident”? AI definitely hasn’t.

AI hallucinations happen because AI lacks self-doubt. When it doesn’t know something, it doesn’t hesitate—it just generates the most statistically probable answer. In one bizarre case, ChatGPT falsely accused a law professor of sexual harassment and even cited fake legal documents as proof.

Take the now-infamous case of Google’s Bard, which confidently claimed that the James Webb Space Telescope took the first-ever image of an exoplanet, a factually incorrect statement that went viral before Google had to step in and correct it.

There are more such multiple instances where AI hallucinations have led to Human hallucinations. Here are a few instances we faced.

When we tried the prompt of “Padmavaat according to the description of Malik Muhammad Jayasi-the writer ”

When we tried the prompt of “monkey to man evolution”

Now, if this is making you question your AI’s ability to get things right, then you should probably start looking have a checklist to check if your AI is reliable.

Before diving into solutions. Question your AI. If it can do these, maybe these will solve a bit of issues:

  • Can AI recognize its own mistakes?
  • What would “self-awareness” look like in AI without consciousness?
  • Are there techniques to make AI second-guess itself?
  • Can AI “consult an expert” before answering?

That might be just a checklist, but here are the strategies that make AI more reliable:

Strategies for Building Reliable AI

1. Neurosymbolic AI

It is a hybrid approach combining symbolic reasoning (logical rules) with deep learning to improve factual accuracy. IBM is pioneering this approach to build trustworthy AI systems that reason more like humans. For example, RAAPID’s solutions utilize this approach to transform clinical data into compliant, profitable risk adjustment, improving contextual understanding and reducing misdiagnoses.

2. Human-in-the-Loop Verification

Instead of random checks, AI can be trained to request human validation in critical areas. Companies like OpenAI and Google DeepMind are implementing real-time feedback loops where AI flags uncertain responses for review. A notable AI hallucination prevention use case is in medical AI, where human radiologists verify AI-detected anomalies in scans, improving diagnostic accuracy.

3. Truth Scoring Mechanism

IBM’s FactSheets AI assigns credibility scores to AI-generated content, ensuring more fact-based responses. This approach is already being used in financial risk assessment models, where AI outputs are ranked by reliability before human analysts review them.

4. AI ‘Memory’ for Context Awareness

Retrieval-Augmented Generation (RAG) allows AI to access verified sources before responding. This method is already being used by platforms like Bing AI, which cites sources instead of generating standalone answers. In legal tech, RAG-based models ensure AI-generated contracts reference actual legal precedents, reducing AI accuracy problems.

5. Red Teaming & Adversarial Testing

Companies like OpenAI and Google regularly use “red teaming”—pitting AI against expert testers who try to break its logic and expose weaknesses. This helps fine-tune AI models before public release. A practical AI reliability example is cybersecurity AI, where red teams simulate hacking attempts to uncover vulnerabilities before systems go live 

The Future: AI That Knows When to Say, “I Don’t Know”

One of the most important steps toward reliable AI is training models to recognize uncertainty. Instead of making up answers, AI should be able to respond with “I’m unsure” or direct users to validated sources. Google DeepMind’s Socratic AI model is experimenting with ways to embed self-doubt into AI.

Conclusion:

AI hallucinations aren’t just quirky mistakes—they’re a major roadblock in creating trustworthy AI systems. By blending techniques like neurosymbolic AI, human-in-the-loop verification, and retrieval-augmented generation, we can push AI toward greater accuracy and reliability.

But here’s the big question: Should AI always strive to be 100% factual, or does some level of ‘creative hallucination’ have its place? After all, some of the best innovations come from thinking outside the box—even if that box is built from AI-generated data and machine learning algorithms.

At Mantra Labs, we specialize in data-driven AI solutions designed to minimize hallucinations and maximize trust. Whether you’re developing AI-powered products or enhancing decision-making with machine learning, our expertise ensures your models provide accurate information, making life easier for humans

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