Have you noticed how financial companies are having a hard time keeping up with today’s market? Even with all their fancy systems, many still lean on outdated tech for tasks like risk management, fraud detection, and data analysis. And here’s the problem: those old-school methods don’t work anymore. The gap between what customers want and what they actually get is huge.

 

The pressure only mounts as financial markets move at breakneck speed and data piles up. Without better tools, banks and firms can’t make quick decisions, miss chances, and stay one step behind fraudsters. It’s like running a race while carrying weights—no wonder customers are frustrated.

 

So, what’s the solution? Generative AI is our new hero. According to a report we came across, between March and June 2023, the number of financial companies using generative AI jumped from a modest 5% to nearly half—49%. This tells us one thing: AI in finance is no longer optional, it’s a must for success in today’s business world.


At Inoxoft, we know this space inside and out, with years of experience and 200+  successful projects under our belt. We’re excited to share our insights and show how Gen AI can transform your financial operations for good. Let’s go!

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Contents

TL;DR

• Over the second quarter of 2023, the use of AI in the financial industry rose from 5% to 49%, highlighting its growing popularity and importance.

• In 2023, over two-thirds of financial businesses used AI for data analytics, making it the top AI application in the field, followed by data processing and natural language processing. 

• Common AI uses in finance include fraud detection, robo-advisors for personalized financial advice, risk management, predictive analytics, automated customer support, and many others.

• The focus on security and compliance, the use of synthetic data for research, and the integration of AI with other technologies are the trends that will define the future development of AI.

Generative AI in finance examples:

• After implementing the AI solution for report generation, our client reduced error rates from 5% to less than 1% and saved $500,000 in the first year.

MasterCard has prevented over $20 billion worth of fraud just 12 months after implementing the AI-powered fraud detection system.

Morgan Stanley is expanding its use of AI with a new assistant that will save thousands of hours of work for the bank’s financial advisors.

Wells Fargo’s new AI-powered LifeSync platform simplifies financial planning, engaging clients and strengthening advisor-client connections.

RBC Capital Markets’ AI-powered platform helps improve trading results and provides better insights for clients around the world.

Featurespace’s new Large Transaction Model (LTM) increased fraud detection by up to 71% compared to standard models, while keeping the typical false positive rate of 5:1.

How Can Gen AI Be Used in The Financial Industry

Financial companies are swimming in data – think petabytes worth of transactions, customer profiles, market trends, and more. It’s no wonder that in 2023, according to Statista, over two-thirds of financial businesses worldwide turned to AI specifically for data analytics, making it the most popular AI application in finance, with data processing and NLP not far behind. But that’s just a glimpse of its full potential. As Mike Walsh, Futurist and CEO of Tomorrow, puts it:

“We’re really just at the beginning of trying to understand what an AI-powered revolution might look like when it comes to transforming organizations. The reason is that for many of us, generative AI has just become a tool for writing backup emails, rhyming birthday greetings, or summarizing memos you can’t be bothered reading. Most people just began to scratch the surface of what it can be really used for in terms of not just summarizing information in new and interesting ways, but becoming an indispensable platform for generating breakthrough insights.”

And AI’s impact goes way beyond just data analysis. It also transforms risk management, compliance, financial reporting, accounting, and even cloud pricing strategies. Plus, AI-powered voice assistants, chatbots, and scheduling tools are bringing new energy to teams across departments. But let’s get an overall look of how is AI used in finance, summarizing it in a table:

Use Case

What It Does

Automated Customer Support 

Chatbots handle complex questions, guide users through processes, and make customers feel heard 24/7.

Fraud Detection

AI models analyze transaction data to spot unusual patterns and potential fraud quickly.

Personalized Financial Advice (Robo-Advisors)

Generative AI helps create customized investment strategies based on individual user preferences.

Risk Management

AI simulates various scenarios to stress-test financial portfolios and help manage risk.

Automated Reporting Tools

AI generates detailed financial reports, saving time and reducing human error.

Credit Scoring

AI evaluates credit applications by analyzing multiple data points for faster, more accurate scoring.

Document Processing

AI automates the review and handling of financial documents, speeding up operations.

Predictive Analytics

AI forecasts market trends to guide decision-making and identify investment opportunities.

Sentiment Analysis

NLP technology reads and analyzes news, social media, and other sources to understand and measure market sentiment.

Optimized Portfolio Management

AI helps portfolio managers fine-tune asset distributions and adjust portfolios according to market shifts.

Voice Authentication

AI-powered voice recognition enhances the security of phone banking services.

Blockchain Transaction Monitoring

AI oversees blockchain activities to spot potential fraud and maintain compliance in cryptocurrency transactions.

Algorithmic Trading

AI develops trading algorithms to optimize investments by analyzing market data in real-time.

Compliance Monitoring

AI systems track transactions and communications to detect potential regulatory violations.

Want your project to look great and deliver even more benefits? Contact us now, and let’s kick off your tech journey. 

Real-Life Examples of How Gen AI Can Work in Your Company

“AI gives companies a way to solve all kinds of problems, but the right solution depends on what you’re trying to fix. Whether it’s making better strategic decisions, cutting down on some mundane tasks, or finding new insights, AI can adapt to fit whatever your business needs, helping your team work smarter and easier.”

– says our COO, Nazar Kvartalnyi.

Let’s look at some successful AI use cases in finance, including one of our recent projects, so you can see for yourself how this technology can improve your company’s operations, increase customer satisfaction, and boost team morale.

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    Our Project for FinTech Bank: Automated Financial Report Generation

    While this project is under NDA agreement, we’re sharing general information about the project. Our client is a mid-sized business offering all kinds of services: from retail and corporate banking to investment advice and wealth management. They came to us with a very specific problem, willing to cut down the time their team spent creating financial reports with an automation solution.

    The Challenge

    The company’s financial analysts were spending countless hours building reports – they had to analyze datasets, summarize complex financial details, and write up explanations – all by hand. The manual nature of this work took up days and hours, causing occasional errors and keeping analysts from bigger-picture tasks that could really add value to the business.

    Our Solution: Gen AI for Report Generation

    To help our client reclaim their time, we implemented an AI-powered solution for automated financial report generation. Here’s how we made it happen:

    1. Data collection and preparation. First, we collected all the important financial data, like balance sheets, income statements, and cash flows, plus market trends and other external information to round out the picture. We double-checked everything to meet quality and regulatory standards.

    2. Model training and development. Next, we trained a large language model (LLM) to analyze this data and generate clear, insightful reports. Our team also made sure that every report it generated was accurate and compliant by fine-tuning the model with specific templates and regulatory guidelines.

    3. Integration with existing systems. Finally, our engineers connected the AI solution directly to the client’s ERP and financial tools, so the new system fits great into their usual workflows.

    Tools and Technologies

    Our key technologies for this project were:

    1. Google Cloud’s Gen AI for developing and training our language model.

    2. BloombergGPT for in-depth financial data analysis and report generation.

    3. SAP ERP-powered integration for data sharing across systems.

    The Results

    Just like we all expected, this project brought major improvements for our client:

    Report generation time dropped by over 70%, from 5 days to less than 1 day, allowing analysts to focus on more strategic tasks.

    • Automated reports reduced the error rate from 5% to less than 1%, making them much more reliable.

    • Reduced manual effort saved the bank about $500,000 in the first year after implementation.

    • The analysts’ team reported an 80% increase in productivity since starting to use the AI solution.

    Generative AI in Finance: the Most Prominent Examples from Our Team and Worldwide

    We’d love to help you get similar and even better results! If you’re ready to see how AI gives your team more time for meaningful work, let’s connect!

    Mastercard’s Case: Enhancing Customer Service and Fraud Prevention

    MasterCard, a huge and well-known name in payment processing, deals with billions of transactions daily. But with big numbers come big challenges: keeping up with transaction security, supporting efficiency, and giving people a top-tier experience every time they swipe their cards. To tackle these, MasterCard turned to AI technologies.

    Instead of relying on outdated, slow methods, the company built an AI system that can spot fraud in an instant by looking at past transactions, finding weird patterns, and flagging anything that seems off. This way, customers’ money stays safe, and they don’t even notice anything happening behind the scenes.

    Ed McLaughlin, CTO of MasterCard, explained:

    “AI has been an essential capability for Mastercard for years now, and we see it only increasing in importance and impact. In the last 12 months, we’ve stopped over $20 billion worth of fraud, using AI.”

    But MasterCard’s AI isn’t just about security – it’s also about making life easier for people. The company has rolled out 24/7 AI-powered chatbots to quickly answer customer questions and handle simple issues. The company also uses AI to make the customer experience more personal. AI algorithms analyze spending habits and preferences to offer rewards and deals that actually matter to clients, instead of sending out generic offers that don’t mean much.

    McLaughlin also notes that MasterCard’s exploration of AI won’t stop, and they will continue finding new ways to get the most out of its capabilities. He also mentioned the company’s growing interest in quantum computing both from a security perspective and as a means to solve complex combinatorial problems that are beyond the reach of classical computing.

    Morgan Stanley’s Collaboration with OpenAI: Reinventing Financial Advisory

    Morgan Stanley, a leader in wealth management, has a massive library of knowledge and insights, including thousands of pages on everything from investment strategies to analyst commentary. Most of this content is spread across many internal sites, often in PDFs, so it’s pretty tough for advisors to find exactly what they need. 

    To make life easier for their team, Morgan Stanley decided to tap into their knowledge base using GPT’s powerful search and retrieval tools – starting with GPT-3 and now moving to GPT-4. This AI model powers an internal chatbot that scans their entire content library and delivers specific answers in seconds.

    Jeff McMillan, Managing Director at Morgan Stanley, put it best:

    “You essentially have the knowledge of the most knowledgeable person in wealth management—instantly. Think of it as having our Chief Investment Strategist, Chief Global Economist, Global Equities Strategist, and every other analyst around the globe on call for every advisor, every day. We believe that is a transformative capability for our company.”

    Right now, more than 200 employees use this AI tool daily and share feedback to make it even more effective. The idea behind the project is simple: give advisors the insights they need, in the exact format they need, as quickly as possible, and strengthen the connection between advisors and their clients.

    Morgan Stanley is also testing out other OpenAI technologies that could analyze advisor notes and make client follow-ups even smoother, pushing the boundaries of how they use AI to make their work more impactful.

    Wells Fargo’s LyfeSync Powered by AI: Easier Financial Planning

    Wells Fargo & Company, one of the biggest names in financial services, manages around $1.9 trillion in assets and supports over 10% of small businesses in the U.S. 

    Recently, they found that most people now prefer handling financial tasks digitally, including investments. In fact, around 80% of Gen Z and 79% of Millennials feel more comfortable managing their investments right from their phones. So, in spring 2023, Wells Fargo introduced LifeSync, a mobile-first platform designed to make long-term financial planning simpler for WIM clients.

    “LifeSync reflects what our clients want,” said Michael Liersch, head of WIM Advice and Planning. “They want a clear view of their goals and how they’re progressing, as well as a way to understand what’s influencing that progress—whether it’s market changes or their own actions. LifeSync does all this, while also making it easy for clients to connect with their advisors as their needs change.”

    Powered by AI technology, LifeSync gives clients personalized insights, integrates data like estimated net worth, portfolio performance, and credit score, and offers timely articles on financial wellness. The app also tracks real-time progress, monitoring key metrics such as market indices, FICO scores, and credit card reward balances. 

    LifeSync helps Wells Fargo Advisors have more meaningful conversations with clients, keeps clients engaged, and adds an interactive touch to their service.

    RBC Capital Markets’ AI-Powered Platform: Reimaging Trading 

    RBC Capital Markets, the fifth-largest bank in the US by capital market share, introduced its award-winning electronic trading platform, Aiden, in October 2020. Built with patented technology, it uses deep reinforcement learning to adapt in real-time to market conditions, continuously optimizing trading outcomes based on live data.

    Aiden’s latest algorithm pulls in over 300 different data points and action combinations, along with adjustments after each trade, to tackle the tricky “arrival price” problem and cut down on slippage. Using deep reinforcement learning, it learns from thousands of real-time decisions, constantly refining its trading strategies. 

    Remarkably, Aiden performed well even during the high volatility of the early COVID-19 pandemic – a challenging period for predictive AIs – adapting quickly to stay close to its target benchmarks.

    “Since launching Aiden, we’ve worked closely with clients to build a more versatile tool for primary benchmarks and achieve greater simplicity,” said Bobby Grubert, Head of Digital Solutions and Client Insights at RBC Capital Markets. “The launch of Aiden Arrival marks a major milestone in expanding our innovative trading platform. Together with Aiden VWAP, it opens new doors for clients to access higher-performing trading tools.”

    The Aiden platform is an important step in RBC’s plan to fully leverage AI for its clients in the future. Its launch was a major milestone, proving that this advanced AI technology can work even in the most complex trading situations.

    Featurespace’s TallierLTM: Revolutionizing Fraud Detection

    Featurespace leads the world in high-grade technology designed to prevent fraud and financial crime. With a mission to make transactions safer, it helps banks and financial institutions protect their customers, lower risk, and cut operational costs. 

    Now, Featurespace has introduced TallierLTM™ – the first Large Transaction Model (LTM) of its kind. As a foundational AI technology for payments and financial services, TallierLTM™ is a large-scale, self-supervised model designed to drive the next wave of AI applications for consumer financial protection.

    “What OpenAI’s LLMs have done for language, TallierLTM™ will do for payments. There is widespread concern about how deep fakes and generative AI have been used to deceive consumers and our financial systems. We plan to reverse this trend by using the power of generative AI algorithms to create solutions that protect consumers and make the world a safer place to transact.” – David Excell, Founder of Featurespace.

    Trained on data from various markets and regions, TallierLTM™ is highly accurate and reflects real-world transaction behaviors. By analyzing billions of transactions, it spots subtle transaction patterns that typical industry methods miss, helping data scientists distinguish genuine transactions from fraudulent ones. In tests, this solution improved fraud detection by up to 71% compared to standard models, while maintaining the industry-typical false positive rate of 5:1.

    What Future Holds for Gen AI in Finance: Our Predictions

    We recently came across an insightful article from the World Economic Forum that breaks down key takeaways from the latest Davos Meeting. Regarding finance, AI took center stage in the discussion, sparking both excitement and concern about how it could reshape jobs and impact power structures within the industry.

    As our Head of Delivery, Maksym Trostyanchuk explained:

    “We’re already seeing AI bring a lot of automation to the world of finance, but this is just the beginning. I believe that as the financial markets become more globalized, the complexity of managing risks will only grow. And that’s exactly where AI will come into its own, playing a crucial role when we need it the most.”

    Let’s explore some key topics discussed and predictions made about the future of AI in finance.

    Regulatory Compliance and Security

    Organizations need to manage their budgets wisely. When money is wasted, it harms public trust, so using resources carefully is important in both the public and private sectors. AI is now helping with this – it can track budgets, alert investigators to possible fraud, money laundering, or other shady activities, and make financial management easier overall.

    Despite all the benefits, there are still roadblocks to using AI, including complex procurement processes, skill gaps, limited data, lack of standards, and resistance to change. Solving these issues will be essential for using AI responsibly in the future.

    Governments are already setting up review boards, like ethics committees, to make sure AI tools are safe and responsible before they’re used widely. These boards are creating strict guidelines to prevent the overuse of technology. While this careful approach will ensure AI is ethical and secure, it could slow down the wider use of AI in the future.

    Synthetic Data Generation

    Financial data is packed with valuable insights but also locked up with strict privacy rules and regulations. This is where synthetic data comes to the rescue. It mirrors the patterns and structure of real data without revealing any actual information, making it a safe alternative that respects privacy laws. 

    As more financial companies start to use synthetic data, it’s expected to become a key tool in AI research and development. This technology will let banks and other institutions work with data that feels realistic, helping them develop smarter AI solutions while keeping data privacy intact.

    One platform already leading the way is MOSTLY AI. They specialize in creating synthetic data for finance, allowing banks to explore customer journeys and improve services in areas like account management and loans. 

    Integration with Other Technologies

    Generative AI is joining forces with other advanced tech to create a new generation of financial tools that are as sophisticated as they are practical. 

    For instance, by blending Gen AI with neural networks, financial teams can generate realistic, data-rich environments to test and train models without using sensitive data. Or, when combined with large language models (LLMs), Gen AI opens up new ways to handle reporting and client communications, letting teams focus on strategic moves. Also, with graph neural networks (GNNs) mapping complex transaction patterns, banks gain a sharper eye for spotting fraud and crafting better investment strategies.

    This layered, collaborative approach to technology is quickly gaining popularity across finance, where the need for smarter, faster decisions is ever-present. As one of our experts notes, 

    “Everyone thinks that pairing Gen AI with other technologies is only about improving speed, but it’s not. It’s also about smarter, more informed decisions that drive long-term growth and stability of any company.”

    Want to know more about how AI might reshape finance? Schedule a free consultation with our specialists to discover how you can leverage it for your own benefit.

    How We Can Help Your Financial Company Conquer the Market with AI

    Ready to see what AI can really do for your business? With 10+ years of experience in FinTech and 230 projects behind us, we know how to turn your ideas into effective tools. 

    Here’s how we can help:

    Custom AI & ML Application Development. Want solutions that fit your business, not just the latest trends? Our AI applications are designed to solve your specific problems and give you real results.

    Data Engineering That Delivers Insight. We build systems that don’t just gather information—they give you clear, usable insights that guide smarter decisions.

    AI-Powered Tools for Better Customer Experiences. Imagine chatbots and virtual assistants that help customers in a more natural way, or recommendation systems that provide advice based on real user needs. We make it happen.

    Fraud Detection That’s Proactive. Our AI-driven tools identify unusual patterns early on, adding an extra layer of security.

    Intelligent Process Automation (IPA). With IPA, routine work is handled automatically, freeing up your time for more important priorities.

    Generative AI in Finance: the Most Prominent Examples from Our Team and Worldwide

    And when it comes to AI and ML in financial software development, we’re not limited to one-size-fits-all. From customer segmentation tools to speech and voice recognition tech, we build exactly what your business needs to thrive. 

    So, whether you need a project partner, a dedicated team, or extra hands on deck, we’re here to fit in where we’re most useful. Our team of 170 experts is ready to help you, so you’re not just keeping up in finance—you’re defining the rules. 

    Contact us and start moving your business forward right now!

    To Sum Up

    AI in financial software development is changing the financial world, opening up exciting new possibilities for everything from banking and investing to insurance. Such advancements can make the future where finance is simpler, safer, and more tailored to you, with fewer errors and fairer outcomes. That’s what AI and machine learning can bring to the table.

    With the right policies, these tools can make a big impact—not just for companies, but for everyone. Picture more accurate credit scores, personalized investment advice, and smarter fraud protection. And with CFOs leading this shift, finance teams can use AI to make better choices and guide businesses with clarity and confidence.

    Looking to bring AI and machine learning to your business? We’re here to help. With over ten years of experience, we offer tools from smart chatbots to AI-powered recommendations. Take a look at our latest projects to see how we can help you reach your goals. Let’s talk about making your business our next success—get in touch today!

     

    Frequently Asked Questions

    What are the challenges associated with using Generative AI in finance?

    Generative AI in finance comes with several challenges. 

    1. First, there are concerns about data privacy and security. Financial data is highly sensitive, and using AI means handling large amounts of this data, which could be targeted by cyberattacks or data breaches. 
    2. Second, there’s the risk of biased or unfair outputs. If the data used to train the AI is biased, the system might produce results that could lead to unfair decisions, like biased loan approvals. 
    3. Lastly, there’s the issue of transparency. Generative AI models, especially those using deep learning, can be complex and hard to explain. This lack of transparency can make it difficult for financial firms to justify decisions to regulators or customers. 

    Despite these challenges, the numerous benefits of AI in finance—such as improved efficiency, predictive analysis, and customer service—are driving its adoption.

    How can financial institutions ensure the ethical use of Generative AI?

    The ethical use of Generative AI in banking and finance starts with building a strong foundation of data ethics and transparency. Financial companies need to make sure their AI models are trained on unbiased, diverse data to avoid discriminatory results. Regular audits and testing should be done to check for unintended biases and improve model fairness. 

    Transparent practices are also important; this means being able to explain how the AI reaches its decisions, which builds trust with customers and meets regulatory requirements. 

    Additionally, having human oversight over AI systems can help catch errors or unethical behavior before they become bigger problems. Providing training and resources to employees about the responsible use of AI also promotes an ethical culture within the organization.

    What are some examples of Generative AI in finance?

    Some examples of AI applications in finance include:

    1. Chatbots that provide personalized customer service and answer complex questions.
    2. Automated reporting tools that generate detailed financial reports quickly and accurately.
    3. Robo-advisors that offer tailored investment advice based on user preferences and market data. 
    4. Credit scoring systems that assess the creditworthiness of customers by analyzing various data points.
    5. Predictive analytics tools that forecast market trends to guide investment decisions.
    6. Some other applications may include fraud detection systems that spot unusual transactions and algorithmic trading programs that analyze market trends to create smart trading strategies.