From investment decisions to operational efficiency, ML and AI solutions are quickly gaining favor in the fintech sector. With 85% of financial institutions making major investments in ML and AI, the industry’s landscape is being transformed in unprecedented ways.

 

It’s not a surprise that AI-based technologies are driving fintech’s digital revolution, as the finance intricate models and vast datasets are ideal for ML’s advanced automation, risk management, personalization, and regulatory compliance capabilities. 

 

Your business can be next to unlock the benefits of these innovations. Want to know how?


Inoxoft brings nearly a decade of experience to the table, with a rich portfolio of machine-learning finance projects and cutting-edge AI solutions. We merged our expertise in these fields to craft this in-depth guide to help you explore whether AI will become the game-changer for your business needs, covering benefits, use cases, and the latest trends. Ready to dive in? Read on!

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Contents

AI and Machine Learning in Fintech: Current Market State

Financial services firms are ramping up their adoption of ML in financial software development to leverage data from new digital channels. According to an Economist Intelligence Unit (EIU) research, 86% of financial executives plan to increase their AI-related investments through 2025.

Regarding the current data, Statista estimates that the financial services industry has invested US$35 billion in AI, and this figure is expected to increase over the next decade.

While ML technologies have penetrated nearly every area of fintech, Forbes highlights that investment banking firms lead in adopting AI and ML, closely followed by the retail sector. Given this trend, it’s worthwhile to explore ML adoption across different fields in greater detail.

Machine Learning in Fintech : What to Expect? - Inoxoft

AI and ML in banking and traditional finance 

As we’ve already mentioned, AI and machine learning in banking are more popular compared to other industries. For example, JP Morgan Chase spends $12 billion annually on technology, including AI and ML, making it an industry leader.

Although huge, these investments pay off significantly, saving a lot of money by solving ongoing challenges such as fraud, risk management, and poor customer service. The same JP Morgan Chase was one of the first banks to roll out an AI-powered virtual assistant, which handles millions of queries simultaneously, reducing wait times and boosting customer satisfaction, as well as increasing profits. 

AI and ML in wealth management and investments 

Wealth management has traditionally been pricey and exclusive, requiring investors to shell out high fees for advice. Most advisors often struggle to deliver timely or personalized recommendations, leaving many investors without adequate service.

Machine learning in investment banking steps in to solve these issues by offering automated, affordable recommendations through robo-advisors, which efficiently analyze market trends and individual goals. As of 2023, the robo-advising market was valued at US$7.39 billion and is projected to reach US$72 billion by 2032.

AI and ML in insurance 

We’re used to insurance reacting to trends rather than foreseeing them. However, AI is fundamentally changing this dynamic by using its ability to analyze vast amounts of data, learn, predict, and prevent issues before they happen.

AI and ML help solve several major challenges in insurance, among which are ineffective fraud detection, time-consuming claim processing, poor customer experience personalization, and weak decision-making. 

According to PrecedenceResearch, the global AI in the insurance market was valued at US$4.59 billion in 2022 and is expected to grow to about US$79.86 billion by 2032, with a strong annual growth rate of 33.06% from 2023 onwards.

AI and ML in payments

In recent years, the payments industry has undergone notable changes, especially with the rise of mobile payments and digital wallets. As the sector expands and develops, businesses are actively pursuing new technologies to stay competitive, with AI and ML getting especially popular.

EDC has recently surveyed payment industry professionals, gathering insights from over a hundred senior specialists worldwide. The survey revealed that 94% of respondents see AI and ML mostly used for improving fraud detection, followed by personalized customer service (67%) and the implementation of chatbots and virtual assistants (65%).

All these benefits cannot go unnoticed, driving the technology forward and creating greater demand for it in the payment sector.

Benefits of Machine Learning for Fintech

Fintech is an industry still being “under construction”. With the technological pace and daily innovations, it sets goals that require specific solutions. Hence, ML being the core of AI is the exact disruptive technology that can meet the goals of the financial industry. How? Here’re some benefits of the ML for the different sectors of finance: 

For banking and traditional services

  • Fraud detection. Fraud detection in banking using machine learning can be very effective as it quickly spots unusual patterns and anomalies that signal fraud. Furthermore, algorithms continuously learn from new data, adapting to changing fraud tactics to better protect against financial losses and increase security.
  • Streamlined loan processing. ML automates document verification by scanning and extracting important information from customer documents like IDs and income proofs. With such a level of automation, banks can easily reduce errors and expedite the approval process.
  • Personalizing customer experiences. AI-powered chatbots demonstrate some of the key benefits of machine learning in banking, as they offer personalized financial advice and suggest products based on individual transaction histories and preferences, making interactions more relevant and engaging for customers.
  • Risk management. ML algorithms in fintech analyze complex relationships to better understand and reduce risks. This helps financial institutions improve their strategies for allocating money wisely and minimizing potential losses.

For wealth management and investments

  • Better trading. ML algorithms analyze large amounts of financial data to find trends in the market. For example, ML can process historical trading data to forecast future trends, allowing traders to make informed decisions in less time.
  • Automated investment advice. ML-powered robo-advisors assess an investor’s financial situation, preferences, and long-term objectives. Based on this analysis, they can suggest diversified portfolios that align with the investor’s risk appetite and expected returns. 
  • Risk assessment and portfolio optimization. ML software evaluates investment risks and uncovers complex patterns and correlations that human analysts may miss, providing proactive risk management features. In portfolio optimization, ML identifies optimal asset allocations based on risk-return profiles and investment objectives, continually adjusting strategies.

For insurance

  • Automated underwriting. Traditionally, underwriting processes in insurance are time-consuming and prone to human error. ML software assesses various risk factors, such as individual health data, credit history, and behavioral patterns, to determine insurance premiums and coverage eligibility. 
  • Claims processing. ML algorithms can quickly analyze massive volumes of data from client claims and other sources, speeding up the process and allowing insurers to use client data for more precise outcome forecasts. 
  • Personalized insurance products. With ML, insurers can offer personalized policies that fit individual needs. For example, auto insurance premiums can be adjusted based on how someone drives, and home insurance can be customized depending on the security of the property and where it’s located.

For payments

  • Seamless payment authentication. ML simplifies payment authentication through the analysis of user behavior and transaction patterns, which strengthens security measures and expedites the verification process, effectively reducing the likelihood of fraud.
  • Faster payment processing. ML software helps with payment process effectiveness by automatically verifying and confirming transactions, making payments quicker and more efficient overall.
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    Machine Learning Use Cases in Fintech

    As machine learning penetrates fintech with great speed, it proposes newer and newer features to help the financial industry get ahead of time. Machine learning banking use cases in finance are:

    Algorithmic Trading and Wealth Maintenance

    Strategic or algorithmic trading and wealth management are some of the most important elements of financial literacy and machine-learning projects in finance. Algorithmic trading executes stock trades by adhering to a pre-programmed set of instructions that evaluate data and make trading decisions.

    The fintech AI and ML system can recognize trading patterns and react promptly to shifting market deals thanks to its ability to continuously analyze and learn from millions of data points. Therefore, AI algorithms can place trades at the best pricing, ensuring bigger returns.

    One of the best examples of financial intelligence is an app that helps predict successful investment deals. Here, Citi Private Bank (USA) is promoting machine learning in banking and finance and shares its investors’ profiles among its customers. Anonymously, of course.

    Here are some more examples of how to use ML algorithms to improve trading and wealth management: 

    • Establish real-time data processing capabilities to instantly analyze and respond to market movements.
    • Implement stop-loss mechanisms and diversification strategies to manage and spread risk. 
    • Provide users with tools like electronic wallets to track their expenses and identify overspending.

    Forecasting Future Trends

    Any business can thrive by gathering huge amounts of data to make better financial trend predictions and promote customer satisfaction. 

    According to Harvard University, by analyzing historical data over a certain period, ML learns about the customer’s budget based on particular economic indicators. It also creates better solutions for business performance improvement. 

    Here are some possible ways to improve trend forecasting with ML algorithms:

    • Use AI models like ARIMA, Prophet, and LSTM to analyze historical data and predict future market trends and economic conditions.
    • Implement natural language processing (NLP) to analyze news articles, social media, and financial reports to gauge market sentiment and predict trends.
    • Combine various data sources such as historical prices, trading volumes, and macroeconomic indicators to forecast stock prices, currency exchange rates, and other financial metrics.

    Automated Customer Services

    The growing need to customize fintech and swiftly respond to changing customer demands drives the adoption of chatbots and AI interfaces. These solutions deliver tailored online advice, which helps cut costs, especially those related to staff augmentation. It’s reported that AI chatbots improve digital experiences for 84% of users and provide a more personalized service for 46%.

    One of the best examples of such a use case is Swedbank, which developed “Nina,” an NLP-powered software designed to handle the high volume of 2 million customer calls annually, thereby relieving pressure on their contact centers.

    Here are more ways to use ML and AI solutions to improve automated customer services:

    • Use conversational AI platforms to develop chatbots that can handle common customer queries, process transactions, and provide account information.
    • Employ AI for automated verification of customer identities and KYC compliance.
    • Integrate AI-driven voice recognition systems into your services to allow customers to perform tasks and get information through voice commands.

    Machine Learning in Fintech : What to Expect? - Inoxoft

    Prevention of Cyber Frauds

    Whenever there is talk about money and personal information, there is always a place for fraud. To decrease its level and eliminate potential risks, the fintech industry invests in AI and ML immensely to stop transactions using fraudulent methods. The benefit here lies in ML’s ability to react faster and analyze large scopes of data.

    With the help of AI technologies, financial services become safer every day. Here’s how you can use them to their maximum in your business:

    • Implement ML tools to create profiles of typical customer behavior by analyzing historical data. These tools can detect deviations from normal behavior, such as sudden large purchases or changes in login locations.
    • Use ML algorithms to assess the risk level of transactions based on factors such as transaction amount, frequency, location, and historical fraud to prioritize which transactions to review manually and which to approve automatically.
    • Utilize AI and ML technologies to analyze biometric data, such as facial recognition or fingerprint scanning, and cross-check it with existing records.

    Customer’s Risk Profile

    Profiling customers and understanding their needs is one of the fintech machine learning projects that is in great demand now. However, detecting risky profiles isn’t just about customer needs. It’s rather about banking security and wise, predicted decision-making. 

    The patterns of the services customers choose or the financial activity they do online can be traced in real-time with the help of AI and ML, and that’s how:  

    • Use ML to trace the patterns of social media usage, web browser history, geo-location pins on maps, and other metrics to automate customer risk evaluation techniques and detect creditworthy customers.
    • Develop early warning systems that monitor and analyze customer behavior and financial health indicators to predict potential defaults or financial distress.
    • Use AI and ML to assess the risk profiles of suppliers and partners to manage risks associated with their supply chain.

    Clear-Cut Decision-Making

    Management decisions can be crucial both for the employees and the customers. That’s why the right decision-making in the financial sector is very important. ML has the potential to effectively analyze numerous data sets and propose the best-predicted outcomes to cut costs and save money. 

    • Use AI and ML to analyze historical sales data, market trends, and other relevant factors to predict future sales and make informed decisions about inventory, staffing, and marketing strategies.
    • Implement ML algorithms to process vast amounts of data from various sources to provide you with insights into market trends, competitor strategies, and customer needs.
    • Leverage AI and ML to optimize business processes, such as supply chain management, inventory control, and resource allocation.

    I predict that, because of artificial intelligence and its ability to automate certain tasks that in the past were impossible to automate, not only will we have a much wealthier civilization, but the quality of work will go up very significantly and a higher fraction of people will have callings and careers relative to today. — Jeff Bezos on AI for CNBC

    Examples of Machine Learning Applications in Finance That You Can Develop

    We’ve already learned that ML techniques are steadily evolving in the financial industry. Today, companies use them to provide better pricing, reduce risks from human error, automate repetitive tasks, and understand customer behavior. But what solution can you develop? Here are five common applications of machine learning in the fintech industry.

    Personalized finance chatbots

    The finance industry usually misses out on potential leads due to inefficient human agents, which can’t be available all the time. To tackle this, finance companies develop convenient conversational AI assistants, or financial chatbots, which automate personal finance management and lead generation while also improving the user experience.

    What can chatbots do? They provide round-the-clock support every day and can answer common questions, check account balances, offer financial advice, as well as manage multiple requests from various channels at once.

    Chatbots offer even more benefits, including saving time, boosting lead generation, and increasing customer satisfaction. In the long run, these advantages lead to financial gains, such as higher customer retention and satisfaction rates, reaching up to 87%.

    Smart budgeting assistants

    Many businesses use tools like Microsoft Excel and Google Sheets for budget management, but tracking budgets manually isn’t the most reliable or accurate method. Finance professionals who rely on spreadsheets face a much higher risk of human error. Forbes has estimated that 88% of spreadsheets contain mistakes.

    Smart budgeting assistants are designed to understand the complexities of budgeting, analyze spending patterns, categorize expenses, and help set realistic savings and expenditure goals. Moreover, it encourages fiscal responsibility and empowers users to control their finances through an engaging, intuitive, and highly responsive interface.

    Using such a tool promotes better financial health by automating budgeting and identifying cross-selling opportunities. It also provides data-driven insights for informed decision-making and gives the institution a competitive edge through innovation.

    Fraud fighters

    Fraud is still a widespread problem in various industries around the world, especially in finance. UK Finance reported that criminals stole over £1.2 billion through both authorized and unauthorized fraud in 2022, which amounts to more than £2,300 every minute.

    That’s why many companies are starting to invest in creating fraud detection solutions for fintech using AI and ML to monitor incoming data and prevent fraud before it happens. The AI software learns from historical data and can adapt its rules to stop threats it hasn’t encountered before, unlike standard fraud software.

    The more data algorithms process, the better their predictions become. More to that, it’s that AI continuously improves, particularly when the systems share knowledge with each other. For instance, when a DataDome AI instance identifies a new threat pattern, it shares this information with all other DataDome AI instances worldwide.

    Investment recommendation engines

    There are many compelling machine learning examples in banking, but investment recommendation engines are one of the most prominent ones. They provide businesses with many advantages, such as improved portfolio management, better decision-making, and many others. With comprehensive tracking, performance analysis, and risk assessment features, businesses and individual investors can see an impressive ROI on AI investing software.

    In addition to providing investment tips and advice, they also automate routine tasks, increasing operational efficiency and allowing financial advisors to concentrate on personalized client interactions and high-level strategy.

    The versatility of these engines, which often include broader financial management features like budgeting, saving, debt management, and spending analysis, fosters a holistic approach to financial health and promotes overall financial stability and growth.

    Loan approval assistants

    AI-powered loan approval assistants are a prime example of how machine learning can transform the finance industry. These systems gather and analyze large amounts of data from various sources, including bank statements, credit scores, employment history, and even social media profiles, to evaluate each applicant’s creditworthiness.

    They assess risk comprehensively, uncovering patterns and trends that human underwriters might miss. This leads to a fairer and faster approval process, reducing biases and improving approval rates for qualified applicants.

    Additionally, these assistants provide real-time recommendations and insights to loan officers, improving the customer experience with quick responses and customer segmentation in banking machine learning.

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    3 Examples of Real-Life Machine Learning Projects for Finance

    It’s safe to say that machine learning and fintech make a great combination. Let’s now look at practical examples of AI technologies in fintech. Here are some ML solutions companies already use to make smarter financial decisions:

    Zest.ai

    Zest.ai, which began in 2009 as ZestFinance, is a leader in using machine learning for credit underwriting. Douglas Merrill, a former Google CIO, founded the company to create more accurate and equitable credit scoring models.

    With the significant transparency that comes from ML analyzing various data points, lenders can better evaluate people who were previously deemed too risky. Moreover, the platform was designed for rapid implementation and scaling. According to Zest.ai, auto lenders using ZAML have already reduced their annual losses by 25%.

    Enova International

    Enova International, founded in 2004, is a global financial technology company that uses machine learning and advanced analytics to provide innovative financial solutions, including online lending and financial services for consumers and businesses. 

    The company’s proprietary analytics platform, Enova Decisions, automates decision-making processes to optimize loan approvals, fraud detection, and customer relationship management. 

    Enova’s systems can adapt to changing market conditions and customer behaviors, leading to more accurate and efficient financial operations. Over the years, Enova has served millions of customers and provided billions of dollars in loans, demonstrating the huge role of AI and ML in fintech.

    Erica

    As we’ve already mentioned, machine learning use cases in banking are more popular compared to other industries, and Erica is no exception. This platform, launched in 2018, is Bank of America’s AI-powered virtual financial assistant designed to help customers manage their finances. Through machine learning, Erica provides personalized insights, budgeting tips, and alerts based on individual spending habits and financial goals.

    Users can interact with Erica via voice, text, or in-app messaging to perform tasks such as checking account balances, tracking spending, and making payments. Since its launch, Erica has gained millions of users and processed over 100 million requests, highlighting its enormous success.

    Inoxoft’s Experience in Implementing AI and ML into Financial Projects

    At Inoxoft, our developers have worked on projects for both large corporations and small businesses, delivering outstanding returns on investment through smart and creative AI and ML solutions. That’s why we say that our value comes from expertise!

    Our team of over 200 professional engineers, designers, project managers, and business analysts provides a full range of development services—from your AI solution idea initiation to post-launch support—with a deep understanding of domestic and international fintech regulations. 

    We’ve refined our approach through multiple use cases across various sectors, consistently addressing key challenges at every level to bring you the highest quality of both services and products. For a glimpse into our work, check out our case study.

    Case Study: Trading Automatization Platform

    Our client, a group of traders who operate at the world’s three major stock exchanges – London, New York, and Tokyo – engages investors based on requested proposals. Despite their considerable experience and unique financial strategies, the traders relied on manual processes for currency data analysis, price monitoring, and deal arrangement, which proved to be time-inefficient.

    During the development, our team focused on three major goals:

    • To replace manual work with automatic optimization. 
    • To implement the traders’ financial strategies within the code, enabling hourly updates as soon as major stock exchanges provided rate information. 
    • To build logic for forming orders and arranging deals with investors or brokers, as faster deal management increases the chances of securing profitable bargains.

    With a solid background in trading, stocks, currency diagrams, and financial operations, the Inoxoft team created a powerful application that eliminates manual work and uses advanced algorithms to make trading predictions according to a defined financial strategy. 

    Our cooperation brought our client impressive profits, a high return on investment, and additional competitive advantages in such a narrow niche.

    Final Thoughts

    Investing in machine learning is undoubtedly promising. Financial companies that adopt these innovations now will remain competitive in the coming decades, continually benefiting from advancing technology.

    AI in finance signifies a fundamental shift towards more efficient, secure, and personalized financial services, meeting the needs of both financial organizations and their clients. As technology progresses, AI applications will address an increasing range of business needs, providing even greater insights and efficiencies across the sector.

    If you want to explore the impact of AI for yourself, consider Inoxoft as your trusted partner. With 10 years of experience, a diverse and talented team, extensive work on similar projects, and ongoing skill development in the fintech field, we’re ready to bring your vision to life. Contact us today for a free consultation with our business analysts.

    Frequently Asked Questions

    What are some challenges of using ML in Fintech?

    As with any technology, fintech faces some challenges in implementing AI and ML solutions:

    1. Protecting sensitive financial data remains a very delicate task, making data security a top priority.

    2. The complexity of financial markets requires advanced models, but developing and interpreting these models can be tough.

    3. Evolving laws and standards make regulatory compliance a constant challenge, demanding continuous adaptation and vigilance.

    4. Managing the risk of algorithmic bias is also a difficult task but is important to ensure fair and ethical outcomes, as biased algorithms can lead to unfair advantages or discrimination.

    Despite these hurdles, tackling them is key to building a trustworthy and efficient fintech ecosystem.

    How will Machine Learning evolve in the future of Fintech?

    Machine learning in fintech is set to become even more advanced, with models that can handle larger and more complex datasets. AI will replace some job positions by automating routine tasks like customer service and fraud detection, making processes smoother and more efficient.

    Personalized financial services will become the norm, as AI customizes products to fit individual needs. We'll see more collaboration between fintech firms and traditional banks, leading to more integrated financial ecosystems. Additionally, advancements in explainable AI will boost transparency and trust in AI-driven decisions, making everyone feel more confident about the technology.

    What specific machine learning algorithms are commonly used in Fintech projects?

    In fintech, specialists use a variety of powerful algorithms to drive innovation:

    1. Decision trees help with credit scoring

    2. Neural networks are key for detecting fraud

    3. Clustering algorithms are great for understanding customer segments

    4. Support vector machines (SVMs) and logistic regression provide data for risk assessment and predictive analytics

    5. Natural language processing (NLP) algorithms dive into text data, like customer reviews and financial news, to uncover trends and insights.

    6. Gradient boosting machines (GBMs) boost model accuracy, making financial applications smarter and more effective.

    What are the challenges of integrating Machine Learning models into existing financial systems?

    Integrating machine learning fintech models into financial systems can create several challenges: 

    1. Compatibility issues can arise with legacy systems, which require significant effort to integrate seamlessly. Data silos within organizations can also hinder the flow of necessary information.

    2. Data security requirements and compliance with regulatory standards add complexity.

    3. There is a need for skilled personnel to manage and maintain these models, as they must also be regularly updated and validated to maintain accuracy and reliability in a dynamic financial environment.

    How does Machine Learning impact regulatory compliance in Fintech?

    Machine learning positively impacts regulatory compliance through its automation benefits, as algorithms can detect suspicious activities like money laundering and fraud.

    For example, AI can analyze transaction patterns to flag unusual behavior that might indicate fraudulent activity. This automation helps companies report accurately and on time, meeting regulatory requirements.

    However, machine learning also presents challenges. Regulators require clear explanations of how AI makes decisions. For instance, if an AI model flags a transaction as suspicious, the company needs to explain how the model arrived at that conclusion.