AI in Fintech: Benefits and Use Cases

Read about the transformation AI brings to the fintech industry and learn to leverage it to excel in your business.

Barely a week goes by for finance experts before new AI innovations begin to gain momentum and make headlines. While some claim to resolve current pain points, others present new possibilities for AI in fintech.

These innovations are giving rise to fintech applications and encouraging fintech development companies to build AI fintech apps. Some of the top generative AI companies have witnessed breakthroughs in recent times by developing futuristic AI models. If you are also looking for the top AI development companies to invest in fintech apps, it’s the right time.

Before discussing further, we will look at the dominance of AI in fintech. You will know how AI plays a crucial role in the finance industry. How can AI in the fintech industry transform your financial management? Who are the key players exploring the potential of AI? What new inventions should you watch out for?

Let’s begin the discussion.

Role of AI in Fintech

It’s 2025, and the competition among fintech organizations is more fierce than ever.

Every single fintech organization has been trying to outdo and explore the potential of artificial intelligence. In the coming times, AI will change the way we manage money, banks operate, and fintech firms function.

Not only fintech firms, Gen Z and Millenials are the top user base for generative AI companies. Recent statistics show that:

• 65% of generative AI users are millennials or Gen Z. (Salesforce)
• Experts expect the market share for global AI in fintech to grow at 16.5% between 2022 and 2030. (Grand View Research)
• In 2025, AI in fintech market share could reach $18.31 billion. (Mordor Intelligence)
• About 72% of financial leaders accept that their departments use AI for fraud detection, risk management, investment purposes, and automation. (Payset)
• Major fintech leaders using AI are Upstart, Ant Group, Zest AI, Cognitivescale, Kiva, PayPal, Mastercard, Stripe, etc. (Globe Newswire)

Undoubtedly, the finance world expects new innovations to skyrocket with the help of AI, ML, big data, etc., and generate more revenue in the future.

What Are the Benefits of AI in Fintech?

AI in fintech has brought enormous advantages over the traditional approach in money transfer, payment, lending, etc. A few of the benefits of AI in the finance sector are as follows:

Improved Accuracy and Efficiency

AI can effectively enhance accuracy and minimize error by automating routine tasks, ensuring timely service delivery, and removing anomalies. AI development companies can focus on training AI models with diverse datasets, regulatory compliances, etc., resulting in less error, higher efficiency, and more accurate forecasts.

Task Automation

Automating routine tasks through AI tools can result in high-performing products. Most fintech firms use AI tools for tedious clerical task automation, virtual assistance, sorting financial data, analyzing massive structured and unstructured data, and more.

Cost Reduction

A reduction in error and time can significantly reduce overall operational costs. When AI can improve accuracy, enhance labor efficiency, automate manual tasks, and save time doing tedious work, you can save many expenses. Hire only the required labor and let AI handle the rest.

Improved Customer Experience

Gone are the days when customers used to wait hours to get their work done. Waiting in the bank queue was the norm. Nowadays, most financial organizations have upgraded their systems with AI software to automate the majority of tasks. So customers can send their queries to official online apps, talk to robo-advisors, and get the work done in considerably less time.

More Opportunity for Personalization

Fintech development companies can leverage the power of AI to deliver more personalized services to their clients. AI-driven personalized financial services can lead to improved customer interaction, brand recognition, and higher revenue generation. The AI models can effectively assess customers’ monetary habits, derive financial planning, and offer recommendations accordingly.

Scalability

Scalability is one of the advantages of using generative AI. Fintech development companies can leverage generative AI to upgrade existing systems to address customer pain points by introducing new technological solutions.

It’s safe to say that in the coming years, AI will surpass human capabilities significantly. The coming times will witness a new world where most financial firms will rely on AI to make every decision.

Let’s look at the top fintech startups exploring the full potential of AI in fintech.

Top 5 AI Fintech Start-ups in 2025

Thousands of startups have been striving towards harnessing the potential of AI. Still, only a few stand out. It could be due to their unique approach, breakthrough innovations, or ingenious ideas.

1. Gynger

It claims to be the first embedded financing platform that offers its diverse services to both buyers and sellers. This startup emerged as a complete technological solution for lending money, managing payment processes, and handling finance.

Users can conduct massive transactions daily across various vendors, including AWS, HubSpot, Google Cloud, Okta, etc.

They have raised $20 million from PayPal ventures to enhance corporate technology purchasing. Vendors can scale their sales, improve cash flow, and enhance revenue. They can do this all while getting payments via the embedded financing platform.

2. Robinhood

The intention to democratize finances for all is what makes Robinhood unique in the fintech industry. Its aim is to make trading affordable and inclusive for all groups.

Customers can take advantage of commission-free trading, mobile investment facilities, cryptocurrency wallets, credit cards, crypto trading, and other banking services, all on a single platform. For 2024, the company registered $1.01 billion in revenue, an 115% increase, and a net income of $916 million.

3. Square

Implementing Square in business operations is like having a trusted partner. It supports sellers of all sizes—beginners, large, enterprise-level businesses, and merchants. Sellers can diversify revenue streams, accept card payments, manage inventory and staff, and execute transactions.

They have partnered with Sysco to deliver intuitive tech solutions. Now, restaurants can pre-authorize bar tabs, payout instantly, and set up house accounts. It further simplifies cash flow and account management.

4. Stripe

Which is one of the renowned names for online payment processing? You guessed right, It’s Stripe!

This Dublin-based company offers financial services to businesses and startups of all sizes in over 100 countries.

Taking into account the ongoing advancements in fintech, Stripe continues to invest more in R&D focused on artificial intelligence and stablecoins (a cryptocurrency). Most Fortune 100, Forbes Cloud 100, and Forbes AI 50 companies use it for payment processing.

5. Jump App

Despite being a two-year-old fintech startup, it is already redefining wealth management and helping fintech advisors simplify their workflow. According to recent news, they acquired $20 million in Series A funding.

The motto is to meet the rising demand for AI-tailored tools for financial professionals. So they could streamline administrative tasks and enrich client interactions.

Several advisory firms, independent broker-dealers, and enterprise-level registered investment advisors have adopted their artificial intelligence assistant. A few notable names are LPL Financials, Mission Wealth, and Sanctuary Wealth.

These are a few notable financial tools developed using artificial intelligence and other new technologies. Now we will shed light on their applications in the finance industry, impacting day-to-day lives.

Read more to learn about top AI development companies

Top Examples and Use Cases of AI in Fintech

The use case in the finance industry ranges from loan underwriting to providing excellent user experience. Consider the few following ones we have listed below.

Automated Financial Reporting

New technology makes it easier to collect data, conduct analysis, and produce accurate financial results. This way, businesses can:

• Automate regulatory adherence,
• Monitor transactions in real time,
• Assure smooth customer onboarding,
• Supervise loan processing,
• Flag violations instantly, etc.

As it lowers the risk of financial mismanagement, businesses can improve accuracy, considering no legal, financial, or security risks.

For example: Booke AI, Trullion, Domo, etc.

Investment and Wealth Management

Artificial intelligence in wealth management has taken a major leap. Not long ago, people used to search for financial advisors and pay a hefty amount.

However, the focus has now shifted to AI-powered tools. You can easily identify associated risks and potential impacts on your investment portfolio.

For example: Cleo AI, Trim, Candlestick, Mezzi, etc.

Robo-advisors

Robotic advisors are the new sensations in the fintech industry. These algorithm-based wealth management and investment advisors require little to no human involvement. If you do not wish to research or keep updated with the financial news, turn to robot advisors.

For example: SoFi, Vanguard, Wealthfront, etc.

Hyper-personalized Customer Experience

AI-powered chatbots and visual assistants have seen an uproar in the fintech world for the last few years. Most artificial intelligence development companies provide chatbot development services to major fintech startups and companies.

These chatbots help people by

• Being available for financial advice 24/7,
• Giving instant responses,
• Sharing personalized advice based on user data,
• Possessing the ability to handle a high number of inquiries,
• Effectively managing complex IT support, etc.

Personalization is the number one thing that people ask when it comes to finances, which AI can effectively do.

Algorithmic Trading

Algorithmic trading, also known as algo-trading, automated trading, or black-box trading, helps traders using a computer program that follows a pre-defined algorithm. Most financial firms use algorithmic trading, which uses historical data to assess patterns, check market conditions, reduce human errors, instantly place trading orders, and more. Many trading platforms allow algo-trading under terms and conditions.

For example: Oanda (One of the best trading platforms for algo-trading in the USA)

Predictive Analysis

The primary role of AI in fintech is to make accurate predictions, effectively reducing human error and analyzing massive data in seconds. AI-powered financial tools use machine learning and statistical analysis to make accurate predictions.

The analysis also considers behavioral recognition and forecasting upcoming events to generate predictable outcomes. Predictive analysis is primarily beneficial for risk reduction, operational improvement, customer segmentation, periodic maintenance, abnormalities detection, etc.

Fraud Detection and Prevention

Financial fraud is more common than we could accept. The banking industry has always been the main target for scammers and hackers. The implementation of AI and ML ensures that fraudulent activities get caught sooner and more effectively. The AI/ML techniques can identify spam data by interpreting large chunks of data and analyzing irregular patterns.

For example: Mastercard’s Consumer Fraud Risk, Swift GPI, Feedzai, ComplyAdvantage, etc.

So, here we have the best use case and examples of using AI in fintech. These AI-powered fintech solutions are attracting millennials and Gen Z and encouraging them to invest and be financially literate.

However, these AI-powered advancements come with a few notable challenges. We will now discuss them in detail.

Challenges of AI in Fintech

We will highlight the major challenges fintech startups and companies using artificial intelligence are facing.

Data Privacy and Security

Despite the financial sector being highly regulated, data privacy and security issues still weigh heavily. How many times have you received an unknown message or phone number? Or How often do we come across spam emails from unknown websites? AI bot attacks are well-heard of these days.

The reason is that some app development companies in fintech lack infrastructure, resources, or tech experts in this field. Most fintech organizations with third-party involvement neglect ethical misconduct with customer data, which also hampers trust and credibility in the business. As a result, it tangles organizations into long financial, legal, and security battles.

To say the least, regulatory framework compliance is extremely vital for legacy systems to work alongside new AI software.

Legacy Systems Compatibility

Modern systems easily adopt the new advancements in the fintech world. However, new AI and ML technologies can be complex for legacy systems. These legacy systems are still deeply ingrained in most fintech organizations.

Most often, lack of required data structure, processing power, and scalability hinder the implementation of new AI systems. The difference in data storage formats and structures also poses incompatibility with the AI tools, increasing potential risks and disruptions.

Therefore, fintech organizations need to develop a strategic plan, train their workforce, and replace outdated systems to effectively address these challenges.

Demand for Technical Experts

New artificial intelligence systems are the amalgam of several modern tech innovations, such as blockchain, virtual reality (VR), augmented reality (AR), extended reality (XR), deep learning, machine learning, quantum computing, etc.

These areas required a certain level of technical expertise, experience, and knowledge. Fintech organizations eager to introduce modern AI tools into their existing systems need to take such requirements into consideration. It’s vital for the organization to enhance the skills of their employees to smoothly operate AI tools.

Cost and Implementation Challenges

The integration of modern AI tools with the existing system is not a one-time event. Periodic upgrades, continuous monitoring, security, and maintenance for the fintech software require significant financial assistance.

Additionally, training staff, acquiring resources, and managing operation expenses to maintain a smooth workflow is also a challenge for most fintech firms.

Despite the challenges, the future of AI is bright and promising. Many organizations have adopted digital workflow, increased efficiency, and boosted personalized experience.

Now we will discuss what the future of AI in fintech holds.

Future of Fintech with AI

Integrating AI into financial operations and leveraging new innovations, like Gen AI, will be the key to staying ahead in this highly competitive landscape. Look at the few new innovations that took place recently, showcasing the bright future of fintech with AI.

• Agentic AI will reshape the business lending processes with the help of big data and data science. It can quickly analyze massive data, help banks approve loans, assess risks, recommend investments, lower operational expenses during loan processes, and more.
• AI-powered agentic solutions (introduced by Shaffra) are here to automate financial workflow and improve decision-making. This bold step will allow the banking sector and other financial firms to integrate AI employees to streamline operations.
• With the rise of digitalization in the fintech industry, chatbots are becoming one of the most adaptable technologies.Example: Bank of America’s Erica emerges as a next-gen chatbot that can give investment advice, manage accounts, and more.
• Many fintech firms are focusing on upskilling the staff to leverage generative AI capabilities, including automation and data analysis.
• With the help of blockchain technology, fintech companies are exploring more innovative AI-powered financial solutions.Example: AnChain.AI is strengthening the global crypto environment by overseeing millions of dollars of transactions and providing security to crypto platforms.
• There is a rise in AI copilots and AI web crawlers to tailor investment strategies, predict market trends, gather financial news, track market trends, consider customer sentiments on social media, and more.
• Some of the most recent AI innovations in the fintech industry are:
  • Voice-first banking.
  • Central bank digital currencies, such as Britcoin.
  • Green fintech.
  • AI-powered personalized banking.
  • Decentralized identity solutions, such as Privado ID.
  • Quantum-resistant cryptography.
  • Embedded finance (Seamless integration of financial integrations with non-financial platforms or applications)

Being a visionary tech enthusiast will bring phenomenal results to your AI fintech business. Numerous fintech startups and professionals attend fintech summits to build networking, share new AI ideas, and join panel discussions.

If you are one of those tech enthusiasts, take inspiration from top generative AI startups. Learn the full spectrum of the fintech ecosystem and dive into the massive potential of sustainable AI advancements.

AI in Fintech: Final Thoughts

AI in fintech brings immense opportunities with the amalgam of machine learning, AR/VR, blockchain, big data, etc. In the past few years, AI has brought people convenience, speed, automation, and flexibility when it comes to wealth management, risk management, data analysis, and more.

AI fintech applications can now address issues that traditional banking systems either fail to address or take eons to resolve, enhancing customer experience and swiftly adapting to market movements. Though new changes come with challenges hand-in-hand, however, fintech AI apps will also emerge with new digital experiences.

Frequently Asked Questions (FAQs)

Q. How is traditional AI different from generative AI?

Traditional AI is useful in predictive analysis, NLPs, and autonomous systems. On the other hand, generative AI is primarily used for research work, content creation, design, and generating information, including images, text, video, etc.

Q. What are the ethics of AI in fintech?

The ethics of AI in fintech ensures that the implementation of AI models occurs in transparent, fair, and accountable ways. There must be no discrimination in rendering fintech services based on race, color, geography, occupation, etc.

Q. What is the major challenge of AI in fintech?

The AI models are heavily dependent on the data. Some financial institutions lack data accuracy, have incomplete data, or have errors in their data. It leads to inaccuracy in results and delays the desired outcome.

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