Today, machine learning affects virtually all corners of finance. Investment firms and hedge funds utilize machine learning (ML) models, fed with huge amounts of alternative and traditional data, to aid in evaluating assets and stocks. But that isn’t all.
According to market research, the global artificial intelligence in fintech market grew from $9.15 billion in 2022 to $11.59 billion in 2023, which implies a growth rate of 26.8% per year. These numbers are further supported by the most recent statistics in finance—70% of financial services companies use ML to boost their predictive and statistical capabilities.
It is clear that the applications of machine learning in the finance industry are rapidly growing. Today, ML is used for everything from trading decisions to risk assessment. It has completely transformed the way in which the financial services industry functions and manages data.
When it comes to the world of machine learning in finance, new trends keep emerging. This blog shares the key trends and applications to know in 2024. Beginning with the historical context of AI and ML in finance, we provide an overview of the impacts of the major trends. We’ll also explain numerous applications like algorithmic trading, fraud detection and prevention, customer service, and chatbots. Finally, we discuss the challenges and considerations for using ML in the financial industry.
Keep reading to learn about this year’s trends and applications of machine learning in finance.
The Rise of Machine Learning in Finance
Machine learning has transformed finance over the past decade. Initially, it was used for automation and data analysis, but now it’s vital for fraud detection, customer service, and risk management.
Three key reasons make machine learning essential in finance: it enhances data analysis, automates tasks, and predicts risks and trends. These capabilities help financial institutions make informed decisions, increase efficiency, and reduce errors.
Essential ML Technologies
Several machine learning technologies have contributed greatly to the transformation of the finance sector. Some of the key technologies are:
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Deep Learning: Deep learning in finance has changed how machines learn, comprehend, and interact with complex data. Essentially, it enables computers to autonomously identify patterns and make decisions from huge amounts of unstructured data. It is especially useful in complex jobs such as market predictions, natural language processing, and image recognition.
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Neural Networks: Neural networks are capable of recognizing patterns in large datasets. In fact, they are the backbone of several machine learning finance applications, including fraud detection and credit scoring.
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Reinforcement Learning: This kind of machine learning is focused on decision-making by learning from the results of previous actions. It is usually used in algorithmic trading, where the aim is to increase returns by making informed trading decisions.
Key Applications of Machine Learning in Finance
Now that you are aware of the emerging trends of ML in finance, let’s explore its applications, highlighting the challenges and benefits of ML for business. Furthermore, we will also share a case study for a better understanding.
Application 1: Algorithmic Trading
Algorithmic trading is the use of algorithms to make better trade decisions. Generally, traders form mathematical models that monitor trade activities and business news in real time to identify any aspects that may lead to a rise or fall in security prices. ML models interpret huge amounts of data to recognize patterns and make trading decisions. The benefits of ML for algorithmic trading include:
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Analyzing large datasets simultaneously
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Faster information processing
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Reduced human error
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Improved trading strategies
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Optimized trading returns
However, algorithmic trading also poses a few challenges, such as:
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Difficulty in interpretation due to model complexity
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Accountability and transparency issues
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Market unpredictability
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Ethical issues
Among the most successful machine learning use cases in finance is that of Renaissance Technologies’ Medallion Fund. It uses ML for algorithm trading, attaining an average return of 66% annually from 1988 to 2018. The fund’s success implies the potential of ML to transform the finance industry.
Application 2: Fraud Detection and Prevention
Today, most businesses leverage ML to flag and fight fraudulent financial transactions. ML operates by scanning vast amounts of data to identify specific anomalies or activities and flags them for continued investigation by companies. Thus, machine learning allows real-time analysis of transactions and consumer behavior. Some of the key advantages of ML in fraud detection and prevention are:
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Improved security
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Decreased financial losses
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Instant identification of fraudulent activities
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Maintaining customer trust due to protection from unauthorized transactions
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Reduced number of false positives
The major challenges that companies may have to face because of artificial intelligence applications in finance with regard to fraud detection and prevention:
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A balance between sensitivity and accuracy
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Flagging legitimate transactions as fraudulent
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Less sensitive models miss fraud
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Staying updated with evolving fraudulent tactics
PayPal, a global leader in online payments, uses machine learning to detect and prevent fraud and analyze transaction data in real time. Over time, it has substantially reduced fraud, protecting its users and improving security across the platform.
Application 3: Credit Scoring and Risk Assessment
Machine learning has changed risk assessment and credit scoring by allowing more accurate predictions of a person’s creditworthiness. Conventional methods were focused on income and credit history. However, machine learning finance projects involve the analysis of alternative data sources such as transaction history and social media activity to evaluate risk. The key benefits of using machine learning for credit scoring and risk assessment include:
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Accurate risk predictions
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Faster loan approvals
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Better risk management
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More inclusive and fairer lending practices
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Expanded access to credit
Some of the challenges that this use may result in are related to:
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Regulatory compliance
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Model fairness
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Data privacy
Zest AI, a venture specialized in ML-driven credit scoring, has helped lenders enhance their credit decision processes. Through the use of ML for analyzing large data, Zest AI models have allowed lenders to approve more loans while reducing or maintaining risk levels.
Application 4: Customer Service and Chatbots
ML-powered chatbots are becoming increasingly popular due to their ability to provide customer service around the clock. Moreover, they can simultaneously handle numerous customer inquiries, from basic account information to difficult financial advice. That’s not all — there are numerous benefits of using ML-powered chatbots in finance, which include:
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Improved customer experience and satisfaction
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Reduced operational costs
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Lesser resources and time
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Improved operational efficiency
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Personalized financial advice
ML-driven chatbots face challenges like:
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Maintaining human-like interaction (engaging and empathetic)
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Handling complex inquires
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Data privacy
Erica, Bank of America’s chatbot, is among the best machine learning use cases in banking. Erica offers customized financial advice, answers customer queries, and assists with transactions.
Application 5: Portfolio Management
Another common application of machine learning is in portfolio management due to its ability to optimize investment strategies and increase returns. Machine learning algorithms interpret large amounts of market data, including financial statements, economic indicators, and historical trends, to anticipate future asset performance.
Using machine learning for portfolio management has several pros:
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Improved returns
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Minimized risk
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Dynamic rebalancing
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Personalized investment strategies
ML-driven portfolio management faces issues like:
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User trust
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Model reliability
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Market volatility
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Unpredictability of financial markets
Wealthfront, a robo-advisor, utilizes machine learning algorithms to manage its clients’ investment portfolios. The platform automatically rebalances portfolios and maximizes tax efficiency, leading to strong returns without human intervention.
Key Trends in Machine Learning for Finance in 2024
In this section, we explore the top trends in machine learning for finance in 2024, providing an overview of each trend and delving into their impacts.
Trend 1: Explainable AI (XAI) in Finance
With the increasing sophistication of ML in finance, the need for interpretability and transparency has risen. Explainable AI or XAI addresses the challenge by making machine learning models more understandable to humans. In the financial landscape, where decisions have far-reaching consequences, it is extremely important for stakeholders – including customers, investors, and regulators – to understand why and how the models make certain decisions.
XAI adoption in finance is driven by regulatory necessities and the requirement to build trust in decisions that artificial intelligence drives. XAI aids financial institutions in meeting these requirements by offering insights into how decisions are made. This makes it simpler to recognize and address any potential errors or biases. Hence, this trend is especially crucial in areas such as credit scoring, where biased decisions can have a huge impact.
Trend 2: Real-Time Data Processing and Analysis
Among the most significant trends of 2024 is the shift towards real-time data analysis and processing using machine learning. As markets become increasingly dynamic and data streams augment in complexity and volume, the potential to process and analyze data in real time has become highly beneficial.
When it comes to the impact of this trend, ML contributes significantly to real-time analytics by automating large dataset analysis and offering instant insights. This ability is vital in domains such as trading, where decisions of split-second matter can have a huge impact on profits. It is also beneficial for fraud detection and risk management since institutions can identify any suspicious activities or risks the moment they occur and respond accordingly.
Trend 3: Ethical AI and Bias Mitigation
With the rising use of machine learning in banking and finance, concerns regarding ethics and biases in ML models have risen. In 2024, there is an increasing need to ensure that these models are accountable, transparent, and fair. Thus, bias mitigation and ethical AI have taken the topmost position in the priorities of financial institutions.
As a result of this trend, it is necessary to develop ML models that are both equitable and accurate. This calls for ongoing monitoring and adjustment of algorithms to ensure they do not exacerbate or perpetuate current biases. Financial institutions have also started to adopt ethical AI frameworks to navigate the development and deployment of machine learning systems while ensuring they align with regulatory standards and societal values.
Trend 4: ML-Driven Personalization
Personalized financial products provide companies with a competitive edge. Today, customers expect products and services related to the financial sector to be customized to their unique preferences and needs. Machine learning applications in finance allow such customization by analyzing customers’ data and anticipating their preferences and behavior.
ML is enabling hyper-personalization in financial services, which has further increased the technology’s adoption across the industry. From customized loan offers to tailored investment advice, ML is allowing businesses to deliver more engaging and relevant experiences to their users. The trend drives business growth and enhances customer satisfaction, resulting in customer retention and loyalty.
Trend 5: Integration of ML with Blockchain
The intersection of machine learning with blockchain technologies is an emerging trend this year, particularly in the finance sector. The secure and decentralized nature of blockchain complements ML by offering a trustworthy environment for processing transactions and sharing data.
Such an integration is further expected to improve the efficiency, transparency, and security of financial transactions in 2024. Machine learning can be utilized to analyze blockchain data and recognize anomalies and patterns that may imply fraudulent activity. Furthermore, convergence can help streamline processes such as smart contracts, improving reliability and efficiency.
Challenges and Considerations for ML in Finance
Using machine learning in finance has numerous benefits. Nonetheless, it also has its challenges. Below, we focus on some challenges and considerations for using ML in this industry to ensure successful implementation.
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Data Quality and Availability: First and foremost, the effectiveness and accuracy of ML models are heavily dependent on the quality of data they are trained on. In the finance industry, this implies access to large volumes of financial data for machine learning that are updated, clean, and relevant. Poor data quality or insufficient data can lead to inaccurate predictions and financial losses.
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Regulatory Compliance: Implementing ML solutions implies the need to carefully navigate complex regulatory frameworks to ensure compliance with laws regarding consumer protection, anti-money laundering, and data privacy. Non-compliance may result in damage to the company’s reputation and legal penalties.
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Ethical Considerations: When it comes to finance, ML services must ensure fairness, transparency, and accountability. Bias in these models can result in unfair treatment of specific groups. Thus, ML models must be explainable and free from bias.
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Scalability and Integration: Scaling machine learning models to handle extensive, dynamic data is challenging. Also, integrating the models into current financial systems implies compatibility, performance, and security considerations to ensure hassle-free functioning without disrupting any financial activities.
Final Verdict
As we move toward the end of 2024, the use of machine learning in the finance industry will deepen, leading to a more efficient, innovative financial world. The applications of ML in finance, such as algorithmic trading, fraud detection and prevention, and portfolio management, will augment the adoption of machine learning by financial institutions due to their numerous benefits and key trends like XAI, ML-driven personalization, and integration of ML with blockchain will further evolve with time.
To continue to be successful, fintech companies must stay up-to-date with market trends. Now is the time to adopt ML technologies for your business to improve efficiency and reduce operational costs.
Contact LITSLINK today. We’ll help you explore the advantages of machine learning to gain a competitive edge.
