Machine Learning in Fintech: How Smart Algorithms Change the Way We Operate Financial Services
If you’ve ever checked your bank account on your mobile device, received a warning about fraud on your account, or noticed the predictions that your favorite trading application makes about stock prices, you’ve already experienced Machine Learning in Fintech. Today, it is no longer a distant vision; rather, it is currently the unseen driving force behind the vast majority of financial services.
In this article, we will delve into how ML and Artificial Intelligence (AI) will shape the future of Fintech. And, more importantly, how it’s transforming the new way people think about their finances today.
ML for Fraud Detection
Fraud detection used to be based on fixed rules, with alerts triggered when certain thresholds were reached, such as attempted withdrawals above a set limit or excessive transactions in a single day. Fraudsters have evolved much quicker than any fixed rule sets could ever keep up with them.
Machine learning has changed the game. Instead of relying on fixed model rules, machine learning learns from millions of transactions, both legitimate and fraudulent, to identify anomalous transaction behaviour instantly.
For example, a customer who routinely shops in London suddenly has an unusually large purchase from Tokyo. With a fixed set of rules, the transaction would be blocked immediately. But with machine learning, the system would also check whether the customer has recently booked a flight to Japan, examine their purchase history, and assess the likelihood that the purchase was made legitimately. This means fewer false-positive alerts and a much quicker reaction time to real threats.
This is why companies like PayPal and Revolut can effectively flag fraudulent transactions within milliseconds. They train machine learning algorithms using mountains of transaction data so that the system “understands” what “normal” looks like for each customer.
Enhancing Customer Experience through Hyper-Personalization
Every person who uses a product or service has unique characteristics and circumstances. A college-aged individual saving money for tuition does not require the same financial advice as a senior citizen with significant wealth and investments. Machine learning enables the development of financial technology applications that provide highly specific, individualized financial services.
For example, when Netflix offers you the opportunity to view a movie or television show based on your preferences, it uses machine learning to generate a personalized recommendation. The financial domain also uses machine learning to provide you with customized insights into your finances, so you can make a FinTech app with personalization features.
Monzo is a UK financial technology company that uses machine learning to help its customers manage their finances more efficiently. Using machine learning, Monzo has developed an application that automatically categorizes all your purchases into categories such as Food, Transport, Entertainment, etc. The application also provides you with a written forecast of your future monthly expenditures and alerts you to any potential negative balances before payday. Monzo offers users the experience of a professional financial planner without the cost of hiring one.
Credit Rating and Risk Assessment by Machine Learning
Machine Learning can be used as part of the technology-driven changes transforming how consumers access credit. Historically, banks have relied on a borrower’s credit report as well as the FICO Score to assess if a borrower can repay a loan. This means banks exclude many individuals with limited credit histories, such as new graduates, freelancers, and individuals living in emerging economies.
Using machine learning enables lenders to view alternative data alongside historical credit information. Lenders can now take a holistic view of alternative data, including information on income and how it reflects a person’s ability to manage their finances. By analysing this data and comparing it with a borrower’s past behaviours or financial situations, lenders can now offer credit opportunities to significantly more individuals while better supporting loans with a high likelihood of successful repayment.
The most well-known example of this technology is Upstart and Zest Finance, both of which have developed machine learning algorithms for credit assessments. According to Upstart, its models allow lenders to approve 27% more borrowers at the same default rate as traditional methods. The transformative power of technology to increase efficiency and fairness in lending can fundamentally change entire industries.
Investment Predictions with Analysis
Traditionally, the predictive aspect of investing (whether predicting price changes, conducting trend analyses, or evaluating customer sentiment) has been at the core of investing. However, the advent of machine learning enables investors to develop much more sophisticated predictive models.
Investors and investment firms alike now implement machine learning models and strategies to analyze large amounts of data. These include stock price histories, social media comments, news articles, and even satellite imagery of store parking lots. By uncovering hidden patterns in this data, machine learning systems can identify relevant signals that traditional investment analysts would miss, enabling them to predict market trends that human analysts would not see.
An example of this is the use of machine learning by hedge funds such as Renaissance Technologies, which employ machine-learning programs to systematically search large markets for profitable investment opportunities and execute trades in less than a second. Similarly, retail trading apps such as Robinhood and eToro use predictive analytics to suggest potential investments to their users based on the users’ interests and risk levels.
It is important to note that while not all predictions made through machine learning will be accurate (the stock market is highly volatile and unpredictable), machine learning provides significant advantages to traders because it allows them to analyze very large volumes of data and, as a result, find patterns or correlations that would not be visible otherwise.
Automated Customer Assistance
Recent advancements in artificial intelligence and machine learning have greatly improved customer service capabilities. We have moved away from static exclamation points and developed tools that can interactively communicate in a natural, conversational style. In addition, artificial intelligence-driven chatbots can recognize speech and generate meaningful responses based on their interpretation of the customer’s request.
Chatbots have been implemented in organizations for a variety of business applications, including the banking sector. For example, Bank of America uses an artificial intelligence assistant, Erica, that enables customers to make better financial decisions by reviewing past and current purchases, displaying payment schedule options, and providing alerts when spending habits and patterns differ from established norms. Like chatbots, Erica continues to learn from each conversation with customers through a feedback mechanism, becoming more useful after each interaction.
The automated customer support provided by these artificial intelligence applications has improved customer service agent performance by enabling them to focus on complex tasks and issues. Automated customer support improves agent productivity and enables personalized interactions with every customer who contacts a company for support.
Anti-money Laundering (AML) and Regulatory Compliance
FinTechs are subject to extensive regulatory oversight, creating challenges for companies to ensure compliance and identify suspicious transactions. This challenge is compounded by changing regulatory requirements.
Using Machine Learning (ML) to automate compliance monitoring and identify potential money laundering transactions. By using ML, companies can monitor very high volumes of transaction data and categorize transactions that exhibit outlier patterns relative to expectations. The use of ML offers a clear advantage over traditional rule-based systems, as ML algorithms continuously adapt to changes in criminal activities used to obtain illegal funds.
A potential use case for an ML solution is identifying small, coordinated transactions that do not exceed reporting thresholds. This is a common methodology in the money laundering space. By learning from historical data on other criminals, the ML algorithm may be able to identify similar activity within the laundering structure, regardless of how sophisticated it becomes.
Cybersecurity Enhancement
Fintech relies heavily on protecting sensitive information in cyberspace, which is why machine learning has become a vital layer of protection for these businesses. Machine learning enhances the effectiveness of cybersecurity systems by continuously monitoring and learning from emerging cyber threats to identify vulnerabilities and anomalous behavior, and to protect against them.
If a cybercriminal attempts to access or hack into a company’s internal network, machine learning can recognize the cybercriminal’s activities and isolate the threat by detecting abnormal network activity or behavior. This proactive approach to network protection has taken on even greater importance in the fintech world, given that a single minute lapse in network security can cost millions of dollars.
Numerous startups, as well as banks, are now utilising behaviour-based authentication, such as verification checks that allow the system to identify whether you are really the one using your online account to access your funds. This means that machine learning is actively protecting your finances while you go about your daily life.
Transformative Trends in Technology Are the Future of Financial Services
Machine learning is not an easy fix. The quality of the training data will determine how accurate the ML results will be, and bias in the training data can create bias in the outcome (e.g., loan applications from specific groups being rejected). Another challenge is a lack of transparency.
Many ML algorithms work as “black boxes,” so even the humans who developed the algorithms cannot understand why an algorithm made a specific decision.
To address these challenges, FinTechs are investing in Explainable Artificial Intelligence (XAI), Ethical Data Practices (EDP), and Continuous Model Evaluation (CME). The goal of these programs is to achieve a good balance between automation and responsibility.
Despite today’s difficulties, we are optimistic about tomorrow, given the growth of computing power and the increasing variety of data sources. Machine learning will continue to develop methods to customize, secure, and streamline delivery for Financial Services. For example, instead of merely showing you how much you spent in a prior month, financial management applications of the future will likely predict when you will achieve your savings goals and alert you to potential future cash flow problems.
