AI in Insurance: Takeaways from AI for Data-driven Insurers Webinar

5 minutes, 54 seconds read

Published on Apr 15, 2020

The adoption of AI has increased exponentially across the business ecosystem in the past couple of years. Yet, Insurance still lags behind many industries due to the nature of its business. However, the ease of convenience that has come with AI implementations has made it indispensable to Insurers. So, where has the demand for the convenience come from? ‘Modern Insurance Customer’. The millennials today demand 24×7 service at their fingertips. They are keener towards information provided on digital channels and more likely to use social media and texting for Insurance interactions. To suffice the needs and demands of the modern insurance customer, AI integration is needed.

Role of AI in Insurance

Currently, AI is playing a pivotal role in transforming Insurance processes such as Claims, Underwriting, Customer Service, Marketing, fraud detection etc. For example, AI chatbots are being used to handle customer service which has led to a significant reduction in cost and optimization of human resources. According to a report by Deloitte on Unraveling the Indian Consumer, India has the world’s largest millennial population of 440 million in the age group of 18-35 years. Internet users in the country are expected to increase from 432 million in 2016 to 647 million by 2021, taking internet penetration from 30 per cent in 2016 to 59 per cent in 2021.

AI-based technologies will be needed to meet the evolving demands of modern insurance customers.

According to the State of AI in Insurance 2020 report, nearly half of all Insurance executives surveyed believe that Automated processing can add value to their customer experience journeys. Nationwide is using artificial intelligence to help analyse customer interactions so it can solve customers’ problems earlier. Using AI and NLP, the insurer identified opportunities for reducing inefficiencies. And the result was more than half of all email enquiries could be resolved by guiding users towards digital channels instead.

During the webinar, we polled the audience to gauge their motivation for implementing AI in their business processes. 44% felt that Claims Processing was the main reason to adopt AI into their business Insurance processes.

The quick poll was in line with Mantra Labs’ State of AI in Insurance report 2020 which found that 74% of the respondents leaning towards the adoption of AI in Claims Processing.

The webinar addressed some of the key challenges faced by Insurers, reasons behind these challenges and how we can approach these challenges to bridge the disconnect.

Data in Silos

Most businesses that have data kept in silos face challenges in collaboration, execution and measurement of their bigger picture goals. Accumulating information in silos may not give accurate insights into improving engagement, which leads to impersonalized content that doesn’t speak to the customer. However, models well-trained on historic data, don’t necessarily perform better with live data. The challenge is that data is often needed before it is even possible to conduct a proof of concept — and sourcing the right data can be both time consuming and costly. The right approach to this issue would be to treat Data as the centrepiece for transformation. Insurers should engage with data scientists/consultants to review the quality of your data. Data exploration exercises need to be performed to challenge/validate the existing assumptions about data captured and stored within the org.

People, Expertise and Technical Competency

Many organizations face a challenge in finding the right ‘Skill and Talent’ for developing AI strategies and implementing them. Critical skill-sets like data scientists, cloud specialists, machine learning engineers, and AI engineers are essential to keep pace. Several Industry experts have also relayed that many AI-based projects and proof-of-concept work do not take off the ground due to lack of quality data at the disposal of such skilled professionals — derailing their availability/ usefulness for hiring purposes. Securing the right data science teams and training the right amount of data needed to support algorithm development can improve confidence levels for organizations.

Clear Vision, Process & Support from Executive Leadership

Often the reason for the failure of AI projects is due to lack of clear thought process from the top management. According to a recent BCG report, there is a big gap between expectations and planning. Most companies want to create a long-term competitive advantage with AI and expect to see a major impact from AI within 5 years. The big disconnect, however, is that only 39% of enterprises had an AI strategy to go with it. Insurers shouldn’t run headfirst into moonshot AI projects. Instead, they should take a more measured approach that identifies a simple problem or problems (use case) that AI can address. Insurers must ensure that the goals of AI projects must be in line with organization goals.

Technology and Vendor Selection

Many Insurers today fail to understand how AI can be leveraged for their business. There is a lot of unseen effort that goes behind any AI implementation project. They are not sure which AI-based technologies to be used for solving a particular problem. According to the State of AI in Insurance 2020 report, InsurTech funding in 2019 reached $6B revealing a stronger emphasis by insurance organizations to fast-track the progress and development made by startups in tackling age-old insurer ills with AI-fueled innovations. InsurTechs are seen as advantageous because they can add value by scaling their operating models at incredible speed owing to their nimble size.

There are tools, products developed harnessing AI-based technologies which have helped optimize several core insurance businesses. The Haven Life Risk Solutions team, in partnership with MassMutual, has developed a platform that uses both a rule engine and machine learning models to analyze the application and third party data in real-time. It can now help MassMutual make many underwriting decisions without human underwriter intervention, and in some cases also without a medical exam. Motor Insurance Claims is where AI is currently driving maximum efficiency. There are certain gaps that are being faced by insurers which can be resolved with AI platforms specific towards claims processing. FlowMagic, a visual AI platform developed by Mantra Labs focuses on streamlining Insurer workflows.

Concluding Remarks

In these challenging times, AI is already helping Insurance companies find their competitive edge, and stay operationally agile even during pandemics. Queries which are being addressed by chatbots help humans to handle more complex issues. It cannot be stressed enough that the next couple of months would be difficult for several businesses including Insurance.

Companies across the world have already started making plans to ensure business continuity in this pandemic. AI or automation will play a crucial role in streamlining various processes and accelerate innovation to adapt to the dynamic environment and ensure long term stability.

Our host Parag Sharma interacted one on one with participants, during an interactive Q&A session where insights were shared with the audience. The discussions centred around some thought-provoking questions such as tracking AI performance once implemented, the role of AI in helping to reach Bharat, the potential for AI in telemedicine, etc.

Articles from Parag:

In 1997, the world watched in awe as IBM’s Deep Blue, a machine designed to play chess, defeated world champion Garry Kasparov. This moment wasn’t just a milestone for technology; it was a profound demonstration of data’s potential. Deep Blue analyzed millions of structured moves to anticipate outcomes. But imagine if it had access to unstructured data—Kasparov’s interviews, emotions, and instinctive reactions. Would the game have unfolded differently?

This historic clash mirrors today’s challenge in data architectures: leveraging structured, unstructured, and hybrid data systems to stay ahead. Let’s explore the nuances between Data Warehouses, Data Lakes, and Data Lakehouses—and uncover how they empower organizations to make game-changing decisions.

Deep Blue’s triumph was rooted in its ability to process structured data—moves on the chessboard, sequences of play, and pre-defined rules. Similarly, in the business world, structured data forms the backbone of decision-making. Customer transaction histories, financial ledgers, and inventory records are the “chess moves” of enterprises, neatly organized into rows and columns, ready for analysis. But as businesses grew, so did their need for a system that could not only store this structured data but also transform it into actionable insights efficiently. This need birthed the data warehouse.

Why was Data Warehouse the Best Move on the Board?

Data warehouses act as the strategic command centers for enterprises. By employing a schema-on-write approach, they ensure data is cleaned, validated, and formatted before storage. This guarantees high accuracy and consistency, making them indispensable for industries like finance and healthcare. For instance, global banks rely on data warehouses to calculate real-time risk assessments or detect fraud—a necessity when billions of transactions are processed daily, tools like Amazon Redshift, Snowflake Data Warehouse, and Azure Data Warehouse are vital. Similarly, hospitals use them to streamline patient care by integrating records, billing, and treatment plans into unified dashboards.

The impact is evident: according to a report by Global Market Insights, the global data warehouse market is projected to reach $30.4 billion by 2025, driven by the growing demand for business intelligence and real-time analytics. Yet, much like Deep Blue’s limitations in analyzing Kasparov’s emotional state, data warehouses face challenges when encountering data that doesn’t fit neatly into predefined schemas.

The question remains—what happens when businesses need to explore data outside these structured confines? The next evolution takes us to the flexible and expansive realm of data lakes, designed to embrace unstructured chaos.

The True Depth of Data Lakes

While structured data lays the foundation for traditional analytics, the modern business environment is far more complex, organizations today recognize the untapped potential in unstructured and semi-structured data. Social media conversations, customer reviews, IoT sensor feeds, audio recordings, and video content—these are the modern equivalents of Kasparov’s instinctive reactions and emotional expressions. They hold valuable insights but exist in forms that defy the rigid schemas of data warehouses.

Data lake is the system designed to embrace this chaos. Unlike warehouses, which demand structure upfront, data lakes operate on a schema-on-read approach, storing raw data in its native format until it’s needed for analysis. This flexibility makes data lakes ideal for capturing unstructured and semi-structured information. For example, Netflix uses data lakes to ingest billions of daily streaming logs, combining semi-structured metadata with unstructured viewing behaviors to deliver hyper-personalized recommendations. Similarly, Tesla stores vast amounts of raw sensor data from its autonomous vehicles in data lakes to train machine learning models.

However, this openness comes with challenges. Without proper governance, data lakes risk devolving into “data swamps,” where valuable insights are buried under poorly cataloged, duplicated, or irrelevant information. Forrester analysts estimate that 60%-73% of enterprise data goes unused for analytics, highlighting the governance gap in traditional lake implementations.

Is the Data Lakehouse the Best of Both Worlds?

This gap gave rise to the data lakehouse, a hybrid approach that marries the flexibility of data lakes with the structure and governance of warehouses. The lakehouse supports both structured and unstructured data, enabling real-time querying for business intelligence (BI) while also accommodating AI/ML workloads. Tools like Databricks Lakehouse and Snowflake Lakehouse integrate features like ACID transactions and unified metadata layers, ensuring data remains clean, compliant, and accessible.

Retailers, for instance, use lakehouses to analyze customer behavior in real time while simultaneously training AI models for predictive recommendations. Streaming services like Disney+ integrate structured subscriber data with unstructured viewing habits, enhancing personalization and engagement. In manufacturing, lakehouses process vast IoT sensor data alongside operational records, predicting maintenance needs and reducing downtime. According to a report by Databricks, organizations implementing lakehouse architectures have achieved up to 40% cost reductions and accelerated insights, proving their value as a future-ready data solution.

As businesses navigate this evolving data ecosystem, the choice between these architectures depends on their unique needs. Below is a comparison table highlighting the key attributes of data warehouses, data lakes, and data lakehouses:

Feature	Data Warehouse	Data Lake	Data Lakehouse
Data Type	Structured	Structured, Semi-Structured, Unstructured	Both
Schema Approach	Schema-on-Write	Schema-on-Read	Both
Query Performance	Optimized for BI	Slower; requires specialized tools	High performance for both BI and AI
Accessibility	Easy for analysts with SQL tools	Requires technical expertise	Accessible to both analysts and data scientists
Cost Efficiency	High	Low	Moderate
Scalability	Limited	High	High
Governance	Strong	Weak	Strong
Use Cases	BI, Compliance	AI/ML, Data Exploration	Real-Time Analytics, Unified Workloads
Best Fit For	Finance, Healthcare	Media, IoT, Research	Retail, E-commerce, Multi-Industry

Conclusion

The interplay between data warehouses, data lakes, and data lakehouses is a tale of adaptation and convergence. Just as IBM’s Deep Blue showcased the power of structured data but left questions about unstructured insights, businesses today must decide how to harness the vast potential of their data. From tools like Azure Data Lake, Amazon Redshift, and Snowflake Data Warehouse to advanced platforms like Databricks Lakehouse, the possibilities are limitless.

Ultimately, the path forward depends on an organization’s specific goals—whether optimizing BI, exploring AI/ML, or achieving unified analytics. The synergy of data engineering, data analytics, and database activity monitoring ensures that insights are not just generated but are actionable. To accelerate AI transformation journeys for evolving organizations, leveraging cutting-edge platforms like Snowflake combined with deep expertise is crucial.

At Mantra Labs, we specialize in crafting tailored data science and engineering solutions that empower businesses to achieve their analytics goals. Our experience with platforms like Snowflake and our deep domain expertise makes us the ideal partner for driving data-driven innovation and unlocking the next wave of growth for your enterprise.

4 Key Takeaways from AI for Data-driven Insurers Webinar