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State of Metaverse-based ecosystems in Fin-Tech

3 minutes read

Paris Hilton has a Roblox virtual island where people can buy digital versions of her outfits. Accenture will onboard 1,50,000 new hires using Metaverse. Metaverse has been the talk of the town since Facebook changed its name to Meta. Let’s look at how metaverse-based ecosystems in Fin-Tech is transforming customer experience (CX).

Global metaverse market size will touch $678.8 billion by 2030, witnessing a CAGR of 39.4%, reveals research and markets. CB Insights’ research predicts that metaverse could represent a $1T market by 2030. Industries are working to create a reality in which the physical and digital worlds blend seamlessly. 

Where Fin-Techs are heading to in the Metaverse-based ecosystem?

European bank ABN Amro was the first to open a virtual branch in Second Life created in 2003. Earliest ventures into the metaverse were primarily motivated by branding and visibility which is now shifting to the mainstream. Metaverse application has moved beyond gamification to virtual training and life-like experiences. We’re moving towards a future where digital lives are becoming more important.

Razorfish and Vice Media Group’s new study shows that Gen Z spends more time in metaverse space than older demographics. They develop more meaningful connections to their online identities and want realistic experiences in their virtual life. For organizations, it becomes highly imperative to understand how these customers connect, interact and interface in this virtual space.

According to JP Morgan’s research, the metaverse offers opportunities to:

  • Transact – every year, $54bn is spent on virtual goods, almost double the amount spent buying music. 
  • Socialize – approximately $60bn messages are sent daily on Roblox.
  • Create – GDP for Second Life was around $650m in 2021 with nearly $80m dollars paid to creators. 
  • Own – NFT currently has a market cap of $41bn.
  • Experience – 200 strategic partnerships till date with The Sandbox, including Warner Music Group to create a music-themed virtual world.

Metaverse has limitless opportunities to offer. Let’s look at some of the top use cases of metaverse in the financial industry.

  1. Recently Lynx announced two use cases: a cryptocurrency-based game that allows players to create and earn and sell digital items with financial value, and an “enhanced remittance experience”, a digital meeting space that allows those sending money to loved ones to visit and communicate with them in a “streamlined, entertaining, economical, and secure” manner.
  2. Navi Technologies has unveiled a metaverse-based “Fund of Funds” scheme. The investors will finance Exchange-Traded Funds (ETFs), which will be used to fund metaverse-based companies. The fintech aims to invest $1 billion in total across multiple assets, with a maximum investment of $300 million in a single ETF. The company will issue a NAV unit at a face value of INR 10. For example, a customer investing INR 500 in the plan, will receive 50 units across the ETFs that Navi will be investing in.
Navi Technologies
  1.  JP Morgan is the first bank to open a lounge- Onyx in Decentraland. In the Onyx Lounge, situated in Metaiuku–a virtual replica of Tokyo’s Harajuku shopping area, a tiger roams the first floor, overlooked by a portrait of the bank’s boss Jamie Dimon. And on the 2nd floor, a person’s avatar can watch experts talk about crypto market.
JP Morgan's Onyx
  1. Korean Bank Kookmin introduced a ‘virtual financial town’ that includes three spaces: (1) The financial and business center consists of branches, public relations and recruitment booths, auditoriums, and social spaces. 

(2) The telecommuting center enhances communication and collaboration between telecommuters and office employees. 

(3) A playground for interacting.

Kookmin Banks' Virtual Financial Town

Source: donga.com/news

  1. Bank of America is the first to launch VR training in over 4,300 financial centers. They use VR headsets to practice skills like strengthen and deepen customer relationships, handle difficult conversations, and listen and respond with empathy. “Managers can also detect skill gaps and provide tailored follow-up training and customized counseling to colleagues to further boost performance using real-time statistics,” the bank says.

The Road Ahead

Decentraland operates via its own cryptocurrency called MANA and Sandbox has Sand. Somnium Space has its own asset marketplace where users can choose to ‘live forever. 

The financial sector is facing intense competition in the virtual space. Digital assets and digital currency are becoming increasingly prevalent in the metaverse. Leveraging the meta-world will help financial organizations create a continuum of experience for the users and provide more personalized and engaging interactions in the time ahead.

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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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