What is data mining in finance?
Data mining is the practice of performing analysis on raw, unstructured data sets to produce comprehensible and functional results. Analytics in finance can be used to assess the success of strategies, understand current trends, and optimize the efficiency of operations.
The main goal of text and data mining algorithms is to discover relationships and find patterns in data, as this information has a very valuable potential. From scientific research to e-commerce, all kinds of organizations, businesses, and industries can be improved through the use of well-structured data.
Data mining algorithms are capable of performing the following procedures:
Also known as opinion mining, this is one of the most important techniques related to data mining. It has many applications across several fields and disciplines. Sentiment analysis is a textual analysis of various data sources, carried out to extract user emotions conveyed through the information they share.
Prime sources of user sentiment include e-commerce platforms, blogs, social media sites, e-mails, and customer support tickets. Text sentiment analysis can be used for polarity detection, allowing companies to recognize whether online populations share a positive, negative, or neutral opinion towards any product, service, situation, or event.
For example, sentiment extraction is useful for stock market prediction. By performing an analysis of news articles related to financial markets, smart algorithms can assess the overall opinions of both amateurs and experts of varying perspectives. Analyzing this data produces a distilled vision of the market’s behavior, leading to more accurate stock data.
Predefined data types can be extracted from internal and external data sources with the use of this technique. The main goal of information extraction systems is object identification. Relevant data is extracted and put together inside of a framework, where it can be properly structured into meaningful and actionable information.
Information extraction can be used to create a comprehensive summary of data coming from various data sets. A company that operates through different branches or has several intertwined departments can drastically improve the efficiency of reporting by implementing solutions to rapidly extract information.
Natural language processing
This textual analysis method combines skills and knowledge from the fields of linguistics, computer science, and artificial intelligence. Natural language processing is concerned with the interactions between computers and human communication.
As machine learning algorithms get a better understanding of how people communicate, they can begin to understand the nuances in human speech. This can help AI draw conclusions out of the data it is analyzing and help identify key concepts imperceptible to less sophisticated analysis tools.
Natural language processing can help recognize main ideas within environments overloaded with information. In financial tech, this can be used to learn more about customers through semantic analysis. Likewise, a natural language processing strategy can provide salespeople with more precise and appealing listings of attributes to promote products.
As a component of machine learning, deep learning teaches a data model to perform content analysis to predict information. Data analyzed using a deep learning strategy must undergo a series of processes. In the financial sector, deep learning can help fix issues related to the complexity and ambiguity of natural human communication.
This organization technique can separate data points into categories. Using specific elements and variables found within text or multimedia, data classification AI reorganizes data into predetermined classes.
Data analytics and classification of customer, employee, and marketplace data can provide essential information to gauge the performance of operations. Since the goal of data classification is to create the most accurate portrayal of data possible, it can be used to create precise financial reports and support the creation of new strategies.
How data mining has revolutionized finance
Machine learning and information technology have caused a substantial change in the financial industry. Data mining has become an important research procedure in every sector of finance, including financial forecasting, banking, and corporate finance.
Applications of data mining in the financial sector
Credit evaluation is a vital procedure for the credit management decisions of banks. In the world of finance, credit scores are used to ensure the credit worthiness of a person, authority, corporation, non-profit organization, or government. A credit rating communicates if an entity is eligible to borrow money and the amount of money it can be trusted to borrow.
To make a proper credit rating assessment, the collection, analysis, and classification of various elements and variables is indispensable. This makes data mining crucial to increase the efficiency of the credit rating process. Data mining helps banks decide whether to accept or reject their clients’ credit requests.
The following data mining techniques can be used to construct credit scoring models:
- Support vector machines: These are supervised learning models, which allow algorithms to understand data based on previously received examples. Support vector machines are common staples of credit scoring models.
- Decision trees: This type of algorithm classifies data into categories, which in turn get arranged into more specific sub-categories. This means that the most general values are found at the top of the documentation while distinct elements can be sub-selected to learn more about them.
- Neural networks: Built using a series of algorithms, neural networks can recognize relationships found within data sources. The process behind neural networks is similar to how the human brain works, and the networks are able to adapt based on the input they receive.
- K-Nearest neighbors: This algorithm functions by making associations to classify data. A K-Nearest neighbors algorithm associates fragments of data with others based on their inherent similarities.
Loan default prediction
Financial institutions require loan default prediction strategies to conduct proper credit risk management. Before loans are granted, data mining models can be used to run predictive analytics and estimate the reliability of a customer. This process helps minimize loan defaults.
Credit is a fundamental component of a functional economy, as it allows businesses to invest and expand. Likewise, consumers make use of credit to make purchases of all kinds of sizes, from their personal shopping to mortgages for buying property. By implementing credit risk management, bank regulators and financial institutions can guarantee that the market is functioning properly and losses are being minimized.
Preventing money laundering and fraud
Money laundering is a fraudulent operation performed by criminals to transform money accumulated through illicit means into clean money, making its criminal origin harder to trace. Financial institutions are considerably disadvantaged due to the practice of money laundering. In addition, fraud is particularly harmful to the financial sector. Credit-card companies lose billions of dollars yearly due to fraudulent activities (source).
Neural networks, support vector machines, and logistic regression models are useful for detecting fraud. The algorithms are able to mine certain patterns to distinguish between fraudulent behavior and genuine user activities.
Stock prediction technology can be used in financial markets to make qualitative decisions. Due to the fluctuations in the stock market, making accurate assessments is complicated. This situation has given researchers a good reason to place significant efforts to better the ability to predict stocks.
The purpose of a time series is to track the movement of specific data points over a period of time, recording information at regular intervals. This collection of organized data provides meaningful patterns found within sequential measurements.
A time series can be used to predict future outcomes based on information gathered about past events. Many factors influence the success of modern time series forecasting, and the data being worked with is often chaotic, random, and non-stationary.
A time series may be performed with one or more of these goals in mind:
- Analysis and interpretation: Describe the impact of time on data.
- Prediction: Estimate future values using known information.
- Control: Make changes to control parameters to fit closer to a goal.
- Adjustment: Observe correlations between errors and estimate variations to resolve them.
- Forecasting: Calculate future stock prices, sales, risk, credit worthiness, or future financial outcomes of a company.
How to get started with data mining in finance
Improving analytics in finance with data mining requires software to put unstructured data in order. Artificial intelligence can collect, extract, store, analyze, and report on data with such a degree of efficiency that doing tasks by hand can’t compare.
Machine learning algorithms can seamlessly perform tasks that would take a human their entire lifetime, in a matter of seconds. Furthermore, thanks to visualization tools, documents like customer records and annual reports can be easily retrieved, displayed, and sent at a moment’s notice.
Semeon offers the best multichannel solutions to gather actionable insights. Thanks to the implementation of software at the forefront of innovation, Semeon’s advanced technology can perform AI-driven market research, enhance the customer experience, and provide a top-tier product experience management.