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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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Smart Manufacturing Dashboards: A Real-Time Guide for Data-Driven Ops

Smart Manufacturing starts with real-time visibility.

Manufacturing companies today generate data by the second through sensors, machines, ERP systems, and MES platforms. But without real-time insights, even the most advanced production lines are essentially flying blind.

Manufacturers are implementing real-time dashboards that serve as control towers for their daily operations, enabling them to shift from reactive to proactive decision-making. These tools are essential to the evolution of Smart Manufacturing, where connected systems, automation, and intelligent analytics come together to drive measurable impact.

Data is available, but what’s missing is timely action.

For many plant leaders and COOs, one challenge persists: operational data is dispersed throughout systems, delayed, or hidden in spreadsheets. And this delay turns into a liability.

Real-time dashboards help uncover critical answers:

  • What caused downtime during last night’s shift?
  • Was there a delay in maintenance response?
  • Did a specific inventory threshold trigger a quality issue?

By converting raw inputs into real-time manufacturing analytics, dashboards make operational intelligence accessible to operators, supervisors, and leadership alike, enabling teams to anticipate problems rather than react to them.

1. Why Static Reports Fall Short

  • Reports often arrive late—after downtime, delays, or defects have occurred.
  • Disconnected data across ERP, MES, and sensors limits cross-functional insights.
  • Static formats lack embedded logic for proactive decision support.

2. What Real-Time Dashboards Enable

Line performance and downtime trends
Track OEE in real time and identify underperforming lines.

Predictive maintenance alerts
Utilize historical and sensor data to identify potential part failures in advance.

Inventory heat maps & reorder thresholds
Anticipate stockouts or overstocks based on dynamic reorder points.

Quality metrics linked to operator actions
Isolate shifts or procedures correlated with spikes in defects or rework.

These insights allow production teams to drive day-to-day operations in line with Smart Manufacturing principles.

3. Dashboards That Drive Action

Role-based dashboards
Dashboards can be configured for machine operators, shift supervisors, and plant managers, each with a tailored view of KPIs.

Embedded alerts and nudges
Real-time prompts, like “Line 4 below efficiency threshold for 15+ minutes,” reduce response times and minimize disruptions.

Cross-functional drill-downs
Teams can identify root causes more quickly because users can move from plant-wide overviews to detailed machine-level data in seconds.

4. What Powers These Dashboards

Data lakehouse integration
Unified access to ERP, MES, IoT sensor, and QA systems—ensuring reliable and timely manufacturing analytics.

ETL pipelines
Real-time data ingestion from high-frequency sources with minimal latency.

Visualization tools
Custom builds using Power BI, or customized solutions designed for frontline usability and operational impact.

Smart Manufacturing in Action: Reducing Market Response Time from 48 Hours to 30 Minutes

Mantra Labs partnered with a North American die-casting manufacturer to unify its operational data into a real-time dashboard. Fragmented data, manual reporting, delayed pricing decisions, and inconsistent data quality hindered operational efficiency and strategic decision-making.

Tech Enablement:

  • Centralized Data Hub with real-time access to critical business insights.
  • Automated report generation with data ingestion and processing.
  • Accurate price modeling with real-time visibility into metal price trends, cost impacts, and customer-specific pricing scenarios. 
  • Proactive market analysis with intuitive Power BI dashboards and reports.

Business Outcomes:

  • Faster response to machine alerts
  • Quality incidents traced to specific operator workflows
  • 4X faster access to insights led to improved inventory optimization.

As this case shows, real-time dashboards are not just operational tools—they’re strategic enablers. 

(Learn More: Powering the Future of Metal Manufacturing with Data Engineering)

Key Takeaways: Smart Manufacturing Dashboards at a Glance

AspectWhat You Should Know
1. Why Static Reports Fall ShortDelayed insights after issues occur
Disconnected systems (ERP, MES, sensors)
No real-time alerts or embedded decision logic
2. What Real-Time Dashboards EnableTrack OEE and downtime in real-time
Predictive maintenance using sensor data
Dynamic inventory heat maps
Quality linked to operators
3. Dashboards That Drive ActionRole-based views (operator to CEO)
Embedded alerts like “Line 4 down for 15+ mins”
Drilldowns from plant-level to machine-level
4. What Powers These DashboardsUnified Data Lakehouse (ERP + IoT + MES)
Real-time ETL pipelines
Power BI or custom dashboards built for frontline usability

Conclusion

Smart Manufacturing dashboards aren’t just analytics tools—they’re productivity engines. Dashboards that deliver real-time insight empower frontline teams to make faster, better decisions—whether it’s adjusting production schedules, triggering preventive maintenance, or responding to inventory fluctuations.

Explore how Mantra Labs can help you unlock operations intelligence that’s actually usable.

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