Machine learning (ML) is becoming increasingly important in the finance industry because of its ability to help solve complex problems, enable better automation, and provide valuable insights to financial institutions.
This article will explore machine learning in finance, including its definition, brief history, and how it differs from artificial intelligence.
We’ll also discuss how machine learning is suited to help automate many tasks in finance, its best use cases, benefits, challenges, and relation to FinTech.
What is Machine Learning?
Machine learning is a subset of artificial intelligence (AI) that enables computers to learn from big data without being explicitly programmed.
In other words, machine learning automatically allows computers to learn and improve from experience without human intervention.
The history of machine learning dates back to the mid-20th century, with the development of neural networks and decision trees.
However, it wasn’t until the 1990s that machine learning became a central research topic in academia and industry. Since then, it has evolved rapidly and has become a powerful tool for businesses across industries.
Types of Machine Learning and Key Terms to Know
Supervised Learning
Supervised learning involves training a machine learning model on a labeled dataset, where the algorithms learn associations between data points and provide predictions by generalizing patterns learned from the input data.
It is widely used to predict future outcomes, such as fraud detection, image recognition, and speech recognition.
Unsupervised Learning
Unsupervised learning algorithms allow models to learn on their own, where the training data is unlabeled.
The model seeks to identify structures and patterns in the data and make data-driven assumptions based on the input data.
Clustering, dimensionality reduction, and association algorithms are the most common examples of unsupervised learning.
Reinforcement Learning
Reinforcement learning method allows machine learning models to learn through trial and error using reward-based mechanisms.
ML makes a sequence of decisions and is rewarded according to the chosen action’s success or penalized for wrong answers.
Applications such as game-playing, robotics, and autonomous driving use reinforcement learning.
Regression
Regression models are critical in the ML algorithm toolkit. Regression models establish a relationship between input and output variables by finding the best-fitted line of the data points.
Regression algorithms can predict unknown variables based on historical data.
Deep Learning
Deep Learning is a subset of machine learning that can learn and improve by itself by examining large volumes of unstructured data through complex algorithms called artificial neural networks (ANN).
ANN allows the machine learning model to understand patterns and the most complex features from raw data without manual feature engineering.
Limitations of Machine Learning
Despite its numerous benefits in automating tasks, increasing accuracy, and producing valuable insights, machine learning is subject to limitations.
It can be challenging to implement, particularly in unstructured data such as images and speech.
In addition, ML models are only as good as the data they are trained on, thus exposing a risk of bias that may need added algorithms.
How Does Machine Learning Work?
Machine learning relies on algorithms and statistical models that allow computers to identify patterns, create rules, and make predictions based on data. It involves three key phases: data processing and preparation, model training, and model evaluation.
During the data processing phase, raw data is collected, cleaned, and turned into a format easily understood by the machine learning algorithm.
The data is fed into the machine learning algorithm in the model training phase.
The algorithm analyzes the data and creates a mathematical model to make predictions or classifications. In the model evaluation phase, the model is tested on new data to determine its accuracy.
Best Use Cases of Machine Learning in Finance
Machine learning is suited to help automate many tasks in finance, and it is already being used across various parts of the industry.
Some of the best use cases for machine learning in finance include:
AP Automation
Machine learning can help streamline accounts payable (AP) processes by automatically extracting data from invoices, verifying information, and approving payments.
This reduces the time spent on manual data entry and helps businesses avoid errors and reduce costs.
Fraud Detection
Machine learning can detect fraudulent activities in real-time by analyzing patterns of transactions and customer behavior, reducing the risk of fraud and improving the accuracy of fraud detection.
Credit Risk Assessment
Machine learning can automate the credit risk assessment process, reducing the risk of human errors and providing insights to help businesses make better and informed credit decisions.
Potential Applications of Machine Learning in Finance
There are additional potential use cases of machine learning in finance beyond those currently used, including:
Customer Engagement
Machine learning can enable businesses to better understand their customers by analyzing data from a wide range of sources, such as social media, web analytics, and customer feedback.
With sentiment analysis, financial companies can get an idea of how their customer base feels about the brand and various initiatives.
All the data becomes the basis for creating deep insights into customer needs and preferences.
As a result, businesses can create highly targeted marketing campaigns to engage customers and improve their user experience.
Tracking all of this manually is too time-consuming for businesses.
For instance, Credit Karma uses machine learning and data analysis to understand its clients’ behavior and recommends financial products according to their credit scores and historical credit inquiries.
You can also use ML to create chatbots to support customer service efforts, to help improve the overall customer experience.
Personal Finance Management
One area where machine learning also can be very helpful is personal finance management.
Machine learning algorithms can automate this through expense categorization.
By classifying a range of financial transactions, including; investments, mortgage rates, and credit card payments, into categories, users can understand their financial habits and spending patterns.
For example, Mint is an example of a personal finance management app that uses machine learning to suggest possible savings based on customer purchase data.
Investment Portfolio Management
Investment portfolio and asset management is another area where machine learning can be very critical.
Investment institutions can make better investment decisions by having access to data analyzed by automation in machine learning.
This data helps to recognize trends and patterns and make more accurate predictions for future market performance.
Robo-advisors are online financial advisory services that automate financial planning, asset allocation, and portfolio management using machine learning algorithms.
The algorithms can make personalized investment recommendations based on individual risk tolerance, investment goals, stock prices, and preferences.
This technology can help improve portfolio performance and reduce volatility.
For example, Betterment is a robo-advisor service that uses machine learning algorithms to manage user portfolios for a fraction of the cost of traditional financial advisors. People can make faster trading decisions more confidently.
Algorithmic Trading
Machine learning algorithms are widely used to identify patterns and forecast trends in the stock market with high accuracy.
Algorithms can analyze huge amounts of financial data and identify profitable trades better than traditional stockbrokers. It can also help reduce trading costs and improve portfolio returns.
For instance, JP Morgan uses machine learning to analyze market data and generate trading strategies that result in efficient portfolio trading.
Fraud Detection and Prevention
Machine learning algorithms can help financial institutions detect and prevent fraudulent activities.
These algorithms analyze vast amounts of transactional data to identify suspicious patterns that could indicate fraudulent activities.
By identifying these fraudulent transactions, machine learning can help financial institutions save billions of dollars and avoid reputational damage.
For example, PayPal uses machine learning to analyze transaction and customer data to minimize the risk of fraud in real-time.
Loan Underwriting
Machine learning algorithms are used to underwrite loans, analyze credit history, and reduce financial risks.
These algorithms can analyze vast customer data such as social media profiles, mobile usage, and banking history to evaluate creditworthiness and personalize loan offerings.
Machine learning algorithms can help financial institutions reduce the time it takes to underwrite loans and improve the accuracy of the underwriting decision.
For example, ZestFinance uses machine learning algorithms to identify lending opportunities that traditional underwriting models may miss, thus improving credit access.
Companies that use machine learning can gain efficiency over the long term compared to those that don’t.
Benefits and Challenges of Machine Learning in Finance
Benefits
Improved accuracy and efficiency in decision-making: Machine learning algorithms can analyze vast amounts of data and identify patterns and insights that humans may not pick up.
Ability to process large amounts of data quickly: Machine learning algorithms can process large data sets quickly, enabling financial institutions to make faster decisions.
Real-time fraud detection and risk assessment: Machine learning algorithms can detect fraudulent activities in real-time, reducing the risk of fraud and improving the accuracy of fraud detection.
Cost savings through automation of manual processes: Machine learning algorithms can automate tedious processes, reducing the need for manual labor and reducing costs.
Enhanced insights and identification of patterns that lead to actionable intelligence: Machine learning algorithms can identify patterns in data that may not be evident to human analysts and provide insights that can drive actionable intelligence.
Challenges
Ensuring the accuracy and completeness of data used to train machine learning models: Machine learning algorithms rely on quality data to provide accurate insights.
Ensuring the integrity and completeness of the data used to train them is critical to the success of machine learning in finance.
The output of machine learning algorithms is based on the quality of the data used to train it. Therefore, having high-quality data is essential for machine learning in finance.
Ensuring the privacy and security of sensitive financial data: Financial institutions have access to vast amounts of personal and sensitive data that must be securely stored, processed, and analyzed to ensure data privacy is maintained in compliance with regulations.
Managing the ethical implications of using machine learning in decision-making: As machine learning algorithms are transparent to the data that trains them, it’s essential to consider potential bias and other ethical considerations.
Addressing concerns and perceptions of job displacement: As machine learning algorithms can automate tedious processes, there is a concern that these advancements could lead to job losses. It is critical to address these concerns by providing training opportunities for those at risk of displacement.
Managing continuous training: Machine learning models are sensitive to data variability, meaning they need to be retrained continuously to remain accurate in the dynamic market of finance.
Handling interpretability: Machine learning models are often seen as black boxes, making it difficult for financial experts to understand the logic behind the decisions made. Hence, model interpretability is essential to address this issue.
By embracing the benefits and overcoming the challenges of machine learning in finance, financial institutions can take advantage of this transformative technology to improve efficiency, generate insights, and enhance the customer experience.
Best Practices for Successful Adoption of Machine Learning in Finance
Define clear use cases and objectives: Machine learning techniques are not a one-size-fits-all solution and must be specific to requirements and goals. Therefore, it is essential to have a clear understanding of your company’s unique needs and goals.
Ensure high-quality data: Machine learning models are sensitive to the data sets used to train them. Ensuring that data is accurate and relevant is critical to the success of the implementation.
Develop interpretability: Machine learning models may be hard to interpret; ensure that models are explainable to users without needing a background in data science.
Maintain a culture of innovation: Investment in machine learning needs to be followed up by continuous research and development funds to keep the tools deployed current and performant.
Collaborate with Data Scientists and Financial Experts: Collaboration between risk managers, data scientists, compliance analysts, and other stakeholders is essential to ensure the implementation aligns with business objectives.
Machine Learning and FinTech
Machine learning is transforming the financial industry by enabling new business models and innovations.
FinTech startups are already using machine learning to disrupt traditional financial services.
The use of machine learning is leading to more personalized services, such as customized solutions for individual customers and a reduction in costs.
Conclusion
Machine learning is a powerful tool for finance professionals, enabling the automation of tedious processes, fraud detection, and risk assessment.
Its ability to process large amounts of data efficiently and to create predictive models makes it an essential component in finance.
As machine learning continues to advance, finance professionals must stay up to date with its developments and innovations to ensure they can take full advantage of the opportunities it presents.
