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7 Chatbot Trends in Insurance 2021

5 minutes read

The chatbot market was valued at USD 17.17 billion in 2020 and is projected to reach USD 102.29 billion by 2026, which, in other words, it is a 34.75% rise in CAGR over the forecast period (2021-2026). 

“According to some estimates, by 2025 95% of all customer interactions will be powered by chatbots”, reports DuckCreek technologies on their blog. 

“Utilizing AI and machine learning, chatbots can interact with customers seamlessly, saving everyone within an organization time – and ultimately saving insurance companies money. A bot can walk a customer through a policy application or claims process, reserving human intervention for more complex cases,” the blog continues. 

Source: www.mantralabsglobal.com

As chatbots help reduce operational costs and increase customer experience for global enterprises, their market size is likely to increase gradually, thus giving an impetus to Chatbot marketing, online payments, customer service, and similar segments. 

Source: chatbotsmagazine.com 

As we head to the second half of 2021, here’s a look at some of the chatbot trends we expect to see: 

  • Customer Intelligence: 

Predictive Analytics depends on a number of statistical techniques including data mining, predictive modeling, pattern matching, and machine learning. The efficient usage of relevant techniques and algorithms for bots helps to ensure not just premium customer experience but also meeting other business requirements. Integrating advanced behavioral analytics to chatbots is now common practice for companies either as standalone software or as a built-in feature, resulting in a better customer experience.

  • Faster claims handling:

Insurance chatbots are a swift way of arriving at a resolution especially when the query requires minimal support from a human, case in point, pulling up relevant data, answering a question and also, filing a claim. A customer can just ask the bot to help them file a claim and the chatbot gets to work by scanning and pulling up the customer’s policy from the insurer’s database or backend system, ask the customer for any additional details (including a security step), and then initiate the claims filing process. 

  • Conversational AI

Conversational AI will go a long way in helping bridge knowledge gaps and lend more clarity around insurance. An AI-based assistant is the first step in responding to a customer’s queries around plans and policies, benefits and coverage, pricing, payment plans and options, and more. For Care Health Insurance, Mantra Labs built Hitee, an emotionally intelligent chatbot, who works as an entry-level customer support specialist aiding Care Health Insurance with customer queries around insurance. 

  • Video Call Support: 

The COVID-19 pandemic saw a surge in phone calls and video calls as there was an increased need to stay home. On a video call, you can see the person you’re talking to, and read their facial expressions, which is almost as good as face-to-face interaction. However, in case of a video chatbot, you aren’t talking with a human but a chatbot with a digital human avatar. Suitably dubbed ‘artificial humans’, a video chatbot has the ability to help customers through its digitally rendered human face, body, and voice.

This newfound breed of digital humankind works on a mix of machine learning and neural networks which has thus far allowed these avatars to better mimic human emotion and behavior. 

  • Local Languages and Dialects

According to Indian Languages – Defining India’s Internet, a report by KPMG, “Chat applications cater to 170 million Indian language internet users. This is expected to grow to 400 million by 2021 at a CAGR of 19%.” 

Source: Indian Languages – Defining India’s Internet, KPMG 

A multilingual chatbot allows enterprises to connect and converse with consumers across language and cultural barriers helping to enhance engagement and conversions. However, building multilingual chatbots requires more than using a language translator to process text or dialogue from English to another language. 

To make multi-language communication effective and on point, a chatbot must be trained on an end user’s culture, history, and any regional nuances. Additionally, global enterprises are also building multichannel bots that connect multiple messaging platforms or voice channels to the same project. 

  • Emotional Awareness

Picture this: You have had a tough day at work and so you want to wind down and get ready for the weekend, stress-free. However, owing to the pandemic and a continued spate of work-from-home scenarios, the usual Friday night out with friends is a far-fetched dream. What’s the next thing you turn to? Fortunately, there’s an option available for that in the form of chatbots with high emotional intelligence that captures human sentiment, emotional states and elicits positive responses during a conversation, while making sure that the person on the other side of the screen feels safe speaking to a stranger, in this case, a machine. 

Wysa, rated as one of the most innovative mental health support apps, does exactly that. You can have a normal conversation, engage in exercises to help you through anxious phases, listen to sleep sounds that calm your nerves, and it also offers an option to speak to a therapist. Wysa’s EQ also ensures that she makes timely follow-ups to ask how you’re doing and sends weekly reports as a summation of your past conversations. 

A Pew Research Center study reports that by the year 2025, AI and robotics will permeate most aspects of one’s daily life. 

  • Personalized Marketing

Gartner had previously predicted that by the year 2020, people would have more conversations with chatbots than their spouses. The chatbots of the future are not just programmed to respond to questions, but to talk and draw relevant insights from knowledge graphs and eventually, forging emotional relationships with customers. 

Sephora’s Facebook Messenger bot is a popular use case when discussing chatbot personalization. The cosmetics company built and deployed a bot to allow customers to book an appointment for an in-store takeover which resulted in a whopping 11% higher conversion rate than any other booking channels Sephora used. 

Chatbots are constantly on the rise amid the need for customers to be online 24X7. Chatbot architecture and design are fast-evolving to the level that conversational AI will become a standard customer service practice. Noteworthy tech companies are pushing themselves forward in industries like retail, banking and finance, and healthcare sectors with the development of advanced chatbots powered by artificial intelligence and machine learning.

According to linchpin.seo, “Experts believe that AI will be a major investment in customer experience for a few years. 47% of organizations are expected to implement chatbots for customer support services, and 40% are expected to adopt virtual assistants. Predictions of consumer-based services suggest that chatbots will be programmed to match human behavior, offer similar services, and improve customer service.” 

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Lake, Lakehouse, or Warehouse? Picking the Perfect Data Playground

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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:

FeatureData WarehouseData LakeData Lakehouse
Data TypeStructuredStructured, Semi-Structured, UnstructuredBoth
Schema ApproachSchema-on-WriteSchema-on-ReadBoth
Query PerformanceOptimized for BISlower; requires specialized toolsHigh performance for both BI and AI
AccessibilityEasy for analysts with SQL toolsRequires technical expertiseAccessible to both analysts and data scientists
Cost EfficiencyHighLowModerate
ScalabilityLimitedHighHigh
GovernanceStrongWeakStrong
Use CasesBI, ComplianceAI/ML, Data ExplorationReal-Time Analytics, Unified Workloads
Best Fit ForFinance, HealthcareMedia, IoT, ResearchRetail, 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.

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