Why Do We Need a New Banking Ecosystem for Gig Economy Workers?

Published on Aug 23, 2023

The emergence of the gig economy in India represents a profound shift in the nation’s workforce. Unlike traditional employment, where workers receive consistent salaries and benefits, gig workers engage in temporary, flexible jobs. According to a report by the Boston Consulting Group and Michael & Susan Dell Foundation, gig workers constitute nearly 90 million in urban India and have shown consistent growth.

India’s gig economy is burgeoning, with an estimated workforce of 15-20 million workers. It’s a space where innovation thrives but is often juxtaposed with financial challenges, especially concerning banking facilities.

Gig Economy Banking is a critical aspect that requires attention, given the unique financial challenges faced by these workers. This blog post explores the banking needs of India’s gig economy, examining the issues and proposing solutions to bridge the gap with salaried employees.

Gig Economy Workforce: Solopreneurs and LLPs

Solopreneurs are self-reliant entrepreneurs operating without a traditional team. Examples include independent consultants, artists, or online retailers. Limited Liability Partnerships (LLPs) on the other hand combine features of partnerships and corporations and are prevalent among small to medium businesses in legal, architectural, and tech sectors. Although they contribute a lot to the economy, the banking facilities at their disposal are lackluster and inefficient. Let’s analyze their pain points in comparison with salaried employees and established businesses respectively.

For Solopreneurs

Access to Credit: While salaried employees usually have easier access to credit based on stable income proofs, only 23% of solopreneurs have access to formal credit systems.

Minimum Balance Requirements: Salaried accounts often come with low or no minimum balance requirements. In contrast, solopreneurs face higher requirements, limiting their banking options.

Interest Rates and Loan Terms: Higher interest rates and stringent loan terms are common for solopreneurs due to perceived risks.

For LLPs

Regulatory Compliance: Unlike larger registered businesses, LLPs face complex and cumbersome regulatory compliances, impacting 40% of LLPs in India in 2018.

Credit Facilities: Even though LLPs contribute significantly to the economy, banks are often hesitant to extend the same credit facilities offered to larger corporations.

Business Banking Features: LLPs often lack access to customized business banking features that are usually available to registered businesses, such as specialized loans, overdrafts, or merchant services.

A 2020 study by the Indian government revealed a significant gap in banking facilities offered to the gig workforce compared to traditional salaried employees and registered businesses:

Low Minimum Balance Savings Account: 75% of salaried employees have access to this, compared to only 28% of solopreneurs.
Credit Facilities: 60% of registered businesses avail themselves of credit lines, while only 15% of LLPs have similar access.

This disparity emphasizes the need for reformation in the banking sector to cater to the unique needs of solopreneurs and LLPs, who are equally vital in contributing to the economic fabric.

Contribution to the Economy

Both Solopreneurs and LLPs play a vital role in India’s economic landscape.

Often innovators and trendsetters, solopreneurs contribute significantly to various sectors like IT, design, and consulting. In 2019, they were responsible for 20% of India’s freelance revenue, translating to nearly $1 billion.

Thereafter, acting as a flexible structure for entrepreneurship, LLPs allow businesses to thrive without the constraints of a corporate framework. They play a vital role in the growth of small and medium enterprises (SMEs), comprising over 10% of India’s GDP.

Need to Address Banking Pain Points

The banking challenges faced by Solopreneurs and LLPs are not mere inconveniences but barriers that can stunt economic growth.

Access to Finance: Lack of tailored financial products leads to a restricted flow of credit, hindering growth opportunities. According to RBI’s report, only 10% of small businesses in India have access to formal credit channels.

Modern Banking Facilities: The absence of personalized banking solutions equivalent to those offered to salaried employees and larger corporations adds to operational difficulties.

Regulatory Hurdles: Complex compliances can deter LLPs from seeking formal banking relationships, pushing them towards informal lending sources.

Initiatives and Solutions

Despite several initiatives aimed at addressing the banking challenges of solopreneurs and LLPs, the current solutions are still falling short. Government schemes like MUDRA and bespoke banking products from financial institutions have made some progress but haven’t completely bridged the gap.

The existing banking paradigm fails to recognize the unique financial needs of the nearly 90 million gig workers in India, who constitute a significant force in the economy. Traditional banking models are largely incompatible with the flexible and temporary nature of gig work, leading to hurdles in credit access, regulatory compliance, and modern banking facilities.

The urgency to build a new banking ecosystem for gig workers is underscored by their substantial contribution to the economy and their growing presence in the workforce. This ecosystem should include:

Tailored Financial Products: Design banking products specifically for gig workers, considering their inconsistent income streams and unique operational needs.
Flexible Regulatory Framework: Streamline regulations to facilitate easier compliance for smaller entities, thus making formal credit more accessible.
Technology Integration: Utilize fintech solutions to provide seamless digital banking experiences tailored to the needs of gig workers.
Collaboration with Gig Platforms: Partner with platforms that engage gig workers to develop customized financial services that align with their working patterns.

What Role Does Tech Companies Play Here?

Tech companies can be the bridge that connects traditional banking to the unique needs of the gig economy. Since banking is largely digitized these days, the new ecosystem for gig workers will be on the Internet. There comes the need to integrate the latest technologies and solutions to provide the best customer experience.

Mantra Labs developed ezetap’s Universal Payment Solution is a vivid example in this case. It is a secure interface that facilitates digital transactions across various touchpoints. This innovation reflects how tech solutions can tailor banking systems to suit the diverse needs of the gig workforce.

The growth of Solopreneurs and LLPs in India is a testament to the country’s entrepreneurial spirit. However, their unique banking challenges call for targeted solutions that understand and cater to their specific needs. With a combination of government initiatives, bespoke banking products, and technological innovation, there’s a pathway to creating a more inclusive financial ecosystem.

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.

Why Do We Need a New Banking Ecosystem for Gig Economy Workers?