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 Predictive Analytics Models Need Retraining?

Predictive analytics in manufacturing relies on historical data and machine learning to foresee potential failures. However, manufacturing environments are dynamic, machines degrade, processes evolve, supply chains shift, and external forces such as weather and geopolitics play a bigger role than ever before.

Without continuous model retraining, predictive models lose their accuracy. A recent study found that 91% of data-driven manufacturing models degrade over time due to data drift, requiring periodic updates to remain effective. Manufacturers relying on outdated models risk making decisions based on obsolete insights, potentially leading to catastrophic failures.

The key is in retraining models with the right data, data that reflects not just what has happened but what could happen next. This is where integrating external data sources becomes crucial.

Is Integrating External Data Sources Crucial?

Traditional smart manufacturing solutions primarily analyze in-house data: machine performance metrics, maintenance logs, and operational statistics. While valuable, this approach is limited. The real breakthroughs happen when manufacturers incorporate external data sources into their predictive models:

Weather Patterns: Extreme weather conditions have caused billions in manufacturing risk management losses. For example, the 2021 Texas power crisis disrupted semiconductor production globally. By integrating weather data, manufacturers can anticipate environmental impacts and adjust operations accordingly.

Market Trends: Consumer demand fluctuations impact inventory and supply chains. By leveraging market data, manufacturers can avoid overproduction or stock shortages, optimizing costs and efficiency.

Geopolitical Insights: Trade wars, regulatory shifts, and regional conflicts directly impact supply chains. Supply chain risk analytics combined with geopolitical intelligence helps manufacturers foresee disruptions and diversify sourcing strategies proactively.

One such instance is how Mantra Labs helped a telecom company optimize its network by integrating both external and internal data sources. By leveraging external data such as radio site conditions and traffic patterns along with internal performance reports, the company was able to predict future traffic growth and ensure seamless network performance.

The Role of Edge Computing and Real-Time AI

Having the right data is one thing; acting on it in real-time is another. Edge computing in manufacturing processes, data at the source, within the factory floor, eliminating delays and enabling instant decision-making. This is particularly critical for:

Hazardous Material Monitoring: Factories dealing with volatile chemicals can detect leaks instantly, preventing disasters.

Supply Chain Optimization: Real-time AI can reroute shipments based on live geopolitical updates, avoiding costly delays.

Energy Efficiency: Smart grids can dynamically adjust power consumption based on market demand, reducing waste.

Conclusion:

As crucial as predictive analytics is in manufacturing, its true power lies in continuous evolution. A model that predicts failures today might be outdated tomorrow. To stay ahead, manufacturers must adopt a dynamic approach—refining predictive models, integrating external intelligence, and leveraging real-time AI to anticipate and prevent risks before they escalate.

The future of smart manufacturing solutions isn’t just about using predictive analytics—it’s about continuously evolving it. The real question isn’t whether predictive models can help, but whether manufacturers are adapting fast enough to outpace risks in an unpredictable world.

At Mantra Labs, we specialize in building intelligent predictive models that help businesses optimize operations and mitigate risks effectively. From enhancing efficiency to driving innovation, our solutions empower manufacturers to stay ahead of uncertainties. Ready to future-proof your factory? Let’s talk.

In the manufacturing industry, predictive analytics plays an important role, providing predictions on what will happen and how to do things. But then the question is, are these predictions accurate? And if they are, how accurate are these predictions? Does it consider all the factors, or is it obsolete?

CX Trends for Banking In India, 2022