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Revolutionizing CX in Banking in 2024: How Banking Apps are Leveraging AI for Enhanced Customer Engagement

In today’s digital age, banking apps have become a critical tool for customers to manage their finances. With the rise of fintech and open banking,

With Gen AI coming into the picture, banks are leveraging AI to not only streamline their back-end processes but also provide hyper-personalized experiences and enhance customer engagement. According to McKinsey Global Institute, gen AI could add $2.6 trillion to $4.4 trillion annually in value with banking predicted to have one of the largest opportunities.

In this article, we’ll explore how banking apps are leveraging AI to transform the banking industry and revolutionize CX in 2024.

The Rise of Banking Apps

Fintech app

According to a study by the Federal Reserve, 53% of smartphone users have used mobile banking in the past 12 months, and this number is expected to continue to rise. As more customers turn to banking apps for their financial needs, banks are under pressure to provide a seamless and personalized CX to stay competitive.

How AI is Revolutionizing CX in Banking Apps

Personalized Recommendations and Insights

AI in banking

One of the key ways that AI is transforming CX in banking apps is through personalized recommendations and insights. By analyzing a customer’s financial data, AI algorithms can provide personalized recommendations for financial products and services that best suit their needs. This not only helps customers make more informed decisions but also increases the likelihood of cross-selling and upselling for banks.

AI can also provide valuable insights into a customer’s spending habits, allowing banks to offer personalized budgeting and financial planning tools. This not only improves the CX but also helps customers better manage their finances.

With Gen AI’s capability to summarize and contextualize documents from ample unstructured data, those working within customer contact functions can get a more comprehensive view saving their time and effort and thus improving their efficiency. 

Chatbots for 24/7 Customer Support

Another way that AI is enhancing CX in banking apps is through the use of chatbots for customer support. Chatbots are AI-powered virtual assistants that can communicate with customers in natural language, providing quick and efficient support. They can handle a wide range of inquiries, from basic account information to more complex issues, without the need for human intervention.

By using chatbots, banks can provide 24/7 customer support, improving the overall CX for customers. This also reduces the workload for human customer service representatives, allowing them to focus on more complex inquiries.

Fraud Detection and Prevention

Fraud detection

AI is also playing a crucial role in fraud detection and prevention in banking apps. By analyzing a customer’s spending patterns and transaction history, AI algorithms can identify suspicious activity and flag it for further investigation. This not only helps banks prevent fraud but also provides customers with peace of mind knowing that their accounts are being monitored for any unusual activity.

Predictive Analytics for Better Decision-Making

AI-powered predictive analytics is another way that banking apps are leveraging AI to enhance CX. By analyzing a customer’s financial data, AI algorithms can predict future spending patterns and provide insights for better decision-making. This can help customers plan for major purchases, budget more effectively, and make informed investment decisions.

The Future of AI in Banking Apps

Voice-Activated Banking

As AI technology continues to advance, we can expect to see more voice-activated banking features in the future. Customers will be able to use their voice to check their account balance, make transfers, and even apply for loans. This will provide a more convenient and hands-free way for customers to manage their finances.

Hyper-Personalization

With the help of AI, banking apps will be able to provide hyper-personalized experiences for customers. This means that every aspect of the CX, from product recommendations to customer support, will be tailored to the individual customer’s needs and preferences. This will not only improve the CX but also increase customer loyalty and retention.

Advanced Fraud Detection and Prevention

As AI technology continues to evolve, we can expect to see more advanced fraud detection and prevention measures in banking apps. AI algorithms will be able to analyze a customer’s behavior in real-time and identify potential fraud before it happens. This will provide customers with even more security and peace of mind when using banking apps.

Conclusion

AI is revolutionizing CX in banking apps, providing customers with a more personalized, convenient, and secure banking experience. With increasing competition and changing consumer expectations, banks must embrace AI to stay competitive and meet the evolving needs of their customers. With the advancements in AI technology, we can expect to see even more innovative features and improvements in the CX of banking apps in the future.

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Machines That Make Up Facts? Stopping AI Hallucinations with Reliable Systems

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

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