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.

There was a time when people truly believed that humans only used 10% of their brains, so much so that it fueled Hollywood Movies and self-help personas promising untapped genius. The truth? Neuroscientists have long debunked this myth, proving that nearly all parts of our brain are active, even when we’re at rest. Now, imagine AI doing the same, providing information that is untrue, except unlike us, it doesn’t have a moment of self-doubt. That’s the bizarre and sometimes dangerous world of AI hallucinations.

AI hallucinations aren’t just funny errors; they’re a real and growing issue in AI-generated misinformation. So why do they happen, and how do we build reliable AI systems that don’t confidently mislead us? Let’s dive in.

Why Do AI Hallucinations Happen?

AI hallucinations happen when models generate errors due to incomplete, biased, or conflicting data. Other reasons include:

Human oversight: AI mirrors human biases and errors in training data, leading to AI’s false information
Lack of reasoning: Unlike humans, AI doesn’t “think” critically—it generates predictions based on patterns.

But beyond these, what if AI is too creative for its own good?

‘Creativity Gone Rogue’: When AI’s Imagination Runs Wild

AI doesn’t dream, but sometimes it gets ‘too creative’—spinning plausible-sounding stories that are basically AI-generated fake data with zero factual basis. Take the case of Meta’s Galactica, an AI model designed to generate scientific papers. It confidently fabricated entire studies with fake references, leading Meta to shut it down in three days.

This raises the question: Should AI be designed to be ‘less creative’ when AI trustworthiness matters?

The Overconfidence Problem

Ever heard the phrase, “Be confident, but not overconfident”? AI definitely hasn’t.

AI hallucinations happen because AI lacks self-doubt. When it doesn’t know something, it doesn’t hesitate—it just generates the most statistically probable answer. In one bizarre case, ChatGPT falsely accused a law professor of sexual harassment and even cited fake legal documents as proof.

Take the now-infamous case of Google’s Bard, which confidently claimed that the James Webb Space Telescope took the first-ever image of an exoplanet, a factually incorrect statement that went viral before Google had to step in and correct it.

There are more such multiple instances where AI hallucinations have led to Human hallucinations. Here are a few instances we faced.

When we tried the prompt of “Padmavaat according to the description of Malik Muhammad Jayasi-the writer ”

When we tried the prompt of “monkey to man evolution”

Now, if this is making you question your AI’s ability to get things right, then you should probably start looking have a checklist to check if your AI is reliable.

Before diving into solutions. Question your AI. If it can do these, maybe these will solve a bit of issues:

Can AI recognize its own mistakes?
What would “self-awareness” look like in AI without consciousness?
Are there techniques to make AI second-guess itself?
Can AI “consult an expert” before answering?

That might be just a checklist, but here are the strategies that make AI more reliable:

Strategies for Building Reliable AI

1. Neurosymbolic AI

It is a hybrid approach combining symbolic reasoning (logical rules) with deep learning to improve factual accuracy. IBM is pioneering this approach to build trustworthy AI systems that reason more like humans. For example, RAAPID’s solutions utilize this approach to transform clinical data into compliant, profitable risk adjustment, improving contextual understanding and reducing misdiagnoses.

2. Human-in-the-Loop Verification

Instead of random checks, AI can be trained to request human validation in critical areas. Companies like OpenAI and Google DeepMind are implementing real-time feedback loops where AI flags uncertain responses for review. A notable AI hallucination prevention use case is in medical AI, where human radiologists verify AI-detected anomalies in scans, improving diagnostic accuracy.

3. Truth Scoring Mechanism

IBM’s FactSheets AI assigns credibility scores to AI-generated content, ensuring more fact-based responses. This approach is already being used in financial risk assessment models, where AI outputs are ranked by reliability before human analysts review them.

4. AI ‘Memory’ for Context Awareness

Retrieval-Augmented Generation (RAG) allows AI to access verified sources before responding. This method is already being used by platforms like Bing AI, which cites sources instead of generating standalone answers. In legal tech, RAG-based models ensure AI-generated contracts reference actual legal precedents, reducing AI accuracy problems.

5. Red Teaming & Adversarial Testing

Companies like OpenAI and Google regularly use “red teaming”—pitting AI against expert testers who try to break its logic and expose weaknesses. This helps fine-tune AI models before public release. A practical AI reliability example is cybersecurity AI, where red teams simulate hacking attempts to uncover vulnerabilities before systems go live

The Future: AI That Knows When to Say, “I Don’t Know”

One of the most important steps toward reliable AI is training models to recognize uncertainty. Instead of making up answers, AI should be able to respond with “I’m unsure” or direct users to validated sources. Google DeepMind’s Socratic AI model is experimenting with ways to embed self-doubt into AI.

Conclusion:

AI hallucinations aren’t just quirky mistakes—they’re a major roadblock in creating trustworthy AI systems. By blending techniques like neurosymbolic AI, human-in-the-loop verification, and retrieval-augmented generation, we can push AI toward greater accuracy and reliability.

But here’s the big question: Should AI always strive to be 100% factual, or does some level of ‘creative hallucination’ have its place? After all, some of the best innovations come from thinking outside the box—even if that box is built from AI-generated data and machine learning algorithms.

At Mantra Labs, we specialize in data-driven AI solutions designed to minimize hallucinations and maximize trust. Whether you’re developing AI-powered products or enhancing decision-making with machine learning, our expertise ensures your models provide accurate information, making life easier for humans

CX Trends for Banking In India, 2022