Ever ponder how the financial services industry has expanded to account for almost one-fourth of the global economy? Both Renaissance Technologies, the best-performing investment fund, and JPMorgan, the most valuable bank in the world, integrate AI in their fundamental business operations.
Finance in machine learning extensively to streamline processes and cut down on time. In fact, machine learning is reportedly used by 70% of all financial services companies.
Numerous financial applications exist for machine learning, which may significantly improve corporate and departmental financial operations. In this post, we’ll examine its use cases and provide examples to demonstrate how it functions.
Table of Contents
How is machine learning applied in finance? What is it?
Computer science’s field of machine learning enables computers to learn without explicit programming. It is employed in a wide range of industries, including finance, retail, and healthcare. It is a branch of artificial intelligence that is utilized extensively in the financial sector as well as other fields like social media and sentiment analysis.
The automation of jobs so that people can concentrate on more sophisticated operations is the main goal of many machine-learning applications in the financial industry. Using credit risk prediction models to reduce credit timelines is one example.
To evaluate possible risks related to lending choices based on past data, credit score prediction models are utilized. Banks may use these models to predict when obtaining loans would be most profitable and when doing so could involve too much risk.
Another use of machine learning in finance is to suggest the appropriate financial goods from robo-advisors or financial services firms at the appropriate moment. The models can also assist banks in determining the appropriate pricing for existing services and which clients to approach for new services. With the help of these forecasts, banks may manage their service portfolio more effectively while gradually cutting costs (for example, by automating tedious tasks).
As AI aids fund managers in analyzing massive data sources, such as stock prices, these models also aid in trading choices and asset management. Machine learning algorithms are used by hedge funds to provide stock market projections.
How can AI help?
In a broader sense, artificial intelligence may assist banks in better comprehending the financial behavior of their clients, which can be helpful for tasks like risk assessment and eventually lending choices.
Although “machine learning” and “algorithm” are sometimes conflated or used synonymously in the banking industry, these two jobs are distinct. A computer program is taught how to learn on its own through the process of machine learning. A set of guidelines have been established for algorithm creation that instructs the computer on how to carry out a task.
The financial adviser, compliance staff, or data scientists spend less time and effort communicating with customers as a result of machine learning. Additionally, it cuts down on the time needed to collect data from clients that may be utilized to guide decision-making.
Processes like fraud detection that in the past required human interpretation are made simpler by machine learning.
Financial companies may get a competitive edge, improved accuracy, and a favorable return on investment by utilizing AI. Getting rid of boring activities lowers expenses and increases productivity.
How is it used in finance?
Let’s look at 15 instances of machine learning in finance:
- Chatbots
- Financial monitoring
- Fraud detection and transaction security
- Automation
- Risk assessment
- Handling of data
- Making choices
- Churn forecast
- Financial trading advice
- loan acceptance
- Loan approval
- Job recommendations
- Bankruptcy prediction
- Robo-advisors
- Tax avoidance forecast
Chatbots
Computer programs called chatbots may replicate human communication and provide information. Chatbots can be used in the financial industry to automate processes including responding to compliance team questions, offering customer care guidance, or helping with financial choices.
Some of these chatbots can be seen in action at businesses like American Express, which employs AI assistants on platforms like Facebook Messenger and Amazon Alexa to enable clients to carry out activities like checking their balance or making payments.
Since more than five years ago, Ally Bank has used the Ally Assist bot to offer people a seamless customer support experience for managing their accounts, accessible through an iOS app or through Amazon Alexa instructions.
Financial monitoring
Financial monitoring is tracking the state of your finances over time using tools like investor dashboards or budgeting software. This is referred to as personal capital management in finance. For instance, Cleo is a clever savings app with lots of discussion.
To assist their customers in tracking their expenditures and keeping track of their progress towards attaining their financial objectives, financial advisers also employ financial monitoring software. These apps can also notify users when they stray from their budget and offer suggestions for how to make the necessary adjustments.
Fraud Detection
Another area where AI may be used is to prevent fraud.
Machine learning algorithms are used by security teams to analyse millions of data points, spot fraud as it happens, and stop it before money is released from a client’s account. Deep learning, which uses big neural networks and is in this instance powered by a tonne of financial data, makes this feasible.
When incoming transactions are analysed for trends and compared to historical data, a fraud protection system can decide whether something seems off or suspicious, such as a high volume of tiny transactions.
Additionally, it aids in preventing false declines or false positives, which can occur when an algorithm identifies a transaction as suspicious even when there isn’t any actual fraud. A fraud prevention system can improve in accuracy over time by doing validation and backtesting, identifying actual fraudulent transactions before they happen.
Automation
Many businesses employ automation to cut the expenses of manual procedures. For instance, a bank may have a group in charge of creating new account applications using an API.
The API’s fraud detection capabilities might be powered by machine learning, which would completely automate the job of the API staff. This would free them up to concentrate on other jobs at the bank, including giving customers guidance and financial education. The API team would still need to check each customer’s eligibility for an account in this scenario, but a different set of staff could accomplish the task entirely—or it could even be fully automated.
Reducing paperwork has been a top priority for many financial businesses. Banks spend billions of dollars every year on paperwork and compliance tasks including confirming account ownership or keeping track on customer behaviour, according to Reuters.
Machine learning can automate some or all of this labour, allowing employees to concentrate on the more intricate facets of their customers’ accounts, such as assisting them in making long-term financial decisions or attending to their particular requirements.
Additionally, automation enables financial advisers to work more efficiently (and maybe avoid performing some jobs completely). This frees up advisers’ time so they can spend it advising customers rather than doing tedious data entry or spending hours every week creating compliance documents that are then distributed to other teams.
Risk Assessment
A crucial component of every investment plan is risk analysis. In order to effectively manage risks, it requires quantifying, collecting, and analysing them. In the field of finance, this entails spotting possible risks in a deal using a mix of quantitative and qualitative research, such as estimating the loss based on past performance or evaluating risk based on variables like industry concentration or macroeconomic conditions.
Machine learning algorithms may be used for risk management in addition to offering insight into transaction hazards by quantifying such risks and enabling businesses to develop rules around them. This aids businesses in creating solid trading strategies, limiting their potential losses based on previous trends, and actively defending themselves against future threats.
Handling of Data
Gathering, storing, and organising data so that it can be analysed is the process of data management. This frequently requires keeping an eye on changes in the financial markets. For instance, a market watcher may examine all trades made by a company to search for trends or patterns that would point to possible areas of concern.
Instead of waiting for an analyst to manually identify these trends, the market monitor would subsequently be able to do so using machine learning. As a result, analysts would have more time to concentrate on urgent problems, and they may even notify management when action is required.
Making choices
Another area where AI might assist cut costs and boost efficiency is in decision-making. In the past, financial institutions would have teams in charge of giving clients investing advice, such as telling them which stocks to purchase or sell. The decision-making process would be lengthier and more expensive if these teams had to manually do due diligence on new investments or keep track of every transaction made by individual traders.
Businesses may offer their analysts more time and focus on analysis rather than data gathering and analysis by employing machine learning for this purpose, boosting productivity and lowering decision-related expenses.
Churn forecast
Churn prediction is one area in which machine learning may be quite useful in the banking industry. This entails identifying the clients who are most likely to go from your business and when they will decide to do so. Churn tracking enables businesses to pinpoint areas that require improvement, such as greater training for their advisers or enhancing the client experience.
By giving clients helpful information and suggestions, churn prediction aids in improving the understanding of consumers and may even assist to stop turnover before it occurs. The correct tools can even help advisers determine which clients are most likely to go and whether or not to spend time and money trying to keep them.
Trading
A company’s trading strategy significantly affects its expenses and efficiency. An algorithm that automatically buys and sells based on market conditions might be the foundation of a trading strategy. This can assist businesses in avoiding placing deals that are unprofitable or unnecessary, thus saving both time and money.
Financial organizations are increasingly using algorithmic trading tactics because many of them consider them to be effective ways to manage risk and generate profits.
Financial advisory
Financial advice is one area where machine learning may have a significant influence on the financial services sector. You’ve used machine learning if you’ve ever used an automated phone system. This technique employs natural language processing (NLP) algorithms to transform speech to text and text to speech.
Loan approval
Loan approval is another area in finance using machine learning may be employed. This method used to be quite labor-intensive, needing a person to examine each loan application and reach a conclusion. This can be expensive and time-consuming.
However, businesses may create algorithms that automatically assess loan applications and suggest whether to accept or refuse them using machine learning. This helps to guarantee that loans are granted to individuals who are most likely to repay them, lowering risk for the lender while also saving time.
Job recommendations
Companies are increasingly using machine learning to propose opportunities to people in the current labor market. This is accomplished by taking into account a number of elements, including a candidate’s qualifications, background, and geography.
The recruiting process may be sped up for both the employer and the candidate by using machine learning to evaluate a candidate’s suitability for a position.
Bankruptcy prediction
The prediction of bankruptcy is another area in finance where machine learning is being employed. Building models to determine which clients are most likely to file for bankruptcy includes utilizing previous data. Banks and other financial organizations can utilize this information to inform their judgments regarding credit lines and lending.
Robo-advisors
An automated investment management service known as a “robo-advisor” makes investment suggestions based on your objectives and risk tolerance. These services are rising in demand since they provide a cost-effective substitute for conventional financial counselors.
Today’s industry offers a wide variety of robo-advisors, including Betterment and Wealthfront. Even with only a few bucks, you can start an account with several of these sites.
Tax evasion prediction
Last but not least, tax avoidance is also predicted using machine learning. This is accomplished by reviewing information about a taxpayer’s earnings, outlays, and assets. Algorithms that can determine which taxpayers are most likely to avoid taxes can be created based on this data.
Tax authorities can then use this information to direct their efforts toward auditing the taxpayers who are most likely to escape paying their taxes. This helps to guarantee that taxes are collected from those who are best able to pay them while also saving time and money.
How to implement ML in your business?
In the past, ML implementation required a team of data science experts with technical expertise in TensorFlow and Python as well as an understanding of machine learning methods like reinforcement learning. These AI initiatives would be expensive and time-consuming even with the proper people, putting many FinTech and portfolio management businesses at a competitive disadvantage.
Simply link a relevant dataset you already have to our platform to begin going, and we’ll take care of the rest. In our case study on churn prediction or on fraud detection, you may see an illustration of how this can seem. Our platform will get you there quickly regardless of whether you want to create regression models on time series data or process automation optimization models for underwriting.
Challenges faced
When utilizing ML in finance, there are two main difficulties that one could run into
- Inaccuracy
- Unintended bias
Inaccuracy
There is a balance between overfitting and underfitting in machine learning. Underfitting arises when a model is too basic and might not be able to fully capture the essential data, whereas overfitting comes when a model is too complicated and does not generalize effectively to new data.
By lengthening the training period, including more high-quality labeled data, and utilizing powerful algorithms, low accuracy may be addressed.
Unintended bias
The ML models are not flawless. Models may have an inherent bias, even when done with the best of intentions, which might have unforeseen results.
For instance, it’s completely feasible that a machine learning (ML) model you’re developing for mortgage lending may categorise people based on their ethnicity or gender. A model that is mostly trained on white male borrowers may not be able to effectively predict who would fail on a loan, which might have major ramifications for your company.
Conclusion
Machine learning has revolutionized the financial industry by providing innovative solutions to numerous challenges. The 15 applications and use cases discussed in this article highlight the diverse ways in which machine learning is employed in finance. From fraud detection and risk assessment to algorithmic trading and personalized recommendations, these applications have enhanced decision-making, increased efficiency, and improved customer experience. As machine learning continues to advance, we can expect even more exciting developments and opportunities in the intersection of finance and technology.
