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The 5 hidden problems for Insurtech

For Insurance giants, the marketplace is changing. For young insurtechs trying to displace these giants and keen on disrupting the landscape altogether; the next big market is becoming plain and obvious: Millenials and the generations that will follow them.

A new wave of AI-driven technologies is making subtle changes to the way young people are re-thinking the whole “Why do I need insurance again?” decision.

Millennials —  are most likely to purchase insurance through an app with a few taps on their smartphones — are driving less frequently than previous generations — thereby creating a market for lower cost, pay-per-mile auto insurance. 

Yet, despite the proclivity of this demographic to stay away from ownership (and, with that, the need for coverage), they do own assets that they want insured. Insurtech is well poised above all else, to satisfy their unique coverage needs.

A majority of the World’s insurance purchases are done physically (in-person), while only a small portion of sales comes from either the web or mobile – yes, even in 2019 and for the foreseeable future, that remains true.

The Hidden Problems of Insurtech


The ‘Insurtech’ model can be broken down into — those that operate at the broker-level, those that offer insurance services/products or product-level, and those that have a hybrid approach (such as peer-to-peer insurtech) that has an insurance product with a strongly linked brokerage aspect to it. Here is a look at the challenges that surround young companies operating in these models.

#1 Partnerships are stark & sparse


For existing incumbents, the advantage is obvious — seize on the hype created by insurtech upstarts, who are capturing previously untapped audiences towards new & innovative products. 

Also, read – Top Innovative Insurance Products of 2019

Large insurers will even venture into setting up their own start-ups; or invest in new technologies within their own business.  However, despite the mutual benefit-for-all reasoning behind partnerships, these are spread thin across most regions.

Without the support of a large insurer or two, insurtechs will find it hard to manage the unit economics of the policies they sell; which brings to question the sustainability of this model for scaling.

#2 Innovation beyond downstream distribution


Insurtechs that have either chosen not to partner/ not managed to attract the right partnership with large insurers — arguably face greater challenges. Most of the insurtech-startup funding pool has moved into distribution, and rightfully so.

Distribution has brought about long-awaited changes to delivering new products and customer experiences — aspects of the business that Insurance giants consistently struggle to produce in.

Insurance, however, has four fundamental units: the underwriting of insurance, claims servicing, regulatory overhead, and distribution (actual selling).

As these insurtechs grow, the looming question remains: how will they manage the other parts of insurance, if all the money has gone into refining one stream?. For example, are they sufficiently capable of handling claims and underwriting as the business scales? These questions are yet to be answered, and the models are yet to be proven.

#3 Frequent changes to the legal & regulatory framework


“Not all insurtech businesses qualify as insurance companies” since they depend on the type and extent of the services provided. A regulatory distinction is essential to separate them — without which a reliable guarantee cannot be given to customers in the event of a loss.

Legal and regulatory commitments change with region and country, hence insurtechs are typically unsuitable for covering potentially large losses. 

#4 Attitudes of the next generation


Younger generations are less likely than previous ones to pay heed to the importance of insurance. They simply do not see it as an important financial instrument. These challenges have plagued the industry for several decades, and insurtechs will have to assume this challenge for themselves as well. At its core, insurance is a hard product to sell, no matter how good the package looks.

Technology in insurance and advancements to customer experiences are making the furthest inroads, the industry has ever seen. Yet, low insurance penetration levels are still an indicator of how difficult it is for insurtechs to find adoption among the masses.

#5 Intelligent Customer-Experiences


Thanks to Big Tech (like Google, Amazon, Apple, etc.) — customer experience has evolved rapidly. Digital products and services are now highly customisable and can be delivered at a high quality consistently. Yet, it has taken until now for the same to slowly seep into insurance. Sensing a huge opportunity, Big Tech has started moving into the insurance on-demand space, which has forced the larger insurers to adapt quickly. 

Insurtechs, who are by-default product- and tech- first, tend to fare better than their much larger counterparts. Yet challenges with data will persist. Just how well insurtechs are using data, remains to be seen. 

Will technology in insurance have to face a test of time?

The use of exceptional data and advanced analytics can help link the behavioural characteristics of customers and their spending habits – true fodder for machine learning models. How will insurtechs leverage useful insights to tackle age-old insurance selling challenges, such as intention to abandon, the propensity to purchase, or the right communication channel — will be the true test of competitive advantage.

Mantra Labs is a deep-tech advisor & consultant for young Insurtechs helping them create a strategic vision and an agile evolution road-map that addresses challenges from scaling to delivery. To learn more, reach out to us at hello@mantralabsglobal.com.

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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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