Unveiling Interconnected Risks in Proactive Commercial Insurance

Telematics, fitness trackers & smart watches are helping customers lower their insurance costs. But what’s beyond that? India's "Make in India" initiative has encouraged remarkable growth in the manufacturing sector, positioning it as a sweet spot of the nation's economic progress.

However, with the growth, it always brings increased complexity and interconnected risks, which is why insurance matters for them. In this era of AI, the foundational role of IoT and graph databases in proactive loss prevention cannot be overstated, but demands a paradigm shift in commercial insurance.

According to the Annual Survey of Industries, the manufacturing sector contributed approximately 17% to India's Gross Value Added (GVA). However, the commercial insurance sector's growth hasn't fully kept pace. Data from IRDAI's annual reports indicate that while the overall general insurance premium has grown, the penetration of commercial insurance, especially for small and medium-sized enterprises (SMEs), remains quite low.

This gap highlights the need for innovative solutions that can accurately assess and mitigate the complex risks associated with modern manufacturing and insure them. The Data is the fundamental for mitigating risks and predictive insurance.

With the advancements in AI, IoT remains crucial for capturing real-world data that fuels predictive insurance models. In India, where industrial automation is rapidly increasing, companies are deploying IoT sensors to monitor equipment performance, environmental conditions, and operational processes. These sensors generate a wealth of real-time data, for example

• Vibration and Temperature, to detect early signs of equipment failure.

• Energy Consumption, to identify inefficiencies and potential hazards.

• Environmental, to monitor air quality, humidity, and other factors that can impact operations.

And these manufacturing companies are already leveraging IoT primarily for

• Predictive Maintenance: A study by McKinsey’s Predictive maintenance indicates that predictive maintenance can reduce equipment downtime by 30-50% and increase equipment lifespan by 20-40%.

• Operational Efficiency: IoT-enabled monitoring systems help companies optimize energy consumption, reduce waste, and improve overall productivity.

How InsurTech can play a vital role in unveiling interconnected risks:

While IoT sensors provide valuable data points, graph databases can be an essential tool for understanding the relationships between these data points. In a manufacturing environment, equipment, processes, and personnel are interconnected, and a failure in one area can have cascading effects. Graph databases excel at representing and analyzing these complex relationships.

Technical Possibilities:

• Graph Modeling: Using graph databases, insurers can create a digital twin of a manufacturing facility, representing equipment, processes, and interdependencies as nodes and edges.

• Graph Analytics: Graph algorithms can be used to identify critical paths, detect anomalies, and predict potential failures. For example, a graph algorithm can identify equipment that is highly connected and prone to cascading failures.

• Real-Time Risk Monitoring: By integrating IoT data with graph databases, insurers can create real-time risk monitoring dashboards that provide alerts when anomalies are detected.

• Predictive Risk Assessment: Machine learning models can be trained on historical data and graph features to predict the likelihood of equipment failures, process disruptions, and other potential losses.

So in summary, an AI-driven platform that integrates IoT sensor data with graph databases, empowering insurers to create digital twins, assess risks, proactively prevent losses, and encourage manufacturers to secure insurance coverage.

As per the Annual Survey of Industries, Food & Textile are two major contributors in the manufacturing industries. Let’s explore a simple case study to gain practical insights.

Proactive Loss Prevention in a Textile Mill:

Consider a textile mill in India that uses IoT sensors to monitor the performance of its weaving machines. A graph database is used to represent the relationships between the machines, the raw materials, and the production processes. By analyzing the data, the insurer can:

• Identify machines that are prone to breakdowns due to wear and tear.

• Predict potential disruptions in the production process due to material shortages or equipment failures.

• Provide proactive recommendations to the mill to prevent losses.

Similarly, proactive Hygiene Monitoring in a Food Processing Plant can save the manufacturer from producing damaged/unhygienic products and save significant costs associated with product recalls, potential legal liabilities, and reputational damage, while the insurer can leverage real-time hygiene data to offer dynamic risk assessments, potentially lower premiums for well-maintained facilities, and reduce the likelihood of costly contamination-related claims.

With the confluence of Data, Technology, and AI, the Indian commercial insurance industry can move beyond reactive claim settlements and embrace proactive loss prevention, contributing to the higher insurance coverage of manufacturing sectors while ensuring sustainable growth for the InsurTech industry.

If you’d like to discuss this further or explore how to implement this strategy, feel free to reach out to us at info@intglobal.com.

Author: Debopam Majilya, Director - Technology, Indus Net Technologies(INT.)

Disclaimer: The opinions expressed within this article are the personal opinions of the author. The facts and opinions appearing in the article do not reflect the views of IIA and IIA does not assume any responsibility or liability for the same