Ever wonder how financial services have grown to represent around a fourth of the world's economy? The world's most valuable bank, JPMorgan, and the best-performing investment fund, Renaissance Technologies, have something in common: both use AI in their core business processes.
Machine learning is highly used in finance to simplify tasks and save time. In fact, it’s reported that 70% of all financial services firms are using machine learning.
Machine learning has a lot of applications in finance and can greatly optimize processes in finance departments and businesses. In this article, we’re going to explain how it works with examples and understand its use cases.
Machine learning is a field of computer science that allows computers to learn without being explicitly programmed. It is used in many different areas, including healthcare, retail, and finance. It’s a subset of the broader field of artificial intelligence, and is used widely used in the finance industry, but also in other areas like social media and sentiment analysis.
There are many applications of machine learning in finance, focused on the automation of tasks so that humans can focus on more complex activities. One example is shortening credit timeframes with credit risk prediction models.
Credit scoring prediction models are used to assess potential risks associated with lending decisions based on historical data. By using these models, banks can determine when it would be most profitable to procure loans or when there may be too much risk involved for them.
Another use of machine learning for finance is to recommend the right financial products at the right time, either from financial services companies or robo-advisors. The models can also help banks decide which customers to approach for new services and how best to price their offerings. With these types of predictions, banks can better manage their service portfolio while reducing costs over time (such as by automating repetitive processes).
These models also help with trading decisions and asset management, as AI helps fund managers analyze big data sources, such as stock prices. Hedge funds use machine learning models to build stock market forecasts.
More broadly speaking, artificial intelligence can help banks gain a better understanding of their customer’s financial behavior, which can help with things like risk assessment and ultimately lending decisions.
In finance, the terms “machine learning” and “algorithm” often get confused or used interchangeably, but these two tasks are different. In machine learning, a computer program is being taught how to learn on its own. In algorithm development, a set of rules have been defined that tell the computer how to perform a task.
Machine learning reduces the effort and time spent in customer communication by the financial advisor, compliance team, or data scientists. It also reduces the time spent on gathering information from customers which can be used to make decisions.
Machine learning simplifies processes that would traditionally rely on human interpretation, such as fraud detection.
By using AI, financial firms can benefit from positive ROI, higher accuracy, and competitive advantage. This leads to a reduction in costs and increased productivity by eliminating tedious tasks.
Let's explore 15 examples of how machine learning is used in finance:
Let’s start with looking at chatbots.
Chatbots are computer programs that simulate conversation with a human and answer questions. In finance, chatbots can help automate tasks such as answering compliance team inquiries, providing customer service advice, or assisting with financial decisions.
You can find some examples of these chatbots at companies like American Express, which uses AI assistants in tools like Facebook Messenger and Amazon Alexa, letting customers perform tasks such as balance checking or making payments.
Ally Bank has been using the Ally Assist bot for over half a decade to provide a seamless customer service experience for users to manage their account, available through an iOS app or through Amazon Alexa commands.
Furthermore, chatbots contribute greatly in lead generation and capture. HubSpot Chatflows are a prime example of this. Integrating it with a reliable email validation tool ensures that only verified and accurate information is captured.
Financial monitoring is the process of tracking your financial health over time using tools such as budgeting apps or investor dashboards. In finance, this is commonly known as personal capital management. For example, Cleo is an intelligent, chat-heavy savings app.
Financial advisors also use financial monitoring tools to help their clients track their spending and monitor progress towards achieving their financial goals. These tools can also alert users if they deviate from their budget and provide recommendations for how to adjust accordingly.
Fraud prevention is another area where AI can play a role.
Security teams use machine learning algorithms to analyze millions of data points and detect fraud as it’s happening, as well as prevent it before funds are released from a client’s account. This is possible with large neural networks called deep learning, in this case fueled by massive amounts of financial data.
A fraud prevention system can look at patterns in incoming transactions and compare them to previous data to determine if something looks odd or suspicious, such as a large number of small transactions.
It also helps tackle false positives, or false declines, which can happen when an algorithm flags a transaction as suspicious, but there is no actual fraud. With validation and backtesting, a fraud prevention system can become more accurate over time, flagging real fraudulent transactions before they occur.
Many firms use automation to reduce costs associated with manual processes. For example, a bank may have a team responsible for generating new account applications using an application program interface.
The work of the API team could be fully automated by using machine learning to power fraud detection capabilities within the API. This would allow them to focus on other tasks within the bank, such as providing advice and financial education to customers. In this case, the API team would still need to verify that each customer is eligible for an account, but that task could be performed by a different group of employees entirely—or even completely automated.
Paperwork reduction has been a key goal for many financial firms. As reported by Reuters, banks spend billions of dollars annually on paperwork and compliance activities like verifying account ownership or monitoring client activity.
This work can be partially or even fully automated using machine learning, freeing up workers to focus on the more complex aspects of clients’ accounts, such as helping them make long-term financial decisions or addressing their unique needs.
Automation also allows financial advisors to do more with less (and perhaps free themselves from doing certain tasks altogether). That means advisors can spend time advising their clients instead of performing repetitive data entry—or even spending hours each week preparing compliance documents that are then passed along to other teams.
Risk analysis is a critical part of any investment strategy. It involves quantifying, aggregating, and understanding risks so that you can better manage them. In finance, this includes identifying potential risks in a transaction using a combination of quantitative and qualitative analysis—such as calculating the expected loss based on historical data or assessing risk based on factors such as industry concentration or macroeconomic conditions.
In addition to providing insight into transaction risks, machine learning algorithms can be used for risk management, by quantifying those risks and giving firms the ability to create policies around them. This helps firms design robust trading strategies, limit their potential losses based on historical patterns, and proactively protect themselves from possible dangers.
Data management is the process of gathering, storing, and organizing data so that it can be analyzed. In finance, this often involves monitoring fluctuations in financial markets. For example, a market monitor could look at all trades being conducted by a firm to identify trends or patterns that could indicate potential areas for concern
Using machine learning, the market monitor would then be able to spot these patterns in real time rather than waiting for an analyst to discover them manually. This would free up analysts to focus on more pressing issues—and perhaps alert the business when it’s necessary to take action.
Decision-making is another area where AI can help reduce costs and increase efficiency. Traditionally, financial firms may have teams responsible for making investment recommendations, such as advising clients on which stocks to buy or sell. These teams might have to manually perform due diligence on new investments or keep track of all trades made by individual traders—both of which add time and costs to the decision-making process.
By using machine learning for this task, firms can give their analysts more bandwidth and focus on analysis instead of data collection and analysis—increasing efficiency and reducing costs associated with making decisions.
One area where machine learning can play a key role in finance is churn prediction. This refers to figuring out which customers are likely to leave your firm and when they will choose to do so. Churn tracking allows firms to identify areas for improvement, such as providing their advisors with better training or improving the customer experience.
Churn prediction helps to better understand customers and potentially prevent churn before it happens by providing clients with useful information and advice. Given the right tools, advisors can even identify which customers are most likely to leave and decide whether or not to invest time and resources into retaining those clients.
The trading strategy used by a firm also has a big impact on costs and efficiency. A trading strategy could be based on an algorithm that automatically buys and sells based on market conditions. This can help firms avoid placing trades that aren’t profitable or aren’t needed—saving both time and money in the long run.
Algorithmic trading strategies have become increasingly popular among financial institutions, many of whom find them to be cost-efficient ways of managing risk while generating returns.
One area where machine learning can have a huge impact on the financial services industry is in the area of financial advisory. If you’ve ever spoken to an automated phone system, you’ve interacted with machine learning. This technology uses algorithms to convert speech into text and vice versa—a process using natural language processing (NLP).
Another area where machine learning can be used in finance is loan approval. In the past, this process has been quite manual, requiring a human to review each loan application and make a decision. This can be time-consuming and costly.
With machine learning, however, firms can develop algorithms that automatically review loan applications and make recommendations about whether to approve or deny them. This not only saves time but also helps to ensure that loans are given to those who are most likely to repay them—reducing risk for the lender.
In today’s job market, it’s becoming more and more common for companies to use machine learning to recommend jobs to candidates. This is done by taking into account a variety of factors, such as a candidate’s skills, experience, and location.
Machine learning can also be used to assess a candidate’s qualifications for a role—saving both the company and the candidate time in the hiring process.
Another area where machine learning is being used in finance is bankruptcy prediction. This involves using historical data to build models that can identify which customers are most likely to file for bankruptcy. This information can then be used by banks or other financial institutions to make decisions about lending or credit lines.
A robo-advisor is an automated investment management service that provides recommendations about investing based on your goals and risk tolerance. These services have become increasingly popular in recent years as they offer a low-cost alternative to traditional financial advisors.
There are many different robo-advisors available on the market today, such as Betterment and Wealthfront. Some of these services even allow you to open an account with as little as a few dollars.
Lastly, machine learning is also being used to predict tax evasion. This is done by analyzing data about a taxpayer’s income, expenditures, and assets. Based on this information, algorithms can be developed that can identify which taxpayers are most likely to evade taxes.
This information can then be used by tax authorities to focus their resources on auditing those taxpayers who are most likely to evade taxes. This not only saves time and money but also helps to ensure that taxes are collected from those who are most able to pay them.
Traditionally, implementing ML would require a team of data science professionals, with technical skills like Python and TensorFlow, as well as knowledge in machine learning techniques such as reinforcement learning. Even with the right talent, these AI projects would be costly and time-consuming, leaving many FinTech and portfolio management players at a competitive disadvantage.
Akkio is a no-code platform that helps companies identify and validate data-driven business opportunities. We give you powerful tools in the background, such as Neural Architecture Search (NAS) and scalable data pipelines, with a simple visual interface in the front-end
To get started, simply connect a relevant dataset that you have to our platform, and we'll do the rest. You can see an example of how this might look in our case study on churn prediction or on fraud detection. Whether you want to build regression models on time series data or build process automation optimization models for underwriting, our platform will get you there in record time.
There are two major challenges one might face when using ML in finance:
In ML, there is a balance between overfitting and underfitting. Overfitting occurs when a model is too complex and does not generalize well to new data, while underfitting happens when a model is too simple and may not be able to capture all of the relevant information.
Low accuracy can be potentially overcome by increasing training time, adding additional high-quality labeled data, and using strong algorithms. Akkio uses Neural Architecture Search to select the best ML algorithms for your use case, and you can select different training times to balance speed and accuracy.
ML models are not perfect. Even when well-intentioned, models may have a built-in bias that can lead to unintended consequences.
For example, if you're building an ML model for mortgage lending, it's entirely possible that the model will classify people based on their race or gender. This could have serious implications for your business—a model that's trained on mostly white male borrowers might not be able to accurately predict who would default on a loan.
To address this, you'll want to build bias mitigation into the model from the beginning, including using a transparent engine, actively monitoring the model, and regularly re-training the model based on diverse data. With Akkio, all of this can be done effortlessly.
To recap, it's important for finance businesses to use machine learning to gain a competitive advantage. If you're interested in getting started, check out Akkio and try a free trial.
With Akkio, it's effortless to get started. We'll help you build a robust, transparent ML model that can help your team make better decisions. To get started, simply connect your dataset with us and start seeing results.