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Why Should Businesses Migrate To Digital Claims Management Process?

The Insurance industry is experiencing tectonic shifts across consumer expectations and process digitalization.

As digital adoption increases for Indian customers, the expectations and corresponding digital experiences that companies need to cater to are also on the rise. The collaboration between insurers and insurtech is critical to bring in the next era of Indian Insurance, especially with the renewed emphasis of GOI and IRDAI towards increasing the number of insured in India. As per a recent BCG report, there has been a 7X growth in global funding for the insurtech industry over the last 5-6 years. Making it essential that collaboration between insurers and insurtechs continues seamlessly.

While companies have been introducing digital touchpoints across the customer lifecycle, a major chunk of focus remains on the pre-purchase and purchase stages of the journey. Amidst rising customer acquisition costs, companies must provide efficient solutions to enhance the experience in the claims and renewal stages of insurance. 

Understanding Insurance Claims Process

Claims processing involves the activities that an insurance company carries out to verify a claim request. During the process, an insurance agent, known as an adjuster, checks information accuracy and provides the claim amount. 

The claims process includes five important steps:

  • Insured informing the insurance company 
  • Initial claims investigation
  • Policy check
  • Claims calculation
  • Payment Terms and Settlement

Based on a survey conducted by SPS Global, 59% of policyholders were dissatisfied with their claims handling. Digital claims processing introduces digital touchpoints to improve the claims customer journey. And, making the process more time and cost-effective for insurers while boosting the overall customer experience. This is why businesses should migrate to digital claims management.

How would a company benefit from the Digital Claims Process?

  1. Faster Claims Processing – The traditional claims process has a long cycle and friction due to multiple physical and digital touchpoints. The verification process involves multiple teams which would be tough to coordinate in a manual setup. Digital channels make it easier to collaborate and settle claims faster.
  2. Reduced Duplication of Effort – Digital means help streamline the operations by leveraging a single unified portal for the teams involved in the process. Providing clear action items, timelines, and statuses for each stakeholder, the platform helps reduce business costs. 
  3. Improved Fraud Detection – AI-powered fraud detection systems help reduce the chances of fraud significantly. Companies such as TagX and DataTrade provide extensive data sets to help insurance companies develop and train their own algorithms and models. 
  4. Higher Customer Satisfaction – Amidst an unfortunate scenario, the insured would prefer minimal delays, simplified processes, and quick settlement. Digital claims processing not only helps speed up the process but also personalizes it for the user. Eventually, the service and support provided boost customer satisfaction and renewal rates. 

A digital future is imminent in every industry today. While the insurance sector remains relatively low in terms of digital maturity. Leaders and Early Adopters will continue to have an edge in attracting and retaining customers.

We’ve listed down potential use cases for the digital claims process which can help companies kick-start their digitalization journey as well. 

  1. Self-Service Insurance Portals – Modern customers require cohesive omnichannel support. Portals that allow users to file and track their claims as well as reach out for further assistance are preferred. A Gartner study observed that 85% of initial customer service interactions start with a well-suited self-service portal. And, technology disruptor, Whatfix claimed that 44% of insurance users would jump to another company if they can’t have control over their claims process. 

Indian Insurtech firm Go Digit allows its customers to file their health insurance claims through their website or mobile application. They provide assistance through videos, faqs, and call assistance to their users. 

Source: www.godigit.com

  1. Conversational Chatbots – The 24×7 available online assistant helps customers with queries and claims assistance without putting them in a long queue. Mostly embedded in a self-service customer portal or the insurance website, conversational tools use AI and Sentiment Analysis to answer customer queries in the most responsive way possible. 

Mantra Labs recently worked with Ageas Federal on a “Claims with Empathy” process to help reimagine messaging and nudges in case of an insurance claim. 

Our conversational tool Hitee offers insurance customer support in multiple local languages to ensure ease of access and understanding to its users. 

  1. Remote Claims Estimation – Leveraging mobile cameras to take a visual estimate of damage incurred on the insured object, assess the extent of damage remotely, and provide an estimate to the insured instantaneously. Helping lower the claim cycle and reduce cost per claim. 

InsurTech companies such as Lemonade and MUA Insurance offer telematics-based apps, which help users file claims through their app, get instant roadside assistance, and provide settlements faster with AI-driven algorithms. Altoros provides a Car Damage Recognition API that leverages computer vision to determine the extent of damage in case of an accident.

Conclusion

The digital transformation of a company’s claims function lies in the redesign of its customer claims journey – Where the changes are not piecemeal solutions or interactions, but a reimagining of how the end-to-end journey will be actionized, perceived, and experienced. As companies begin their journey toward digital claims processing, they need to understand and deliver the core value that users gain from digitalization. 

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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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