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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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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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