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The Cognitive Cloud Insurer is Next

4 minutes, 8 seconds read

Today’s Insurance enterprise is moving away from the all-too-familiar ‘reactive-only’ approach to a new predictive-first model. The sector is seeing dramatic changes, as we enter the fourth Industrial Revolution (Industry 4.0) — or The Connected Age. Digital businesses are gradually realizing the limitations of human and machine systems without any real intelligence or computing power behind it. Between human prone errors and the scalability challenges of traditional technologies — a new mechanism is required to learn and adapt better. 

Enter Cognitive Computing. But what is it?

The short answer is — it has everything to do with interpreting data. Big Data, to be precise. This activity is particularly hard because most of the data in use remains unstructured. In insurance, for example, nearly 90% of carrier data is disparate or partially structured as text & image data, in varying formats. With cognitive computing, data can be made meaningful and then used to derive new insights for future use.


To achieve this, ‘Cognitive Systems’ leverage the use of distinct technologies such as natural language processing, machine learning and automated reasoning. It helps in processing great volumes of complex data and can aid faster & accurate decision-making by breaking down the complexities in big data. When done right, a cognitive computing system can comprehend, reason, learn and interact with humans naturally ultimately enhancing the enterprise’s digital intelligence capabilities.

Another aspect of cognitive computing is the ‘Cloud’ advantage. Cloud computing is not new, however, when fitted with a cognitive solution — it can foster dramatic agility to organizational workflows. 

For the digital insurer, this means that all aspects of the value chain can be transformed, ushering in a new business model that seamlessly engages with both customers and prospects in near-real-time, at all times. 

Also read – How does XaaS help your business?

The Cognitive Insurance Transformation Journey

Transitioning from a digital to a cognitive business enabled by the ‘cloud’ has a clear business objective behind it — evolve the model to improve profitability. The addition of the cognitive component allows smart systems to free up critical manned resources and drives greater (STP) straight-through processing. 

Take ‘underwriting’ for example, which is an area of insurance that necessitates looking at  vast heaps of unstructured data. Without the supporting information, the risk cannot be precisely measured or priced. 

Accelerating data analysis from historical information can improve the underwriter’s efficiency in the manufacture of meaningful and personalised insurance products, within short turn-around time. This is how insurance carriers will stay their competitive advantage when vying for the wallet-share and mind-share of tomorrow’s customer.

The Cognitive Insurer in cloud is Next

Source: The Cognitive Insurance Value Chain

Yet, the redesign of the underwriting process is only one of many insurance processes that has the potential for Cognitive enhancement. The number of connected things will grow to 25 billion by 2021, which will increase the amount of data. Insurance data alone is expected to grow by 94%. Other parts of the value chain like claims processing, new business and underwriting, rapid customer onboarding, rules-based processes and contract validation are also experiencing cognitive upgradation.

In the past few years, the number of cognitive projects in insurance is on the rise. Carriers are running pilots, testing and validating the right use cases to invest in. For instance, Australian Insurer, Suncorp used IBM’s Watson for ratifying a specific use case — determining who is liable for causing a motor accident, by studying 15,000 historical records of de-personalised claim files.

The Cognitive Insurance process and application

Source: CognitiveScale

Intelligent and cognitive systems like these can do a lot more. From cognitive claims to cognitive chatbots — AI and Machine Learning are behind new behaviour-based, pay-as-you-use products in insurance. Automated post-hospitalisation claims, Motor damage estimation using advanced image recognition, Cognitive mail handling through intention analysis, etc. among others are just a few examples of AI solutions being deployed by Insurers, who are evolving their business models along their transformation journey.

Our own SaaS-based intelligent platform built for improving insurer workflows, FlowMagic takes advantage of cloud-based capabilities to enhance business automation. The intuitive Visual Platform uses AI-powered applications that are easily configurable requiring zero-coding effort, while the jobs can be visually monitored continuously to give real-time decision-ready insights.

Cognitive-Insurance-Ecosystem-Flowmagic

FlowMagic — Visual AI Platform for Insurer Workflows

Here’s a simple 3 step formula for a successful cognitive cloud transformation journey:


1. Identify (internally) use cases with a potential for a high degree of market disruption.

2. Validate (both internally & externally) the use cases through small-scale pilot deployments.

3. Define areas in your operational value chain ripe for transformation, that will enable new processes, engagements and business models through it.

By 2020, 25% of customer service and support operations will integrate with cognitive cloud-enabled chatbots to deliver natural, conversational guidance to users. Solutions like these have proven demonstrable ROI in both front & back-office operations, creating over 80% FTE savings for the enterprise.

Mantra Labs is an InsurTech100 company, that helps digital insurance enterprises enhance agility and operational efficiency through new Cognitive Cloud capabilities. To know how, reach out to us at hello@mantralabsglobal.com

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Why Netflix Broke Itself: Was It Success Rewritten Through Platform Engineering?

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Let’s take a trip back in time—2008. Netflix was nothing like the media juggernaut it is today. Back then, they were a DVD-rental-by-mail service trying to go digital. But here’s the kicker: they hit a major pitfall. The internet was booming, and people were binge-watching shows like never before, but Netflix’s infrastructure couldn’t handle the load. Their single, massive system—what techies call a “monolith”—was creaking under pressure. Slow load times and buffering wheels plagued the experience, a nightmare for any platform or app development company trying to scale

That’s when Netflix decided to do something wild—they broke their monolith into smaller pieces. It was microservices, the tech equivalent of turning one giant pizza into bite-sized slices. Instead of one colossal system doing everything from streaming to recommendations, each piece of Netflix’s architecture became a specialist—one service handled streaming, another handled recommendations, another managed user data, and so on.

But microservices alone weren’t enough. What if one slice of pizza burns? Would the rest of the meal be ruined? Netflix wasn’t about to let a burnt crust take down the whole operation. That’s when they introduced the Circuit Breaker Pattern—just like a home electrical circuit that prevents a total blackout when one fuse blows. Their famous Hystrix tool allowed services to fail without taking down the entire platform. 

Fast-forward to today: Netflix isn’t just serving you movie marathons, it’s a digital powerhouse, an icon in platform engineering; it’s deploying new code thousands of times per day without breaking a sweat. They handle 208 million subscribers streaming over 1 billion hours of content every week. Trends in Platform engineering transformed Netflix into an application dev platform with self-service capabilities, supporting app developers and fostering a culture of continuous deployment.

Did Netflix bring order to chaos?

Netflix didn’t just solve its own problem. They blazed the trail for a movement: platform engineering. Now, every company wants a piece of that action. What Netflix did was essentially build an internal platform that developers could innovate without dealing with infrastructure headaches, a dream scenario for any application developer or app development company seeking seamless workflows.

And it’s not just for the big players like Netflix anymore. Across industries, companies are using platform engineering to create Internal Developer Platforms (IDPs)—one-stop shops for mobile application developers to create, test, and deploy apps without waiting on traditional IT. According to Gartner, 80% of organizations will adopt platform engineering by 2025 because it makes everything faster and more efficient, a game-changer for any mobile app developer or development software firm.

All anybody has to do is to make sure the tools are actually connected and working together. To make the most of it. That’s where modern trends like self-service platforms and composable architectures come in. You build, you scale, you innovate.achieving what mobile app dev and web-based development needs And all without breaking a sweat.

Source: getport.io

Is Mantra Labs Redefining Platform Engineering?

We didn’t just learn from Netflix’s playbook; we’re writing our own chapters in platform engineering. One example of this? Our work with one of India’s leading private-sector general insurance companies.

Their existing DevOps system was like Netflix’s old monolith: complex, clunky, and slowing them down. Multiple teams, diverse workflows, and a lack of standardization were crippling their ability to innovate. Worse yet, they were stuck in a ticket-driven approach, which led to reactive fixes rather than proactive growth. Observability gaps meant they were often solving the wrong problems, without any real insight into what was happening under the hood.

That’s where Mantra Labs stepped in. Mantra Labs brought in the pillars of platform engineering:

Standardization: We unified their workflows, creating a single source of truth for teams across the board.

Customization:  Our tailored platform engineering approach addressed the unique demands of their various application development teams.

Traceability: With better observability tools, they could now track their workflows, giving them real-time insights into system health and potential bottlenecks—an essential feature for web and app development and agile software development.

We didn’t just slap a band-aid on the problem; we overhauled their entire infrastructure. By centralizing infrastructure management and removing the ticket-driven chaos, we gave them a self-service platform—where teams could deploy new code without waiting in line. The results? Faster workflows, better adoption of tools, and an infrastructure ready for future growth.

But we didn’t stop there. We solved the critical observability gaps—providing real-time data that helped the insurance giant avoid potential pitfalls before they happened. With our approach, they no longer had to “hope” that things would go right. They could see it happening in real-time which is a major advantage in cross-platform mobile application development and cloud-based web hosting.

The Future of Platform Engineering: What’s Next?

As we look forward, platform engineering will continue to drive innovation, enabling companies to build scalable, resilient systems that adapt to future challenges—whether it’s AI-driven automation or self-healing platforms.

If you’re ready to make the leap into platform engineering, Mantra Labs is here to guide you. Whether you’re aiming for smoother workflows, enhanced observability, or scalable infrastructure, we’ve got the tools and expertise to get you there.

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