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The 7 InsurTech Trends That Matter for 2021

The COVID-19 pandemic has triggered structural changes that have forced insurance players to become more competitive than ever. The pandemic has proved to be a catalyst, nudging insurers to prioritize their focus on improving customer centricity, market agility, and business resilience.

As per a report by Accenture, almost 86% of insurers believe that they must innovate at an increasingly rapid pace to retain a competitive edge.

‘Insurtech’, short for ‘insurance technology’, is a term being widely used these days to talk about the new technologies bringing innovation in the insurance industry. The digital disruption caused by technology is transforming the way we protect ourselves financially.

In this article, let’s explore the top insurtech trends for 2021 that will pave the way for the future of insurance. 

  1. Data-backed personalization

Insurance companies are increasingly drifting towards collecting data to understand customer preferences better. Using data collected from IoT devices and smartphones, insurance companies are trying to deliver customized advice, the right products, and tailored pricing. 

Personalization enables exceptional experiences for customers while offering them products and services tailored to their specific needs. The idea is thus to put customers at the core of their operations.

Some examples of data-backed personalization include the following –

  • Reaching out to customers at the right time. This involves pitching to customers when they are thinking of buying insurance like while making high-value purchases, during financial planning, or during important life events.
  • Reaching out to customers through the right channel. This involves reaching out to customers through appropriate platforms like a website or mobile app.
  • Delivering the right products to specific individuals. This involves delivering products to customers based on their specific needs like reaching out with auto insurance to a customer who travels often.

Take the example of the financial services company United Services Automobile Association. The organization collects data from various social media platforms and uses advanced analytics to personalize its engagement with customers. The company advises customers when they are buying automotive insurance or are looking to purchase a vehicle. The company also provides its customers tailored mobile tools to help them manage and plan their finances.

  1. Usage-based policies

One of the biggest trends in the insurance industry is the growth of usage-based policies. In the coming year, we are going to hear a lot more about the ever-growing popularity of short and very-short term insurance that needs to be activated quickly.

We are going to see the rise of dedicated apps that allow easily activating policies based on usage needs. For instance, one would be able to take insurance for a sports event or a travel plan.

  1. Robotic and cognitive automation (R&CA)

Both robotic process automation (RPA) and cognitive automation (CA) represent two ends of the intelligent automation spectrum. At one end of the spectrum, there is RPA that uses easily programmable software bots to perform basic tasks. At the other end, we have cognitive automation that is capable of mimicking human thought and action. 

While RPA is the first step in the automation journey for any industry, cognitive automation is expected to help the industry adopt a more customer-centric approach by leveraging different algorithms and technologies (like NLP, text analytics, data mining, machine learning, etc.) to bring intelligence to information-intensive processes. R&CA, therefore, encompasses a potent mix of automated skills, primarily RPA and CA.

In the insurance industry, there are vast opportunities for R&CA to ease many processes. Some of its use cases in the insurance industry include –

  • Claims processing – R&CA can help insurance companies gather data from various sources and use it in centralized documents to quickly process claims. Automated claims processing can reduce manual work by almost 80% and significantly improve accuracy.
  • Policy management operations – R&CA can help automate insurance policy issuance, thus reducing the amount of time and manual work required for it. It can also help in making policy updates by using machine learning to extract inbound changes from policy holders from emails, voice transcripts, faxes, or other sources.
  • Data entry – It can be used for replacing the manual data entry jobs, hence saving a significant chunk of time. There are still many instances where data like quotations, insurance claims, etc. is entered manually into the system.
  • Regulatory compliance – R&CA can be key in helping companies improve regulatory compliance by eliminating the need for human personnel to go through many manual operations that can be prone to errors. It helps reduce the risks of compliance breach and ensures the accuracy of data. Some examples of manual work that R&CA can automate include name screening, compliance checking, client research, customer data validation, and regulatory reports generation, etc.
  • Underwriting – It involves gathering and analyzing information from multiple sources to determine and avoid risks associated with a policy like health, finance, duplicate policies, credit worthiness, etc. R&CA can automate the entire process and significantly speed up functions like data collection, loss assessment, and data pre-population, etc.
  1. Data-driven insurance

Although insurance has always been driven by data, new technology means that insurers are likely to benefit from big data. Using valuable data insights companies can customize insurance policies, minimize risks, and improve the accuracy of their calculations.

Here are a few use cases of how insurance companies use big data – 

  • Shaping policyholder behavior – IoT devices that monitor household risk help insurers shape the behavior of policyholders.
  • Gaining insights on customer healthcare – Medical insurance companies are drawing insights from big data to improve recommendations in terms of immediate and preventive care.
  • Pricing – Companies are using big data to accurately price each policyholder by comparing user behavior with a larger pool of data.
  1. Gamification

Gamification is turning out to be a very interesting and promising strategy that may get a lot more popular in 2021. It involves improving the digital customer experience by applying typical dynamics of gaming like obtaining prizes, bonuses, clearing levels, etc.

Gamification has shown promise in increasing engagement and building customer loyalty. For example, an Italian insurance company was able to observe a 57% increase in customers (joining the loyalty program) due to a digital game created by the company.

  1. Smart contracts

Smart contracts are lines of code that are stored on a blockchain. These are types of contracts that are capable of executing or enforcing themselves when certain predetermined conditions are met.

The market for smart contracts is expected to reach a valuation of $300 million by the end of 2023.

The insurance sector can benefit from smart contracts because these can emulate traditional legal documents while offering improved security and transparency. Moreover, these contracts are automated, so companies do not need to spend time processing paperwork or correcting errors in written documents.

  1. Other key trends

Some other key trends that may be relevant in 2021 include – 

  • Extended reality – Although it’s still in its early days, extended reality can benefit the insurance industry by making data gathering much safer, simpler, and faster by allowing risk assessment using 3D imaging.
  • Cybersecurity – Since insurance companies are migrating towards digital channels, they also become prone to cyberattacks. That is why cybersecurity will remain a trend in 2021 as well.
  • Cloud computing – The year 2021 could witness cloud computing become more essential than ever before. 
  • Self-service – It allows customers to have an alternative path to traditional agents as per their need and convenience, and thus looks to pick up pace in 2021.

Conclusion

It can be concluded that the pandemic has accelerated the shift towards digital in the insurance industry. As for the trends for 2021, there seems to be a general inclination towards personalization, data mining, and automation in the industry.

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What’s Next in Cloud Optimization? Can We Optimize Costs Without Sacrificing Performance?

Not too long ago, storing data meant dedicating an entire room to massive CPUs. Then came the era of personal computers, followed by external hard drives and USB sticks. Now, storage has become practically invisible, floating somewhere between data centers and, well, the clouds—probably the ones in the sky. Cloud computing continues to evolve, As cloud computing evolves, optimizing costs without sacrificing performance has become a real concern.  How can organizations truly future-proof their cloud strategy while reducing costs? Let’s explore new-age cloud optimization strategies in 2025 designed for maximum performance and cost efficiency.

Smarter Cloud Strategies: Cutting Costs While Boosting Performance

1. AI-Driven Cost Prediction and Auto-Optimization

When AI is doing everything else, why not let it take charge of cloud cost optimization too? Predictive analytics powered by AI can analyze usage trends and automatically scale resources before traffic spikes, preventing unnecessary over-provisioning. Cloud optimization tools like AWS Compute Optimizer and Google’s Active Assist are early versions of this trend.

  • How it Works: AI tools analyze real-time workload data and predict future cloud resource needs, automating provisioning and scaling decisions to minimize waste while maintaining performance.
  • Use case: Netflix optimizes cloud costs by using AI-driven auto-scaling to dynamically allocate resources based on streaming demand, reducing unnecessary expenditure while ensuring a smooth user experience.

2. Serverless and Function-as-a-Service (FaaS) Evolution

That seamless experience where everything just works the moment you need it—serverless computing is making cloud management feel exactly like that. Serverless computing eliminates idle resources, cutting down costs while boosting cloud performance. You only pay for the execution time of functions, making it a cost-effective cloud optimization technique.

  • How it works: Serverless computing platforms like AWS Lambda, Google Cloud Functions, and Azure Functions execute event-driven workloads, ensuring efficient cloud resource utilization while eliminating the need for constant infrastructure management.
  • Use case: Coca-Cola leveraged AWS Lambda for its vending machines, reducing backend infrastructure costs and improving operational efficiency by scaling automatically with demand. 

3. Decentralized Cloud Computing: Edge Computing for Cost Reduction

Why send all your data to the cloud when it can be processed right where it’s generated? Edge computing reduces data transfer costs and latency by handling workloads closer to the source. By distributing computing power across multiple edge nodes, companies can avoid expensive, centralized cloud processing and minimize data egress fees.

  • How it works: Companies deploy micro data centers and AI-powered edge devices to analyze data closer to the source, reducing dependency on cloud bandwidth and lowering operational costs.
  • Use case: Retail giant Walmart leverages edge computing to process in-store data locally, reducing latency in inventory management and enhancing customer experience while cutting cloud expenses.

4. Cloud Optimization with FinOps Culture

FinOps (Cloud Financial Operations) is a cloud cost management practice that enables organizations to optimize cloud costs while maintaining operational efficiency. By fostering collaboration between finance, operations, and engineering teams, FinOps ensures cloud investments align with business goals, improving ROI and reducing unnecessary expenses.

  • How it works: Companies implement FinOps platforms like Apptio Cloudability and CloudHealth to gain real-time insights, automate cost optimization, and enforce financial accountability across cloud operations.
  • Use case: Early adopters of FinOps were Adobe, which leveraged it to analyze cloud spending patterns and dynamically allocate resources, leading to significant cost savings while maintaining application performance. 

5. Storage Tiering with Intelligent Data Lifecycle Management

Not all data needs a VIP seat in high-performance storage. Intelligent data lifecycle management ensures frequently accessed data stays hot, while infrequently used data moves to cost-effective storage. Cloud-adjacent storage, where data is stored closer to compute resources but outside the primary cloud, is gaining traction as a cost-efficient alternative. By reducing egress fees and optimizing storage tiers, businesses can significantly cut expenses while maintaining performance.

  • How it’s being done: Companies use intelligent storage optimization tools like AWS S3 Intelligent-Tiering, Google Cloud Storage’s Autoclass, and cloud-adjacent storage solutions from providers like Equinix and Wasabi to reduce storage and data transfer costs.
  • Use case: Dropbox optimizes cloud storage costs by using multi-tiered storage systems, moving less-accessed files to cost-efficient storage while keeping frequently accessed data on high-speed servers. 

6. Quantum Cloud Computing: The Future-Proof Cost Gamechanger

Quantum computing sounds like sci-fi, but cloud providers like AWS Braket and Google Quantum AI are already offering early-stage access. While still evolving, quantum cloud computing has the potential to process vast datasets at lightning speed, dramatically cutting costs for complex computations. By solving problems that traditional computers take days or weeks to process, quantum computing reduces the need for excessive computing resources, slashing operational costs.

  • How it works: Cloud providers integrate quantum computing services with existing cloud infrastructure, allowing businesses to test and run quantum algorithms for complex problem-solving without massive upfront investments.
  • Use case: Daimler AG leverages quantum computing to optimize battery materials research, reducing R&D costs and accelerating EV development.

7. Sustainable Cloud Optimization: Green Computing Meets Cost Efficiency

Running workloads when renewable energy is at its peak isn’t just good for the planet—it’s good for your budget too. Sustainable cloud computing aligns operations with renewable energy cycles, reducing reliance on non-renewable sources and lowering overall operational costs.

  • How it works: Companies use carbon-aware cloud scheduling tools like Microsoft’s Emissions Impact Dashboard to track energy consumption and optimize workload placement based on sustainability goals.
  • Use case: Google Cloud shifts workloads to data centers powered by renewable energy during peak production hours, reducing carbon footprint and lowering energy expenses. 

The Next Frontier: Where Cloud Optimization is Headed

Cloud optimization in 2025 isn’t just about playing by the old rules. It’s about reimagining the game entirely. With AI-driven automation, serverless computing, edge computing, FinOps, quantum advancements, and sustainable cloud practices, businesses can achieve cost savings and high cloud performance like never before.

Organizations that embrace these innovations will not only optimize their cloud spend but also gain a competitive edge through improved efficiency, agility, and sustainability. The future of cloud computing in 2025 isn’t just about cost-cutting—it’s about making smarter, more strategic cloud investments.

At Mantra Labs, we specialize in AI-driven cloud solutions, helping businesses optimize cloud costs, improve performance, and stay ahead in an ever-evolving digital landscape. Let’s build a smarter, more cost-efficient cloud strategy together. Get in touch with us today!

Are you ready to make your cloud strategy smarter, cost-efficient, and future-ready with AI-driven, serverless, and sustainable innovations?

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