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Machine Learning in Finance

30 May
Machine Learning in Finance

Machine learning has been a buzzword for a few years now, but its applications are growing every day. Machine learning is a form of artificial intelligence (AI) and refers to the ability of computers to execute commands without human input. It has become widely used across industries for process automation and to help reduce human error.

Researchers at Statista use historical data from businesses and government organizations to make projections. Their 2023 report expects machine learning to increase by a staggering 17% annually between now and 2030.1 Even the applications of machine learning are vast and exciting, 52% of Americans say they feel more concerned than excited about the growing role of AI in everyday life.2

The same researchers expect finance technology to gain the second-largest market share as machine learning expands into various industries. Thus, financial experts should prepare to apply machine learning in finance and see how this transformative technology can enhance their everyday work.

Keep reading to discover how experts use AI to improve operations in the financial industry.1

Data Sources and Preprocessing

Businesses deal with multiple, disparate sets of data at once. Data sources detail the various streams of data a company may use. For instance, market data provides valuable insights into consumer habits and purchasing trends, while transaction data tracks a company's purchasing, sales, and ordering. The data source for customer experience and purchasing trends may come from market data, while transaction data comes from the company’s own system.

Preprocessing describes the preparation of raw data into a useful format. That's because at times, data may be incomplete or in the wrong format. Some companies rely on machine learning to clean and process the data, removing or fixing incomplete or corrupted information. Through feature engineering, this raw data can be further processed and compiled into more useful information, allowing businesses to gain greater insight into financial positions faster.3

Supervised Learning in Finance

Supervised machine learning describes a computer’s ability to learn that input “x” corresponds to output “x.” By giving the system enough examples, AI can predict outcomes based on data it has not seen. Some forms of AI in finance use supervised machine learning for predictive analysis of the stock market. AI-powered prediction tools use historical data, market trends, and other proprietary data to make informed predictions on the stock exchange.

AI can also aid in credit scoring and risk assessment. Supervised machine learning enables financial companies to draw from known data to make predictions about a consumer or company's financial stability and ability to repay loans.

Likewise, AI-powered accounting software uses AI for financial analysis, combing through transactional data sets and detecting purchasing or payment anomalies that human agents might miss, such as large, unexpected purchases outside a company's normal spending habits or payments with inconsistent amounts or frequencies. This is one obvious way AI offers a solid line of defense against fraud.

Unsupervised Learning in Finance

Unsupervised machine learning gives the system uncategorized data, allowing it to find patterns, anomalies, or relationships on its own. There are several ways AI can help organizations work through uncategorized data. For example, a retail company that hopes to fix inventory issues might leverage machine learning to run a cluster analysis, grouping customers with similar spending habits into a single category. This can help the business predict a big purchasing rush, giving it time to order enough products to meet the anticipated demand.

Machine learning can check unknown data sets in higher volume, meaning it can detect unusual trading activity. The U.S. Treasury Department recently began using AI-enabled fraud detection and recovered $375 million for the 2023 fiscal year alone.4

A market basket analysis tries to identify trends and find what customers might buy together. Businesses may use AI to run market basket analysis with customer data in the hope of cross-selling, as the AI-powered software can detect when customers of one product also purchase another.

Sentiment Analysis and Natural Language Processing (NLP)

Natural language processing allows computers to understand and produce meaningful human language. Researchers have developed NLPs designed to scour social media for investor sentiment. Essentially, these models try to predict market trends based on the sentiment trends revealed in social media posts.7

Text mining describes how AI can sift through huge quantities of text, in this case pulling out relevant investor information. For instance, an investor could use text mining to analyze a company's earnings calls or sentiments among online users before investing.

Many large companies use text mining to connect automatically with customers who complain via social media. This quickly starts the customer support sequence and demonstrates responsiveness to potential clients.

Algorithmic Trading and High-Frequency Trading (HFT)

Algorithmic trading uses automation in hopes of executing faster, better-informed stock transactions. Within algorithmic trading, execution algorithms carry out financial operations according to preset rules.

Market making is one of these algorithmic trading strategies. The market-making trader serves as a middleman, buying and holding stocks while posting prices for other traders. Market makers play an important role in providing liquidity to the market.8 Some market-making firms utilize high-frequency trading to execute numerous transactions in a fraction of a second using complex algorithms.

Risk Management and Compliance

Banks and other financial institutions use credit risk modeling to assess the financial risk associated with loaning money to businesses or individuals. By applying machine learning to the task, they can streamline the credit risk modeling procedure by automating manual input and drawing data from multiple diverse streams.

Likewise, AI-powered accounting software can detect unusual patterns or anomalies in transaction data, helping financial institutions fight fraud more quickly. In fact, 63% of financial institutions said "increased fraud detection" was their primary motivator in employing AI.9

AI-driven accounting software also simplifies compliance monitoring and reporting. Many platforms can send alerts when an unusual transaction is detected. Similarly, AI-powered software can drastically simplify the reporting phase of compliance by compiling documents with minimal manual input.

Ethical and Regulatory Considerations

With all of its benefits in mind, it's important to know the weaknesses of machine learning models. For instance, AI has shown a tendency to amplify human bias. One prime example of this is when racial discrimination plays a role in credit scoring if historical data is allowed to guide future decision-making.10

Companies should strive to use models possessing an appropriate level of transparency. Transparency describes how much of the inner workings of a machine-learning model are accessible. Depending on the composition of the model, full transparency may be extremely difficult.11

Regardless, businesses must ensure they follow all relevant regulatory standards. For instance, companies that do business in the European Union must follow General Data Protection Regulation (GDPR) transparency standards relating to how companies process peoples’ data.12 Likewise, companies in the EU must remain compliant with MiFID II standards for transparency within financial markets.13

Real-World Application

All this talk of machine learning in the finance industry is not a pipe dream. In 2021, JPMorgan Chase began implementing an AI-powered large language model to assist with payment validation screening.14 The company noticed an issue with the previous system, which was producing too many failed validation requests for valid accounts. By 2023, JPMorgan Chase had seen account validation rejections cut by 15-20%. The company also noted a reduction in the levels of fraud due to the use of AI-powered accounting.14

Future Trends in Machine Learning in Finance

The coming years will likely see expanded use of AI in the financial services industry. The appetite is growing, with Zoomers (those born between the mid-1990s and early 2010s) 11% more likely than Baby Boomers to use an AI financial adviser.15

Machine learning also yields exciting potential in the cryptocurrency space, promising to optimize blockchain procedures. Blockchain validates cryptocurrency through a complex interchange of computers. AI can make this more efficient. Likewise, AI-powered models could conceivably apply to crypto markets as well, surveying data, noting trends, and making market predictions.16

As technology evolves, regulation has yet to keep pace but is certainly on its way. Currently, 17 states have enacted AI regulations, with federal legislation likely to come in the future.17

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If you want to leverage finance, data analytics, and machine learning to build your company or your career, consider the Online Master of Science in Finance & Analytics from Santa Clara University's Leavey School of Business. You can start in spring or fall and finish in less than a year while enjoying the convenience of learning online. The experienced faculty helps prepare graduates for the cutting edge of AI-powered finance. Schedule a call with an admissions outreach advisor today.

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