Finance has always been a hotbed of innovation. The introduction of machine learning (ML) has been revolutionary, particularly for the FAANG+ companies. Machine learning in finance is harnessing the power of machine learning to disrupt financial services, optimize operations, and improve customer experience.
However, it means more than shaping the future of finance; it also creates a new world to advance your career. The finance industry is experiencing a high demand for machine learning engineers and data scientists worldwide.
In this article, we give you a brief introduction to ML and present 15 uses cases of machine learning in finance. Further, we also explore the key skills for machine learning engineers in finance.
What Is Machine Learning?
Machine learning is the science and art of programming computers to learn from data so they can act or make decisions on their own. In essence, machine learning engineers use statistical approaches to understand patterns and associations in data, which help systems get better as time passes. This makes machine learning an excellent tool for finance, given the enormous amounts of data generated daily.
Key Components of Machine Learning
The following are the key components of machine learning:
- Collect data: Gather related data from all sources
- Data Preprocessing: Clean and organize your data to make it suitable for analysis. Breaking the data into test and training set
- Model Selection: The process of selecting a proper machine learning algorithm for implementing an application
- Model Training: Providing the training data for the selected algorithm to learn
- Model Evaluation/Testing: Evaluating the model on test data to see how it performs
- Production Deployment: The phase where a trained machine learning model is integrated into an existing production environment to provide real-time predictions or make decisions based on new data
15 Use Cases of Machine Learning in Finance Industry
Machine learning engineers use machine learning for many things in the finance industry. However, here are the most common uses of ML in finance sector:
1. Algorithmic Trading
Beyond mere speed, machine learning models have enabled algorithmic trading to become smarter. Artificial intelligence (AI) models even include sentiment analysis, reading through news articles and social media posts to figure out what investors are thinking about the market and how that could impact stock prices.
Machine learning engineers are also getting into advanced trading techniques like pairs-trading, where algorithms identify similar assets and extract profit from mispricings. This natural evolution is spawning a new type of “quant” trader—one who increasingly relies on machine learning models for their advantage.
Real-life example
Virtu Financial, a high-frequency trading firm, uses complex algorithms to analyze market data and execute trades at incredibly fast speeds. These algorithms can identify patterns, arbitrage opportunities, and execute trades based on pre-defined strategies.
2. Fraud Detection
Although both rule-based systems and traditional machine learning models have been used for a long time to assess fraud, recently, the newer models have become more and more prominent in this area.
Machine learning engineers are using machine learning models that are well-suited to assessing an ongoing risk of fraud because they can adapt to new patterns observed in data over time. Their state-of-the-art adaptation techniques include, but are not limited to, clustering and densities for spotting unusual data that might signal a potential risk of fraud.
Real-life example
PayPal employs machine learning algorithms to analyze millions of transactions in real-time. These algorithms look for unusual patterns, such as large purchases from unfamiliar locations or sudden changes in spending behavior, which could indicate fraudulent activity.
3. Risk Assessment
From a risk perspective, machine learning has profoundly changed our way of thinking and provided us with the most detailed assessments. Legacy models are based on time-tested data categories like credit scores and income brackets.
In contrast, machine learning engineers are making machine learning models that can take into account as many variables as you believe do (or even don’t) make judgments, e.g., social media activity, utility bill payments, or a psychometrically unimaginable metric.
Real-life example
Banks like JPMorgan Chase use machine learning models to assess credit risk for loan applicants. These models analyze various data points, including credit history, income, employment, and debt-to-income ratio, to predict the likelihood of default.
4. Customer Service
Chatbots are good at answering the questions that customers ask most frequently, but customer service has changed from just providing basic FAQs thanks to machine learning. This allows machine learning engineers develop AI chatbots to have NLP capabilities, opening up the possibility of users asking more complicated questions and we can provide a response as tailored for them.
Even better, more advanced chatbots offer an unprecedentedly smooth financial advisory experience by automatically generating personalized product or service recommendations based on a detailed insight into customer needs and preferences.
Real-life example
Capital One uses chatbots powered by natural language processing (NLP) to handle customer inquiries about account balances, transactions, and credit card rewards.
5. Personalized Financial Advice
When it comes to investing and finances, machine learning-driven robo-advisors are making these concepts easier for people. Powered by algorithms, these digital platforms automatically design and administer individualized investment portfolios based on users’ risk appetite, financial objectives, and time horizon.
They can do everything from tax-loss harvesting to automatic rebalancing and financial planning recommendations. This helps machine learning engineers democratize financial advice, making it cost-effective and available to the masses.
Real-life example
Wealthfront, a robo-advisor, uses machine learning algorithms to analyze a client’s risk tolerance, investment goals, and financial situation to create a personalized investment portfolio.
6. Loan Underwriting
Machine learning is helping machine learning engineers accelerate loan underwriting and reigniting a wider availability of credit. Traditional credit scoring models often fail to capture those with little-to-no credit history or who manage their finances through myriad other ways.
Non-traditional credit indicators machine learning models can achieve a more holistic view of discretionary income, including one’s ability and willingness to repay loans. Such a step can also carve out a path for other individuals who are underprivileged, like small business owners and gig workers.
Real-life example
Upstart, an online lending platform, uses machine learning models to assess creditworthiness beyond traditional credit scores. These models consider education, employment history, and other non-traditional data points to determine loan eligibility and interest rates.
7. Portfolio Management
Passive investing is in vogue, but machine learning has revitalized active portfolio management. machine learning-enabled algorithms can help swiftly execute factor investing or tactical asset allocation most efficiently, which human machine learning engineers would take more time to do with inferior efficacy.
This allows them to be highly responsive to continual market changes and helps achieve the optimal strategy during high-potential periods of profit, as opposed to slower macro dynamics, which under traditional strategies lag behind due largely in part to outdated axis divisional tactics.
Real-life example
Betterment, another robo-advisor, uses machine learning algorithms to automatically rebalance client portfolios to maintain the desired asset allocation and risk level.
8. Stock Price Prediction
With ever-growing machine learning models being used to predict stock prices, today, they are not just considering traditional financial data points but also blending a lot of exotic new sources like social media sentiments.
Machine learning engineers use these alternative data sets to track market trends, as consumer behavior. Having said this, it is important to remember that the stock market is a tough game to predict (and machine learning models are getting better and increasing in accuracy but still not able at all levels of security).
Real-life example
Hedge funds like Renaissance Technologies employ machine learning models to analyze vast amounts of market data to predict stock price movements.
9. Insurance Underwriting
Advancements in machine learning have made it easier for machine learning engineers to assess risk for underwriting and UBI usage-based insurance. Insurers can also use complex telematics data from connected car pressure sensors or fitness wristbands to customize premiums based on real driving behavior, such as speed and rotation.
This rewards both careful driving and good health, helping to stop those who load insurance premiums from refusing coverage.
Real-life example
Lemonade, an insurance tech company, uses machine learning algorithms to assess risk profiles of potential customers. These algorithms analyze vast amounts of data (e.g., customer demographics, past claims history, social media activity) to determine insurance premiums and personalize policy options in real time.
10. Regulatory Compliance
Machine learning is no longer just automating regulatory compliance roles. Machine learning models are also helping identify potential risks to compliance before they occur and become more serious.
Machine learning engineers are developing machine learning algorithms that can analyze a massive amount of data, such as transaction records, communications, and social media activity, which could show some abnormal behavior (dynamic pattern) for fraud or ethical violations. This enables a financial institution to act proactively and patch vulnerabilities before they turn into expensive issues.
Real-life example
ComplyAdvantage, a regtech firm, employs machine learning to monitor financial transactions for potential money laundering activities. Their system scans millions of data points, flagging suspicious patterns and ensuring compliance with anti-money laundering (AML) regulations.
11. High-Frequency Trading (HFT)
In recent years such technological advancements have been made that enable HFT firms to make trades in microseconds and even nanoseconds with machine learning algorithms. In this, the algorithms help machine learning engineers comb through market data for little price discrepancies and execute trades in milliseconds; speed faster than what a human trader can even blink an eye at.
Real-life example
Citadel Securities, a leading market maker, utilizes HFT algorithms to execute trades at lightning-fast speeds. These algorithms analyze market data and execute orders within microseconds, aiming to profit from small price discrepancies
12. Sentiment Analysis
A new generation of NLP practices—e.g., sentiment analysis—is becoming more specialized for matching the sentiment in news, tweets, etc., with market performance trends. Machine learning engineers use machine learning models that can predict market movements and help mandate new trading strategies based on how people feel about certain stocks, companies, or even economic events. This acknowledges the presence of human emotions in financial markets and tries to separate that out, putting it on a scale.
Real-life example
Bloomberg Terminal’s sentiment analysis tools gauge market sentiment by processing news articles, social media posts, and other sources. These tools identify positive or negative sentiments towards specific companies or assets, helping traders make informed decisions.
13. News Analytics
Beyond sentiment analysis, machine learning is also being used to analyze news articles for hidden insights that can impact financial markets. These algorithms can help machine learning engineers identify patterns, correlations, and causal relationships between news events and market movements. For example, they might detect that news about a company’s earnings release tends to precede a spike in its stock price. This information can be invaluable for traders and investors seeking to anticipate market reactions to news events.
Real-life example
Kensho, acquired by S&P Global, uses machine learning to analyze news articles and extract relevant financial information.
14. Credit Scoring
Traditional credit scoring models have long been criticized for their limitations and potential biases. Machine learning is transforming credit scoring by incorporating a wider range of data points and using more sophisticated algorithms to assess creditworthiness.
This can help machine learning engineers to get more accurate and personalized credit scores, potentially opening up access to credit for individuals who have been underserved by traditional models. It can also help lenders make more informed decisions about loan approvals and interest rates, reducing the risk of defaults and promoting financial inclusion.
15. Debt Collection
While debt collection can be a sensitive issue, machine learning is making it more efficient and less stressful for both debtors and collectors. Machine learning-powered chatbots can initiate conversations with debtors, answer questions, negotiate payment plans, and even provide financial counseling.
By automating routine tasks and providing personalized assistance, machine learning engineers can benefit from a streamlined debt collection process, reduced costs, and improved outcomes for everyone involved.
Real-life example
TrueAccord, a debt collection agency, leverages machine learning to predict debtor behavior. Their algorithms analyze historical repayment patterns, communication preferences, and other factors to tailor personalized collection strategies that are more effective and less confrontational.
Required Skills for Machine Learning Engineers in Finance
Want to ride the wave of machine learning in finance? Here are some essential skills you’ll need as a machine learning engineer:
1. Programming Languages
Proficiency in programming languages like Python, R and SQL is essential for machine learning engineers. Python is particularly popular due to its extensive libraries and frameworks for data analysis and machine learning, such as TensorFlow, scikit-learn, and PyTorch.
2. Statistical Analysis
A strong understanding of statistical concepts is crucial for developing and evaluating machine learning models. Knowledge of probability, distributions, and hypothesis testing is fundamental for interpreting data accurately. A lot of machine learning interview questions are asked around this topic.
3. Data Handling and Preprocessing
The ability to clean, preprocess, and manipulate data is vital for machine learning projects. This includes dealing with missing values, normalizing data, and transforming it into a suitable format for analysis.
4. Machine Learning Algorithms
Familiarity with various machine learning algorithms, including supervised, unsupervised, and reinforcement learning, is essential. Understanding when and how to apply these algorithms is critical for solving machine learning interview questions.
5. Deep Learning
Deep learning, a subset of machine learning, is increasingly important in finance for machine learning engineers because it can handle complex datasets and generate more accurate predictions. Skills in using deep learning frameworks such as Keras and TensorFlow, understanding neural network architectures, and knowledge of techniques like convolutional neural networks (CNNs) and recurrent neural networks (RNNs) are invaluable.
6. Generative AI
Generative AI, which includes models like Generative Adversarial Networks (GANs) and transformers, can be used by machine learning engineers in finance for tasks such as synthetic data generation, anomaly detection, and enhancing trading algorithms.
7. Domain Knowledge
Understanding the finance industry, including knowledge of financial instruments, markets, and regulations, is crucial for applying machine learning effectively. This domain knowledge helps in developing relevant models and interpreting their results accurately.
8. Communication Skills
Effective communication skills are necessary to convey complex machine learning concepts and findings to non-technical stakeholders. This includes writing reports, presenting results, and collaborating with cross-functional teams.
How to Get Started With a Machine Learning Engineer Job in Finance?
Machine learning (ML) isn’t just a buzzword; it’s a real game-changer in the finance industry. If you’re looking to stand out and propel in your machine learning engineer job, embracing machine learning is a smart move. Here’s your roadmap to success:
1. Build a Solid Foundation
- Try Machine Learning Courses: Start with the basics of programming (Python or R), statistics, and machine learning algorithms. Online machine learning courses and tutorials are excellent resources.
- Understand Financial Concepts: A strong grasp of finance is essential. Brush up on topics like investment strategies, risk management, and financial markets.
2. Get Hands-On Experience
- Personal Projects: Apply your machine learning skills to financial datasets. Explore kaggle.com for datasets and competitions related to finance.
- Internships or Entry-Level Machine Learning Engineer Jobs: Seek opportunities where you can work on real-world machine learning projects in finance. This hands-on experience is invaluable.
3. Specialize in a Niche
- Risk Management: Dive deep into credit risk modeling, fraud detection, or portfolio risk optimization.
- Algorithmic Trading: Focus on developing trading strategies, market prediction models, or high-frequency trading algorithms.
- Quantitative Finance (Quant): Combine your machine learning skills with financial expertise to build sophisticated models for pricing derivatives, risk management, or asset allocation.
4. Network and Collaborate
- Attend Industry Events: Conferences, meetups, and workshops are great places to connect with other machine learning engineers in finance.
- Join Online Communities: Participate in online forums and groups to share ideas, ask questions, and learn from other machine learning engineers.
- Collaborate on Projects: Team up with machine learning engineers on machine learning projects to expand your knowledge and build your portfolio.
5. Stay Ahead of the Curve
- Continuous Learning: Machine learning is a rapidly evolving field. Stay up-to-date with the latest algorithms, techniques, and tools by reading research papers, attending webinars, and taking advanced machine learning courses.
- Embrace New Technologies: Explore emerging areas like deep learning, reinforcement learning, and natural language processing (NLP) to expand your skill set.
6. Showcase Your Expertise
- Build a Portfolio: Create a website or online portfolio to showcase your prowess as a machine learning engineer and highlight your accomplishments.
- Publish Articles or Blog Posts: Share your knowledge and insights on machine learning in finance to establish yourself as a thought leader.
- Speak at Conferences: If you’re confident in your expertise, consider speaking at industry events to increase your visibility.
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FAQs: Machine Learning in Finance
Q1. What is Machine Learning’s Role in Financial Fraud Detection?
Machine learning plays a crucial role in financial fraud detection by helping machine learning engineers analyze transaction patterns and identify anomalies that may indicate fraudulent activities. Algorithms can learn from historical data to recognize suspicious behaviors and flag them for further investigation, helping to prevent fraud before it occurs.
Q2. How Can Machine Learning Improve Risk Management in Finance?
Machine learning helps machine learning engineers improve risk management by analyzing large datasets to identify potential risks and predict future trends. For example, banks can use machine learning models to assess the creditworthiness of borrowers, predict loan defaults, and manage investment portfolios more effectively.
Q3. What Are The Benefits of Using Machine Learning For Personalized Banking?
ML enables personalized banking by analyzing customer data and preferences to offer tailored financial products and services. This improves customer satisfaction, enhances user experience, and helps financial institutions build stronger relationships with their clients.
Q4. Is Domain Knowledge In Finance Necessary For Machine Learning Engineers?
No, domain knowledge is not necessary for machine learning engineers but it is good to have. Understanding financial instruments, markets, and regulations helps in developing relevant machine learning models and interpreting their results accurately, ensuring their practical application in financial contexts.
Q5. How Can I Start A Career In Machine Learning In Finance?
To start a career in machine learning in finance, focus on building a strong foundation in both machine learning and finance. Enroll in relevant machine learning courses, gain practical experience through internships or projects, network with machine learning engineers, and continuously update your skills. Building a portfolio showcasing your machine learning projects in finance can also help you stand out to potential employers.
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