Machine Learning in Fintech: Revolutionizing Financial Services

Staying ahead in the financial industry requires operational efficiency, accurate fraud detection, and high-quality customer experience—all driven by innovation. The role of machine learning in fintech in this context is hard to overestimate. 

McKinsey names artificial intelligence (AI) as one of the top seven technologies shaping the future of finance. Why is machine learning in finance so important? This technology allows financial companies to quickly analyze vast amounts of data and facilitates advanced decision-making across nearly all business areas, from overall strategy to individual loan approvals. With these capabilities, financial institutions can improve performance, meet rising customer expectations, and strengthen their market position.

In this article, we’ll explore the main applications of machine learning in fintech and explain the benefits you can expect. We will also share some tips on implementing it based on Neontri’s extensive expertise in fintech software development.

Key takeaways:

1. Banks using ML have cut costs by over 10% while improving fraud detection and risk assessment in real time.
2. Top banks like JPMorgan and Bank of America are already using ML to analyze customer behavior, automate lending decisions, and personalize services. 
3. The success of ML implementation depends on quality data, ongoing monitoring, and having the right expertise to avoid common pitfalls.

The current state of machine learning in fintech

The financial sector is at the forefront of AI adoption, demonstrating one of the highest integration rates across sectors. In 2023, investment in AI technologies by industry players reached $35 billion, with projections to grow to $97 billion by 2027. 

Machine learning is the most widely used of all technologies powering AI, accounting for $79.29 billion of the $184 billion AI market. This means businesses adopt it more frequently than other AI technologies, and financial institutions are no exception.

A survey on the adoption of machine learning in finance conducted by the  Bank of England revealed that 72% of financial companies are currently using or developing machine learning solutions. 79% of respondents have an ML implementation strategy.

Many leading financial institutions like Bank of America, Mastercard, PayPal, and JPMorgan Chase already leverage machine learning. New fintech companies are also competing for leadership in the financial ML arena. Moreover, these fintechs often pioneer successful ML implementations across use cases, setting a higher bar for the entire industry. Examples include Zest AI (underwriting), Robinhood (investment recommendations), and TrueAccord (debt collection).

What is machine learning?

Machine learning is a technology that enables software systems to learn from data. It operates through algorithms—rules determining how the system learns from input information. The outcome of this learning process is called a machine learning model. In a nutshell, the algorithm provides the framework and guidance for learning, and the model is the output that applies what it’s learned to new situations.

ML plays a key role in the development of artificial intelligence. While these terms are often used interchangeably, they are not the same. Let’s break it down:

• Artificial intelligence is an umbrella term that covers any use of technology to mimic human cognitive functions, such as perception, reasoning, decision-making, and problem-solving.
• Machine learning is a specific set of tools and techniques used to bring AI to life. It allows systems to make predictions or decisions by recognizing patterns in data, extracting knowledge, and improving performance without the need for explicit programming.

The ability of ML to understand data structures and learn from them is foundational to generative AI, a rapidly growing category within AI that focuses on the automatic creation of new content. For example, ML powers OpenAI’s GPT, which generates coherent, contextually relevant, and human-like text. 

Prominent use cases of machine learning in finance

ML helps financial companies gain a competitive edge by automating processes and enhancing customer service. Here are the key use cases driving these results.

Risk management 

Financial companies have to deal with market volatility, economic downturns, credit defaults, and operational disruptions. Therefore, effective risk assessment is essential for the banking sector’s long-term stability and profitability. Machine learning technology can take this process to the next level, increasing its accuracy, speed, and predictability. 

How does it work?

Machine learning models analyze internal data (e.g., transaction histories, credit scores, financial statements) and external data (e.g., news, social media sentiment, and macroeconomic indicators). These models then identify patterns that signal potential risks and flag unusual activity in real time. 

For example, machine learning in finance helps manage such risks as:

• The likelihood of borrowers defaulting on loans
• Stock price fluctuations and market downturns
• Economic shifts that may impact portfolios
• Internal inefficiencies or vulnerabilities within processes
• Liquidity shortfalls that could affect operations

Additionally, ML allows financial companies to conduct scenario analysis—a simulation of various conditions to see how they would impact risk exposure. This way, organizations can better prepare for changes and make sure they have a remedy strategy.

Fraud detection and prevention

Fraud is an ongoing challenge in the financial industry. According to Alloy’s 2023 report, 57% of surveyed decision-makers from the UK and the US reported direct fraud losses of $500K or more, while 25% faced losses exceeding $1 million in the past year. ML helps financial companies mitigate this risk as it automates the detection of suspicious activities and improves response times.

Machine learning models analyze vast amounts of transaction data to identify patterns of legitimate actions that may signal fraud. They can detect different types of fraudulent activities, including:

• Credit card frauds (unauthorized or unusual purchases)
• Account takeover (suspicious login attempts)
• Anti-money laundering (unusual fund transfers)
• Phishing (scam messages targeting financial accounts)

Unlike traditional, rule-based systems, machine learning solutions can learn from new data, adapting to evolving fraud tactics. This allows financial companies to stay ahead of emerging threats.

Credit scoring, loan approval, and underwriting

Traditional approaches to credit scoring, loan approval, and underwriting have many flaws. They are based on limited data, lack personalization, and require human involvement. With machine learning in finance, companies can solve these issues in multiple ways:

• Expanded data analysis. Machine learning models analyze a broader range of customer data, including utility payments, social media activity, and spending habits, alongside traditional data points like financial transactions. This helps score creditworthiness better, even for customers with limited credit histories (thin-file borrowers) or unconventional income.
• Personalized loan offers. Traditional lending decisions rely on standardized rules and metrics and offer uniform loan rates for most customers. Machine learning solutions, on the other hand, provide customized loan offers based on a data-driven assessment of each individual. Low-risk borrowers get better rates, while higher-risk individuals are offered terms that fit their profile.
• Accelerated underwriting. Traditional underwriting can take days or weeks as loan officers manually evaluate each application. Machine learning algorithms automate much of the data processing, delivering near-instant decisions.

Another benefit is that machine learning algorithms can adapt to changes in the economy and borrower behavior, making the lending process more responsive. For example, a Chinese fintech firm found that ML models outperform traditional credit scoring methods by 6 % to 25 %, especially during economic disruptions.

Personalized customer experience

Financial institutions have traditionally used broad marketing strategies, targeting large customer segments with generic messages. Machine learning transforms this approach by enabling personalization across key touchpoints, including:

• Tailored financial products (loans, premiums, credit cards, or investment advice). For instance, ML-enabled usage-based insurance (UBI) relies on data from telematics devices like GPS to assess driving patterns and mileage. Based on this analysis, insurers can offer customized premiums based on risk profiles—rewarding safe drivers with lower rates.
• Advanced customer service. ML-powered chatbots provide real-time assistance, adapting their responses based on customer queries and previous interactions. Additionally, machine learning solutions can identify customers’ preferred communication channels, allowing businesses to adapt to their habits.
• Proactive support for increased retention. Predictive machine learning models allow you to anticipate customer needs and approach them with relevant offerings, such as suggesting better loan refinancing options based on market conditions. This approach reduces churn and boosts loyalty by addressing customer needs before they arise.

Adopting machine learning in finance transforms how financial companies interact with customers, and the trend is felt on both sides. A recent study found that 74% of bank consumers across generations want more personalized services, while another 86% of financial institutions consider personalization a top priority.

Investment advice

Machine learning is redefining how trading decisions are made and executed, simplifying access to investments for many people. Two areas where it made the greatest impact are algorithmic trading and robo-advisors. 

Algorithmic trading is the automated, high-speed execution of trading decisions based on predefined rules. These rules can depend on various factors, including market data, timing, and price. 

Integrating machine learning into this process offers several benefits. For example, ML tools can analyze large volumes of data far more efficiently than traditional systems. This allows traders to respond to market changes in milliseconds and capitalize on fleeting inefficiencies. 

Machine learning algorithms continuously learn from past data and adjust to shifting market conditions. This capability allows for more advanced algorithmic trading strategies that accurately predict price movements and manage risk more effectively.

Robo-advisors offer financial advice, including retirement planning, estate management, and financial goal setting. They can also manage investment portfolios with minimal human intervention.

Machine learning is transforming robo-advisors with:

• More relevant advice as it allows for analyzing a much broader range of customer and market data.
• Continuous adjustments of portfolio recommendations based on new data. This helps traders adapt to shifts in market conditions, whereas traditional robo-advisors typically update portfolios less frequently.
• Asset performance predictions. This feature helps traders align their portfolios with the dynamic market environment.

The widespread adoption of machine learning in robo-advisors and algorithmic trading led to the rise of fintech companies such as Wealthfront, Yieldstreet, QuantConnect, and Kavout. Traditional financial institutions have also started investing in machine learning solutions to keep pace, resulting in offerings like JPMorgan’s You Invest, Morgan Stanley’s Discover, and BlackRock’s Aladdin.

Business benefits of using machine learning in fintech

Implementing machine learning in finance may require time and investment, but the resulting ROI is significant and often hard to achieve through other means. Let’s explore the specific advantages you can expect.

Automation and efficiency gains

Incorporating machine learning in finance allows companies to automate complex processes that would typically take much longer if done manually. This is especially beneficial in areas where fast and precise data processing is crucial, such as credit scoring, fraud detection, and risk assessment. 

What’s more, machine learning technology takes efficiency to the next level by enhancing already automated operations. For example, traditional automated document processing systems can be replaced with ML-powered alternatives for more accurate text recognition and smarter, context-aware data extraction.

Enhanced decision-making 

Using machine learning in fintech provides a financial company with deeper insights, trend identification, and decision-making at all levels.

At a strategic level, machine learning models facilitate market forecasting. By predicting economic trends and market movements, they empower executives to make informed decisions on investment strategies and portfolio management. Predictive analytics also helps anticipate major risks, such as credit defaults or market downturns, so financial institutions can prepare and mitigate them effectively.

Machine learning in finance also supports tactical decisions, like improving loan approvals. By evaluating creditworthiness through data analysis it ensures fairer outcomes for customers and more reliable results for a financial company.

Finally, machine learning solutions improve day-to-day decisions at the operational level. For instance, they can detect anomalies in real time, so financial inspectors can act fast and prevent fraudulent activities.

Minimized financial losses  

Over the past three years, financial decision-makers have identified the following risks as their biggest challenges: 

• interest rate changes
• macroeconomic shifts
• regulatory changes
• cyber threats
• third-party risks. 

While machine learning in finance won’t eliminate these risks, it can predict them and help a financial institution develop effective crisis strategies.

Machine learning algorithms can forecast market fluctuations by analyzing real-time and historical data. They can also simulate “what-if” scenarios so organizations can prepare for worst-case situations, such as economic downturns or geopolitical crises. This proactive approach helps companies minimize potential losses. 

Cost reduction and resource optimization

According to McKinsey, operating costs for retail banks have been rising steadily over the past four years. Similarly, a KPMG survey found that 56% of banks cited cost management as the sector’s top concern, with 18% ranking it as their main one. 

This highlights the urgent need for expense optimization, and implementing machine learning in finance is one of the most effective ways to do so. For instance, a recent NVIDIA survey revealed that 36% of financial service professionals reported their organizations reduced annual costs by over 10% using AI applications.

ML can automate many data-heavy tasks, such as document processing and handling routine customer queries.  It can also detect financial data discrepancies early on and prevent costly mistakes in transactions or reporting. ML algorithms can also forecast cash flow requirements, staffing needs, and inventory demands, ensuring efficient resource allocation.

Increased customer satisfaction

The competition in the financial industry is getting fiercer, fueled by changing customer expectations and the rise of fintech companies offering better services. Machine learning in finance is a powerful way to stay ahead of the curve.

ML-based solutions help companies create tailored offerings that align with individual lifestyles. But that’s not all. They can also predict customers’ future financial needs, such as saving for education, buying a home, or planning for retirement.

These ML capabilities can help financial companies offer highly relevant services to each client, boosting satisfaction. This, in turn, fosters stronger customer loyalty and reinforces the organization’s competitive advantage in the market.

Financial inclusion

One of the benefits of ML is that it helps financial institutions reach underserved populations and provide services to those outside the formal financial system. This includes low-income individuals, young adults without a credit history, and migrant workers.

ML can also help financial companies extend their services to remote areas, ensuring quality 24/7 customer support. With machine learning’s ability to predict market-specific risks, companies can expand safely, breaking down financial inclusion barriers.

Key steps for adopting machine learning in finance 

Machine learning implementation is a complex process that requires specialized expertise. The exact steps will depend on your current IT setup and the machine learning use cases you want to implement. Overall, ML involves three primary learning methods: 

• Supervised learning. A model is trained on labeled data, so each input is paired with the correct answer. For example, you upload images of cats and dogs and “tell” the model which is which.
• Unsupervised learning. A model is given data without labels. In this case, you upload images of cats and dogs and let the model figure out the categories on its own.
• Reinforcement learning. A model learns by trying out different actions and getting feedback. For instance, the model might guess whether an image is a cat or a dog. If it guesses right, it gets a reward; if it guesses wrong, it gets a penalty.

In finance, you’ll mostly use supervised learning to solve classification and regression tasks. Classification involves sorting data into categories. For instance, models classify loan applicants as “high risk” or “low risk” based on creditworthiness. Regression predicts continuous values. Examples include forecasting stock prices based on market trends or estimating the future value of an asset.

Regardless of the task, the typical implementation process will consist of the following steps:

1. Data collection. The process starts by gathering relevant datasets, such as historical financial transactions, market trends, and customer credit histories.
2. Data labeling. If the datasets lack labels, you’ll need to assign them either manually or using semi-automated methods. 
3. Data preprocessing. At this stage, you prepare the data for training by cleaning it—handling missing values, removing duplicates, etc. This process may also involve feature engineering, when you select, modify, or create new features (attributes) from raw data to improve the performance of your machine learning model. 
4. Model selection. You’ll need to choose an appropriate machine learning algorithm based on the task you want to solve (e.g., logistic regression, random forests, or neural networks).
5. Model training. Once you’ve chosen a model, train it using the labeled data you’ve collected. Training means feeding the model your data and allowing it to learn patterns.
6. Model tuning. Double-check that your ML model works as expected. Then, use the validation dataset to fine-tune its hyperparameters, which are the settings that control how the model learns, such as the learning rate or depth of decision trees.
7. Model testing. This is the final check before deployment. You need to see how well the model works on new, unseen data. Take a separate set of data (called a test set) and check how accurately the model performs. 
8. Model deployment. Use your machine learning model in a real-world environment. For example, it can be integrated into a business application or system so that it can start making predictions or decisions.

Machine learning in finance: Pitfalls to avoid

When implementing machine learning in finance, companies face common challenges. Here are the main ones:

• Poor data quality. Inaccurate, insufficient, or compromised data can lead to unreliable models. Invest in establishing a robust data management system that ensures data quality at every stage—collection, cleaning, and validation.
• Ignoring bias. Machine learning models may inherit biases from training data. Implement regular fairness checks, such as auditing data imbalances, addressing overrepresentation, and ensuring equal true positive rates across different groups.
• Neglecting model monitoring. It’s important to use real-time monitoring tools to identify problems early on. Failing to track model performance can result in unnoticed issues later in the production.
• Overfitting. When a model learns the noise and specific details in the training data, it can compromise its performance on new data. To overcome this issue, you can optimize the model by increasing the training data volume or using cross-validation.

While these challenges can affect the outcomes of machine learning solutions, an expert can help you navigate them effectively. If your company lacks the necessary expertise, collaborating with a reliable tech partner is a proven way to access the skills and knowledge required for success.

Conclusion

Machine learning offers vast opportunities to financial institutions. It helps banks spot fraud before it happens, figure out which customers might default on loans, and customize products based on what people want. These use cases translate into business benefits, allowing companies to save money, make smarter choices, and get more done without adding extra staff.

To fully leverage this technology, it’s essential to bring the right expertise on board. At Neontri, we specialize in fintech and banking software development, focusing on cutting-edge technologies. We have 10+ years of experience helping financial companies put ML solutions to work in the real world. Our team knows both the technical side and the banking industry inside out, which means they can jump in and start delivering value right away.

Contact us to discuss how we can help you integrate machine learning into your organization.