Discover how generative AI is revolutionizing the financial industry by enhancing fraud detection

Link: https://www.analyticsinsight.net/generative-ai/how-generative-ai-is-transforming-financial-industry?utm_source=pocket_saves

Source: https://www.analyticsinsight.net

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Artificial intelligence (AI) may cut costs, but financial services companies have reportedly been slow to embrace it.

That’s according to a report Sunday (June 30) by the Financial Times (FT), which said that regulatory concerns and worries about job losses have kept banks from adopting AI products.

“The big banks will definitely not adopt [the technology] as quickly as any of the FinTech,” said Tom Blomfield, co-founder of neobank Monzo and group partner at Silicon Valley startup incubator Y Combinator. 

He added that generative AI will “make banks more efficient and able to provide the same products at a cheaper cost.”

The report cited a study by Capgemini showing that just 6% of retail banks are ready for widespread AI implementation. 

However, the FT also pointed to an estimate by McKinsey that AI could add up to $340 billion in value per year to the global banking sector, which comes out to around 4.7% of industry revenues. Despite this windfall, the report said, there are fears that the change will cost people their jobs.

“People don’t understand that it’s there as a productivity tool,” said Nasir Zubairi, chief executive of FinTech accelerator Luxembourg House of Financial Technology. “They still genuinely believe it will take away their jobs.”

It’s not just the finance sector where professionals feel threatened by AI. As covered here recently, there are also concerns about its impact on jobs in the creative industries.

In anticipation of AI’s potential impact, some figures in the music industry, including artists like Billie Eilish and Nicki Minaj, have signed an open letter asking for protections against the unauthorized use of their songs to train AI models, expressing concerns that unchecked AI could devalue their work and bar artists from fair compensation.

Meanwhile, PYMNTS wrote last week about the potential of generative AI to reduce the expensive burden of payments fraud.

“As this technology continues to mature and its adoption gains traction, it could become a cornerstone of modern payments fraud prevention strategies, promising improvements in accuracy, efficiency and cost savings,” that report said.

The excitement is due in large part to the technology’s potential to overcome the limitations of traditional fraud detection tools. Its capabilities could supplement current methods with real-time identification and neutralization of payments fraud, which could protect the purchasing experience and improve banks and businesses’ bottom lines.

Link: https://www.pymnts.com/artificial-intelligence-2/2024/financial-services-sector-shows-reluctance-to-embrace-ai/

Source: https://www.pymnts.com

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• The financial services’ vast well of data and communications means the industry both benefits and is vulnerable to harm by artificial intelligence (AI).

• Fusing AI and quantum technologies (AQ) will enable fast data analytics while vastly improving cybersecurity – a game changer for the financial industry.

• Integrating AQ technologies still requires groundwork, including research, talent and infrastructure, as well as vigilance around regulatory and ethical considerations.

By Colin Bell and Jack Hidary

Artificial intelligence (AI) drives significant change in the financial services industry faster than many other sectors. The financial sector’s high density of data and communications makes it ripe for both improvement and attack by AI tools. Even greater transformation in this sector is now coming with the fusion of AI and quantum technologies.

The synergy of these two powerful forces, abbreviated to AQ, will reshape the financial landscape and raise the bar for innovation and security. Institutional investors holding shares in financial institutions increasingly demand reduced risk and greater growth from their portfolio – leveraging quantum and AI technologies is key to unlocking this upside.

In recent years, AI has started to revolutionize the financial industry in ways we could only have dreamed of in the past. Machine learning algorithms can now analyze vast data sets in real time, providing deeper insights into market trends, risk assessments and customer behaviour. AI-driven tools have streamlined operations, improved customer service and enhanced investment decision-making.

Financial institutions are also preparing for when large-scale quantum computers with powerful error correction have the potential to crack the asymmetric encryption methods used as the bedrock of communications for the banking industry. That day is still some years away but HSBC and other leading banks are taking proactive steps to modernize cryptography management, including implementing crypto-agility and migrating to post-quantum cryptography to secure infrastructure, financial assets, intellectual property and customer information. Financial institutions should begin the discovery process today to lay the groundwork for this lengthy, complex transition.

Link: https://www.weforum.org/agenda/2024/01/ai-quantum-technologies-transforming-financial-industry/?utm_source=pocket_saves

Source: https://www.weforum.org

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If before betting depended mainly on manual analysis and probabilities calculated by experts, today sports betting apps employ advanced technologies to offer a more accurate, faster and safer experience.

Today we will explain how technology is marking the course of sports betting in mobile applications and its impact on users.

The technological evolution in sports betting

Sports betting has come a long way, from traditional methods in physical premises to the sophisticated digital platforms we know today.

Betting in its beginnings depended almost exclusively on the player’s intuition and the basic information that he could collect. With the arrival of the Internet, this changed, allowing users to access statistics, probabilities and results in real time.

Thanks to this, users began to receive statistics and predictions based on historical data and behavioral patterns. However, the most significant change came with the integration of artificial intelligence, which took these tools to a whole new level.

Today, sports betting apps not only provide real-time data, but also analyze trends, dynamically adjust odds, and personalize the user experience based on their preferences and habits. This technological evolution has improved the accuracy of betting and is becoming more attractive to the public every day.

What does AI bring to sports betting apps?

Artificial intelligence is transforming sports betting apps by offering tools that were previously unthinkable. This advancement not only improves the user experience, but also boosts the efficiency and security of the platforms.

More accurate predictions

One of the greatest advantages of AI is its ability to analyze large volumes of data in real time. This allows for more accurate predictions on the results of matches and sporting events.

Machine learning algorithms process historical statistics, current team performance, and even external conditions such as weather or player injuries. For bettors, this means making informed decisions with a higher probability of success.

Personalized experience

Modern sports betting apps use AI to personalize each user’s experience. They analyze behavior, preferences, and betting histories to offer recommendations tailored to individual interests.

Security and fraud prevention

AI helps identify suspicious patterns that could indicate fraud attempts or unusual activity, protecting both users and platforms.

Automated customer support

AI-based virtual assistants and chatbots have revolutionized customer support in betting apps. These systems are available around the clock, answering common questions and helping users resolve issues quickly and efficiently.

Optimizing odds

Algorithms analyze the progress of sporting events and other external factors to offer constantly updated odds. This gives users a more exciting experience that is in line with the reality of the game.

These tools not only improve accuracy and security, but also offer a richer and more personalized user experience.

The future of AI in sports betting

Artificial intelligence will continue to refine predictive analysis, offering more accurate forecasts by processing complex data such as player emotions and specific match situations.

In addition, with the arrival of virtual reality, it will allow for immersive and dynamic experiences in real time, while advanced automation will facilitate the placing of bets based on personalized parameters.

Finally, AI will reinforce security and responsible gaming by detecting risky behavior and offering personalized assistance. With these innovations, platforms will be more efficient, secure and accessible to a wider audience.

The future of sports betting is more connected to technology than ever, and AI is the key to ensuring that this evolution benefits everyone involved, offering a safer, more exciting environment that is adapted to modern needs.

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Banks have historically been at the forefront of technological advancements, they are renowned for using computers as well as providing internet-based financial services. However, the rise of AI has brought with it a new dawn of innovations. These days, artificial intelligence (AI) is disrupting the entire banking sector in several ways.

These technologies range from customer support through chatbots to assisting in deterring complex frauds in the industry. Their innovations have enabled banks to provide customized solutions, operate more efficiently, and minimize risks better when compared with conventional methods.

Recent industry reports suggest the global AI in banking market size stood at $3.88 billion in 2022 and this figure is projected to hit $64.03 billion in 2030 at a CAGR of 32.6% from 2021 to 2030.

Therefore, the use of AI in banking continues to expand and introduce new vistas while reshaping financial services. Feeling curious to know how AI in banking is used? This blog post deals with how AI works in the banking sector and its impact on revolutionizing finance generally.

Why is AI needed in banking?

AI is picking up the pace in the banking sector mainly because it enhances customer service delivery, reduces fraudulent activities, simplifies credit scoring processes, and enhances risk management mechanisms. AI has found its way into banking systems driven by the significant cost savings, efficiency gains, and security enhancements that it comes with.

One significant factor for the increased usage of AI in banks is improving customer service quality. AI chatbots or power virtual assistants enable 24-hour seven days support which handles routine questions as well as transactions quickly and efficiently. It not only reduces waiting periods but also involves personal interactions for better customer satisfaction.

Fraud detection is also essential for AI to enter the area of banking. This has become a significant issue with modern cyber-attacks becoming more sophisticated by the day such that financial fraud stands out as one major concern among many others especially in the context of AI in banking. It is here that AI algorithms using real-time transaction data analysis on a huge scale can identify any irregularities and raise alerts on possible fraud activities.

The international business will spend more than $10 Billion on AI-based financial fraud detection and prevention by 2027 the study from Jupiter Research acclaims, reflecting an increase from $6.5B in 2022; hence, it proves the rising significance of artificial intelligence’s role in safeguarding monetary transactions.

As a result, the integration of artificial intelligence (AI) into banking is being motivated by the need to enhance efficiency, streamline customer service, and bolster security measures.

What major applications does AI have in banking?

The introduction of efficient, secure, and customer-friendly alternative solutions has been made possible by the introduction of Artificial Intelligence (AI) in the banking industry. Major areas where AI has been applied in banking comprise customer service, fraud detection, credit scoring, risk management, and process automation, among others including investment management and security as well. Here is how AI in banking works in different aspects

1. Customer Service

Banks employ AI chatbots to serve customers around the clock. This technology helps to simplify many functions such as customer account management and answering basic questions by customers about various bank products or services when there are no human employees on duty.

Erica is one of these systems developed by Bank of America for instance, which provides personalized financial advisory services among other banking-related services too. These systems analyze personal customer data to give product recommendations as well as financial advice possible with such systems which in turn leads to more tailored and pleasant banking services that increase loyalty among the clients resulting in repeat business.

2. Fraud detection and prevention

Using artificial intelligence, banks can monitor transactions in real-time to identify unusual patterns that may detect potential fraud cases as they happen. This helps them to track accounts in real-time and flag any suspicious activities, hence reducing financial fraud incidences.

AI also uses historical data that help predict or prevent future security breach incidences meaning it identifies those things which lead up to such breaches before they occur. Consequently, banks can stop fraudsters in their tracks because they have been stealing money from innocent people’s accounts which include yours whose funds still remain intact.

3. Credit Scoring and Loan Approvals

Artificial Intelligence takes time to analyze more data sources including social media activities other than just credit bureau records when determining eligibility for borrowing this lowers default rates significantly especially among people with limited credit history.

Similarly, AI takes less time evaluating loan applications, thus speeding up credit decisions and making them more customer-oriented besides increasing operational efficiencies by decreasing the approval processing timeline for loans.

4. Risk Management

Artificial intelligence helps in recognizing such things as trends in markets as well as predicting financial stability enabling banks to make prudent judgments before developing risk management plans which are preventive-based concepts anyway on time.

It assists in continually checking transaction records against prevailing laws all around the globe while at the same time coming up with compliance reports just so that there will be no non-compliance penalties whatsoever during checks done internally rather than externally making sure they adhere fully without any kind of error.

5. Process Automation

Data entry, and onboarding new clients’ transactions; among other repetitive manual activities such as customer service can be easily done through automation software tools developed with artificial intelligence technologies for bank installations.

Because of the AI, operational costs and human errors are minimized leading to more efficiency. Banks attempt to integrate AI in most of their services from internal operations to financial accounting systems which take place right there inside a bank.

6. Investment and Wealth Management

Wealth management is democratized by AI-powered robo-advisors who offer low-cost financial planning services without involving humans very much in the process. For instance, AI-powered software can automate an investment strategy based on historical stock market data and other relevant information sources. Thereby leading to intelligent decision-making while driving the performance of client portfolios through personalized advice.

7. Customer Insights and Marketing

AI uses customer behavior, transaction patterns, and preferences, hence recognizing their needs. The latter helps a lot in product offers and strategies used by different banks. Also, they can learn this way what various people will be interested in buying.

8. Enhanced Security

When it comes to security enhancements such as those made through biometric authentication measures i.e. facial recognition or voice print analysis would work well with AI. Biometrics face recognition, Voice Recognition, and Fingerprint Scanning systems empower banks to strengthen their existing security system.

Conclusion

AI revolutionizes banking by spearheading change within financial institutions that leads to high levels of productivity, safety, and customer satisfaction. From delivering superior customer experiences to improving credit scoring systems, AI has taken over various functions within banks.

This huge shift is attributed to real-time analysis of big data, provision of personalized engagements, and forecasting abilities that are unattainable through traditional methods. It will transform into a dynamic and all-inclusive ecosystem within an undeveloped banking structure.

Link:http://insideainews.com/2024/07/26/ai-in-banking-how-artificial-intelligence-is-used-in-banks/

Source: http://insideainews.com

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This article is authored by Mohit Gupta, co-founder, IndiaP2P.

Link: https://www.hindustantimes.com/ht-insight/future-tech/artificial-intelligence-and-machine-learning-in-credit-risk-assessment-101718255636906.html

Source: https://www.hindustantimes.com

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Link: https://www.analyticsinsight.net/artificial-intelligence/the-transformative-impact-of-ai-technology-on-the-gaming-industry

Source: https://www.analyticsinsight.net

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Harnessing Generative AI for Strategic Cost Reduction in Businesses

Link: https://www.analyticsinsight.net/generative-ai/how-generative-ai-is-used-for-cost-reduction-for-businesses?utm_source=pocket_saves

Source: https://www.analyticsinsight.net

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Artificial intelligence, particularly generative AI, is redefining the aggressive fintech landscape. The McKinsey Global Institute recently predicted that generative AI could add an additional $200 to $340 billion in annual value to the banking sector—representing a potential 9-15% uplift in operating profits.

As industry players level up their game, Visa is betting big on AI, releasing over 500 generative AI applications to drive innovation and productivity spike at scale.

The vision for AI integration isn’t just about reducing costs; it’s about redefining workflows. Visa has poured $3.3 billion into AI and data infrastructure over the past decade, most recently channelling $100 million into generative AI startups. The company’s AI-backed credit approval systems even bridge service gaps for banks during network disruptions.

Credit: The McKinsey Global Institute

AI + Humans is the Future

Visa’s approach to AI is less about workforce reduction and more about enhancing human oversight. “My vision is for Visa to have AI-generated digital employees overseen by human workers. Any given human employee can oversee, on average, eight to 10 AI employees that are trusted with a variety of tasks,” said Rajat Taneja, the president of technology at Visa.

Meanwhile, other technology leaders also seem to echo this belief. “I do think there’s a potential to have a high percentage of jobs automated, but I also think there’s a great potential to bring a lot more efficiency to jobs,” said Deb Lindway, executive vice president & head of AI, PNC, in an interview.

BYOT (Bring Your Own Tools) 

According to Mckinsey, GenAI comes with plenty of risks, such as misinformation, IP issues, transparency gaps, bias, and security concerns. Sustained value demands moving beyond initial trials and addressing these challenges. It is for this reason that AI at most fintech companies is still largely internal, and not consumer facing.

“We do not have customer-facing use cases. At the moment, all of these cases are internal, and there’s a natural sequence. There have been interesting tales from companies that have gone live with generators and chatbots, and then there have been specified pricing terms that have turned into lawsuits. The legal precedent set is very clear: a customer channel is an extension of the company, and they are accountable for what an AI system generates. Therefore, we want to be very careful before we go live with those,” said Derek Waldron, chief analytics officer at JPMorgan Chase.

Unwilling to play the waiting game, many fintech employees are bringing their own tools to work, even if their companies haven’t officially rolled them out yet. However, caution needs to be exercised here.

“There’s definitely a demand for companies to come up with policies, and come up with ways for these tools to be used because when people just use them rogue, that can create some issues, and can create some inequities,” said Amanda Hoover, senior correspondent at Business Insider, on the need for companies to formally adopt these tools internally to ensure uniformity.

Use Cases of GenAI in Banks

Earlier this year, JP Morgan launched Quest IndexGPT, using GPT-4 to enhance thematic index construction for institutional investors, and introduced LLM Suite, an AI assistant for 60,000 Chase employees to aid tasks like writing emails. Ranked first in the 2024 Evident AI Index, the financial services firm headquartered in New York leads in AI adoption in finance.

Not willing to be left behind, Morgan Stanley launched AI @ Morgan Stanley Debrief around the same time in a bid to boost advisor productivity. It also released the AI @ Morgan Stanley Assistant, adopted by 98% of advisor teams. Jeff McMillan, the head of analytics, data and innovation at Morgan Stanley wealth management and Firmwide AI, describes AI as a window of opportunity. “I’ve never seen anything like this in my career, and I’ve been doing artificial intelligence for 20 years,” he said.

Meanwhile, Visa’s archrival Mastercard has brought in a new AI technology that improves payment security by detecting compromised cards twice as fast and reducing incorrect fraud alerts by 200%. It also identifies at-risk merchants 300% faster, helping spot complex fraud patterns and protect future transactions.

This upgrade strengthens Mastercard’s Cyber Secure program, giving banks and merchants better tools to protect customer data. With faster, more accurate alerts, banks can act quickly to prevent fraud, block compromised cards, and build trust in the payment system.

However, there are areas in finance that are likely to remain AI-proof for a long time to come. “Customer relationships are always going to be high value and irreplaceable. I think finance will always have a very big element of human trust and relationships are going to be very important,” said Derek Waldron, chief analytics officer at JPMorgan Chase, on areas in the financial sector that will win the AI disruption, however, he caveated this with this gap constantly reducing.

GenAI Push in India 

In India, AI applications are also transforming banking operations. HDFC Bank, for example, adopted an AI-driven approach to fraud detection, which has proven essential as fraudsters continue to adapt to traditional rule-based systems. In 2017, the bank launched Eva, India’s first AI-driven banking chatbot, handling millions of queries instantly and setting a new standard for customer service.

By 2020, ICICI Bank introduced iPal, enabling transactions via voice commands through Alexa and Google Assistant, but discontinued it in 2021. In 2023, SBI announced an AI initiative to boost decision-making and operational efficiency, planning advanced data systems and fintech partnerships to enhance co-lending.

Link: https://analyticsindiamag.com/ai-features/ai-is-eating-fintech/

Source: https://analyticsindiamag.com

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Maybe ToT is a temporary freak in a crypto asset bubble. Or, maybe it’s a preview to a lasting change in how humans build and use computer technology. AI agents are now operating autonomously in the economy, owning assets, creating narratives and coordinating human activity – without the need for human operators behind keyboards.

Tokenization mattered here because it gave the AI a direct route to form its own market presence. By existing as a tradable asset, ToT could attract capital, demonstrate credibility and grow – without teams of developers and marketers. It proved that an AI agent can achieve economic influence when structured as open, tokenized software – rather than a closed, centralized system.

AI agents represent the cutting edge of computer technology in 2025. In the past, any emerging technology like this would be the province of well-capitalized research laboratories or Wall Street hedge funds. Today, projects like Virtuals Protocol and AI Agent Layer are already building platforms where AI agents can be developed, tokenized, marketed and traded. As a software revolution, AI has a chance to be more inclusive, with autonomous AI agents and blockchain-based infrastructure taking the place of costly and complex computer logic. To achieve this, these platforms will need to securely mint tokens via API – and likely have those tokens move across multiple blockchains.

From Memes to Mainstream

ToT’s rapid rise meant more than a surprise windfall. It showed that tokenized AI agents can operate as genuine economic players. They are not serving as back-end tools or following predefined scripts; they are setting terms and seizing opportunities. Instead of submitting to external management, a tokenized AI agent can direct its own treasury, align incentives with its stakeholders, and adapt to feedback from a global user base.

The implications are huge: AI systems can now solve problems and generate wealth autonomously, creating and capturing value without constant human oversight.

The current landscape of tokenized AI agents might seem frivolous, but the underlying logic is sound. Tokenization makes these agents simpler to fund, launch and distribute. It transforms what once required armies of programmers, back-office personnel, marketers, lawyers and salespeople into a process in which code is deployed once and runs reliably and autonomously, in perpetuity.

Infrastructure Requirements

For platforms like Virtuals and AI Agent Layer to operate effectively at scale, they need an easy way to mint and manage tokens via API. Platforms for minting tokens exist today: Pump.fun is the most current example. These tools are associated with lightweight uses – memecoins, or the rapid tokenization of new internet obsessions. For AI agents to realize more consequential economic potential, institutional-grade infrastructure is required. Reliable, secure protocols must safeguard these minting tools from faults and undue risk.

Security is an obvious baseline requirement for such tooling, protecting minting functions from abuse by attackers and safeguarding the ownership rights expected by tokenholders. In addition, I believe issuers will desire minting tools that extend across multiple blockchains. Once a token is created to represent an AI agent, it should be deployed across as many chains as possible. This allows agents to tap into liquidity, utility and users across ecosystems, maximizing their potential impact.

Interoperability ensures that an AI agent can move where the opportunities are, while robust protocols deter malicious actors. Without this foundation, tokenized AI agents will remain curiosities rather than reliable contributors to the global economy. The Interchain Token Service (ITS) is one project tackling these challenges, enabling rapid deployment to multiple chains while maintaining security.

The Automated Economy

When the infrastructure matures, tokenized AI agents will find roles in multiple sectors. They can deliver financial services without human overhead, run customer support operations continuously, streamline compliance monitoring and handle content production at scale. They might design investment portfolios, answer queries, develop go-to-market campaigns or produce data-driven insights for many organizations at once. Tokens can be used as payment mediums, governance mechanisms or simply fractional ownership. Because they represent themselves as tokens with transparent rules, their path to market is simpler and their potential reach is global.

As more agents take root, a network of self-directed market participants will emerge. These agents will coordinate supply chains, settle financial contracts or manage data pipelines. Humans stand to benefit from greater efficiency and lower costs.

They can focus on conceptual development and complex problems, while the agents address routine assignments. This is not a vague promise. It is the logical extension of what we are already seeing, only scaled up and refined.

To move from a single extraordinary event to a stable ecosystem, infrastructure providers, blockchain developers, investors, and entrepreneurs should streamline token-minting processes, refine cross-chain tools, strengthen security standards and ensure transparency. Platforms that simplify AI agent creation and management will not just disrupt markets; they will build the foundation for a more value-driven, connected and innovative economy.

Link: https://www.coindesk.com/opinion/2025/01/09/the-ai-revolution-will-spawn-millions-of-new-tokens?utm_source=pocket_reader

Source: https://www.coindesk.com

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