The technology is overpowering the traditional business models, and each sector is gradually going the digital way to meet the evolving customer expectations. Life insurance is a sector that is still in the nascent stages of digitization due to the amount of complexity and sensitivity it involves. Insurance startups are hell-bent on leveraging the new technologies to remodel the design and delivery of the life insurance.

Insurance startups are making use of analytic and digital tools to develop life insurance products that are flexible and fast to deliver. The goal of these InsurTech innovations is to decrease the total time for the application process and create a comfortable setting for the customers. The key to implementation of these innovations is that they should be compliant with the insurance law and regulations.

The InsurTech innovations for life insurance will include:

1. RPA and AI for core processes:

The automation of core processes is essential as it helps to speed up the processing of the policies and servicing customer requests. RPA (Robotic process automation) and AI work together to process the structured and unstructured data respectively. AI backed Insurance chatbots can help the consumers to chat and converse with their providers and get solutions to their queries immediately. InsurTech as a service need to handle large volumes of data obtained from connected devices like the social media and other resources which can be easily done through automation. As there is a lot of paperwork involved with life insurance policies, automation is a great way to avoid human errors and save some time.

2. Smart contracts:

Blockchain has deeply impacted the technology sector and the blockchain based smart contracts are a game changer in automating the life insurance policy claims. It works on the concept of the decentralized ledger where each customer has a copy of the ledger, and he can commit to a transaction independently. The smart contract can be processed automatically based on a set of pre-defined conditions. It is a great way to enhance the operational efficiency and process the claims quickly.

3. Predictive analysis:

Predictive analysis plays an important role to analyze the needs of the current as well as future customers. Life insurance companies can make use of the actionable analysis to find the past as well as the real-time trends and accordingly plan out their strategy. It helps to design personalized offerings based on the inputs from the customers. InsurTech consulting services need this information for providing meaning consultancy to their customers.

4. Advanced analytics for fraud prevention:

The reports suggest that insurance companies suffer losses of at least 3% due to fraudulent activities. So, the insurance companies are determined to leverage the benefits of advanced analytics that is backed by AI for a more trusted, reliable and transparent environment with their customers. The customer data from various resources like mobile devices, social media channels are analyzed and monitored continuously for any behavioral patterns anomaly.

5. Cloud technology:

Life insurers are also leveraging the capabilities of the cloud for it is capable of handling huge volumes of data from varying sources like the wearables or the social media channels or any other electronic devices. The cloud is also beneficial when it comes to saving IT deployment costs due to the inflexibility of IT infrastructure, in cases of underuse and under capacity.

Technical innovation in the field of life insurance has just started to evolve. The above-mentioned technical aspects will form the foundation of InsurTech innovation and will even go far beyond it in the coming future. We can wait and see how it will transform the life insurance sector in the near future.

Know the Mantra Labs capabilities in InsurTech and reach out to us for any query.

References:

https://www.jdsupra.com/legalnews/insurtech-innovations-in-life-insurance-69458/

https://www.capgemini.com/wp-content/uploads/2017/12/life-insurance-top10-trends-2018.pdf

https://www.capgemini.com/2018/06/insurtech-opens-new-life-insurance-frontiers/

The Future-Ready Factory: The Power of Predictive Analytics in Manufacturing

By : Sampada S M

Published on Mar 6, 2025

Updated on Mar 13, 2025

Manufacturing Predictive Analytics

In 1989, a missing $0.50 bolt led to the mid-air explosion of United Airlines Flight 232. The smallest oversight in manufacturing can set off a chain reaction of failures. Now, imagine a factory floor where thousands of components must function flawlessly—what happens if one critical part is about to fail but goes unnoticed? Predictive analytics in manufacturing ensures these unseen risks don’t turn into catastrophic failures by providing foresight into potential breakdowns, supply chain risk analytics, and demand fluctuations—allowing manufacturers to act before issues escalate into costly problems.

Industrial predictive analytics involves using data analysis and machine learning in manufacturing to identify patterns and predict future events related to production processes. By combining historical data, machine learning, and statistical models, manufacturers can derive valuable insights that help them take proactive measures before problems arise.

Beyond just improving efficiency, predictive maintenance in manufacturing is the foundation of proactive risk management, helping manufacturers prevent costly downtime, safety hazards, and supply chain disruptions. By leveraging vast amounts of data, predictive analytics enables manufacturers to anticipate machine failures, optimize production schedules, and enhance overall operational resilience.

But here’s the catch, models that predict failures today might not be necessarily effective tomorrow. And that’s where the real challenge begins.

Why Predictive Analytics Models Need Retraining?

Predictive analytics in manufacturing relies on historical data and machine learning to foresee potential failures. However, manufacturing environments are dynamic, machines degrade, processes evolve, supply chains shift, and external forces such as weather and geopolitics play a bigger role than ever before.

Without continuous model retraining, predictive models lose their accuracy. A recent study found that 91% of data-driven manufacturing models degrade over time due to data drift, requiring periodic updates to remain effective. Manufacturers relying on outdated models risk making decisions based on obsolete insights, potentially leading to catastrophic failures.

The key is in retraining models with the right data, data that reflects not just what has happened but what could happen next. This is where integrating external data sources becomes crucial.

Is Integrating External Data Sources Crucial?

Traditional smart manufacturing solutions primarily analyze in-house data: machine performance metrics, maintenance logs, and operational statistics. While valuable, this approach is limited. The real breakthroughs happen when manufacturers incorporate external data sources into their predictive models:

Weather Patterns: Extreme weather conditions have caused billions in manufacturing risk management losses. For example, the 2021 Texas power crisis disrupted semiconductor production globally. By integrating weather data, manufacturers can anticipate environmental impacts and adjust operations accordingly.
Market Trends: Consumer demand fluctuations impact inventory and supply chains. By leveraging market data, manufacturers can avoid overproduction or stock shortages, optimizing costs and efficiency.
Geopolitical Insights: Trade wars, regulatory shifts, and regional conflicts directly impact supply chains. Supply chain risk analytics combined with geopolitical intelligence helps manufacturers foresee disruptions and diversify sourcing strategies proactively.

One such instance is how Mantra Labs helped a telecom company optimize its network by integrating both external and internal data sources. By leveraging external data such as radio site conditions and traffic patterns along with internal performance reports, the company was able to predict future traffic growth and ensure seamless network performance.

The Role of Edge Computing and Real-Time AI

Having the right data is one thing; acting on it in real-time is another. Edge computing in manufacturing processes, data at the source, within the factory floor, eliminating delays and enabling instant decision-making. This is particularly critical for:

Hazardous Material Monitoring: Factories dealing with volatile chemicals can detect leaks instantly, preventing disasters.
Supply Chain Optimization: Real-time AI can reroute shipments based on live geopolitical updates, avoiding costly delays.
Energy Efficiency: Smart grids can dynamically adjust power consumption based on market demand, reducing waste.

Conclusion:

As crucial as predictive analytics is in manufacturing, its true power lies in continuous evolution. A model that predicts failures today might be outdated tomorrow. To stay ahead, manufacturers must adopt a dynamic approach—refining predictive models, integrating external intelligence, and leveraging real-time AI to anticipate and prevent risks before they escalate.

The future of smart manufacturing solutions isn’t just about using predictive analytics—it’s about continuously evolving it. The real question isn’t whether predictive models can help, but whether manufacturers are adapting fast enough to outpace risks in an unpredictable world.

At Mantra Labs, we specialize in building intelligent predictive models that help businesses optimize operations and mitigate risks effectively. From enhancing efficiency to driving innovation, our solutions empower manufacturers to stay ahead of uncertainties. Ready to future-proof your factory? Let’s talk.

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