CX Trends for Banking In India, 2022

Traditional banking relationships are based on years of face-to-face customer care, but modern banking relationships are based on a customer’s ability to swiftly access banking goods and services digitally, via their phone or any device.

According to Deloitte, only 11% of financial institutions throughout the world have properly upgraded their core systems. Other banks, on the other hand, are having difficulty implementing modern technologies.

The challenges being faced by Indian banks:

Public Sector Banks struggling with economies of scale are not able to unleash technology on full scale to pass on low costs to consumers so far and despite the abundance of solution providers ready to help, more than half of the companies said they are having difficulty deploying artificial intelligence (AI).

Financial institutions will need to use new technologies that enhance agility, efficiency, security, and innovation to address these issues and become future-ready. Intelligent decisioning, open banking APIs, embedded solutions, cloud computing, metaverse banking, and cybersecurity will differentiate banks and credit unions in 2022 and beyond. Every technology deployment should make a concerted effort to improve digital consumer experiences on a big scale and in a timely manner.

Trends Revamping Customer Experience in Banking for 2022

AI and applied analytics

AI and advanced analytic algorithms can project reports on the organization’s processes and employees may use this data to improve back-office processes, customer service, loyalty, revenues, and save money and time.

Financial institutions will be able to provide the greatest value-added services in terms of client demands and preferences owing to AI and applied analytics. Personalized and contextual communication will explain how products and services fit customers’ needs in near-real time, reducing both engagement costs and financial consequences. At scale and in real-time, proactive and dynamic advising is also possible.

Conversation AI bots

With the development of chatbots, the high adoption rate of artificial intelligence (AI) has been leveraged to focus on customer happiness.

According to Mordor Intelligence, the chatbot industry was worth USD 17.17 billion in 2020 and is expected to grow to USD 102.29 billion by 2026, with a CAGR of 34.75 percent between 2021 and 2026.

Chatbots in the banking industry uses cognitive analytics to facilitate communication and establish customer relationships by learning what consumers are thinking and responding instantly.

For instance, YES Bank introduced YES ROBOT, an AI-enabled chatbot to assist its customers. YES ROBOT uses conversational AI with vast financial knowledge to enable clients to conduct financial and non-financial banking transactions. Similarly, there’s Eva from HDFC, AXAA from Axis bank, ADI from Bank of Baroda, ABHi from Andhra bank and the list goes on.

Open Banking APIs

An open banking API approach can enable a variety of useful services for both users and providers.

Banking firms, for example, can collect useful data about buying habits, financial goals, and risk tolerance from both internal and external sources. This information can be utilized to improve multichannel marketing accuracy and provide proactive solutions and advisory services. It can aid in the introduction of services like phone banking, peer-to-peer lending, risk management, and loan processing, among others.

Despite the advantages, there are certain drawbacks, such as data security and financial privacy, the lack of grievance redressal procedures, compliance issues, and cybersecurity risks.

However, open banking models established by State Bank and Axis Bank make customer connections and transactions easier every day.

Neo Banking

According to Statista, the average transaction value per user in the Neobanking segment is US$4.71k in 2022 and is expected to expand at a rate of 20.60 percent annually (CAGR 2022-2026), resulting in a predicted total amount of US$101.40 billion by 2026.

Neo Banks are a cost-effective alternative to traditional banks, providing very convenient and user-friendly financial services specialized to a specific audience (both business and consumer). They provide savings accounts, prepaid cards, bill payments, and money transfers, as well as financial management services, 24-hour customer care, and high-security features. The user interface of the smartphone app is straightforward and intuitive. A transparent structure with a real-time notification feature.

Customer neo banks like Niyo, FamPay, Jupiter, and Fi raised $230 million in total in 2021. In the commercial neo banking industry, Open was reportedly valued at $500 million. Neo banking has a lot of space to grow in India, as smartphone imports (and usage) are continuously expanding.

Cloud Computing

According to a recent IDC report, approximately 80% of corporate banks in India will be using Cloud technology to run their trade finance and treasury workloads by 2024.

Cloud computing will open doors for banks to react rapidly to changing market conditions as well as obtain and analyze data in real-time, resulting in high engagement and personalization across all channels. Cloud technology will also help banks increase their customer base by providing a variety of mobile and application-based capabilities.

Embedded Finance

Embedded Finance has created an ecosystem in which any organization can offer innovative financial solutions on a single platform, spanning from credit card transactions to insurance, billing, and payments, all without requiring much human participation.

Embedded finance has played a critical role in India in encouraging the adoption of digital payments— UPI.

According to Statista, there were over 25 billion UPI transactions worth over 41 trillion Indian rupees in the fiscal year 2021. In the fiscal year 2025, the country’s transaction value is expected to exceed 128 trillion Indian rupees. The increase was due to a spike in peer-to-merchant transactions, implying that UPI might play a larger role in financial inclusion by bringing thousands of people from tier 3 cities and beyond into the digital economy.

Metaverse

A metaverse bank can provide a “telecommuting” center for employees and allow customers to roam around in their own virtual financial town, complete with a virtual branch and financial playground while interacting with content and a real-life agent through video chat.

Customers visiting virtual branches for excellent customer service, having a real-time mortgage broker visit their home, discussing retirement plans with an avatar advisor, attending an investor event, or participating in a bank-sponsored community programme are just a few of the new ways the metaverse has opened up for reaching out to new audiences, including a younger, more experienced generation of NFTs.

According to Lina Lim (HSBC, Asia Pacific), the metaverse ecosystem is still in its early stages, but it offers many interesting potentials as organizations of all sizes and backgrounds flock to it. Therefore, HSBC is investing $3.5 billion into its wealth and personal banking division.

What Lies Ahead

All of these trends lead to the Indian banking industry adopting technology quickly, but data security is a major worry for both banks and their consumers. Recently, Microsoft has made it possible for users to go password-free by using their Authenticator app. While this will not stop fraudsters from operating, as biometrics becomes more frequently used, it will provide an extra layer of security.

Cyberattacks are more common than any other sort of attack these days. Captchas and tick boxes are no longer adequate security measures. As a result, financial institutions must invest in data security and protection. Conduct audits and re-evaluations of existing systems. Above all, make sure that privacy policies don’t become a roadblock for customers.

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.

CX Trends for Banking In India, 2022