Introduction
Data Mining also referred to as Knowledge Discovery in Databases or KDD is a procedure of finding patterns in data warehouses and an extensive set of data. Several techniques like outlier analysis, regression analysis, classification, clustering, and association are linked to data to find beneficial outcomes. Such techniques utilize backend and software algorithms that dissect the data and reveal patterns.
A few of the well-known data mining methods are Bayes theorem analysis, decision tree analysis, and frequent item-set mining. Some paid and open-source tools for data mining are available in the software market like Rapid Miner, Weka, and Orange data mining tools.
In Finance – Data Mining Examples
The process of data mining begins with giving a fixed data input to tools which make use of algorithms and statistics to present patterns and reports. Results can be seen with these tools which can be comprehended and applied further to carry out business changes and improvements.
The finance industry comprises insurance companies, banks, and investment companies. These enterprises gather a big amount of data. Such data is at most times complete, dependable, and of top quality and requires a systematic and planned data analysis.
For the storage of financial data, data warehouses that store data in data cubes are built. For data analysis, advanced concepts of data cubes are used. Methods such as outlier analysis, clustering, and characterization are made use of in financial data mining and analysis. Some instances where data mining is used are mentioned below.
1. Targeted and Focused Marketing with Data Mining
Classification, grouping, and clustering data mining procedures will aid in searching for factors that affect the consumer’s decisions in banking. Behavioral consumer identification which is similar will support targeted marketing.
2. Loan Payment Foreseeing
Data mining procedures such as attribute ranking and attribute selection will dissect consumer payment history and choose essential factors like credit history, loan term, payment to income ratio, etc. The results will assist the banks in deciding the loan granting policy and also issue loans to the consumers in the form of per factor analysis. Managing financial risk in banks is an important concern today.
3. Identify Financial Crimes with Data Mining
Bank data is sourced from different cities and bank locations. Several data analysis tools are used to study and identify uncommon trends such as big value transactions. Clustering tools, data visualization tools, and outlier analysis tools are utilized to detect the relationships and action patterns. Computational finance is useful and also used in data mining today.
A great amount of data is released in virtual transactions, so the capacity to identify correct information at the right time can be the difference between losing or gaining millions:
- Enhance customer loyalty by analyzing and collecting customer behavior data
- Assist banks in predicting consumer behavior and launching important products and services.
- Find hidden links between several financial indicators to identify suspicious activity with high risk.
- Improve diligence to speed alerts and facilitate real-time decision-making activity
- Spot fraudulent or non-fraudulent actions by gathering historical data and converting it into verified and beneficial information. Financial fraud is widely prevalent in online transactions today.
4. Predictions and Assumptions of the Stock Market using Data Mining
Investors aim to maximize profits by purchasing and selling the investments at the correct time. Research has shown that expansion rates of basic fundamental characteristics like capital investment, revenues, earnings per share, market share, and debt are used to foretell future returns of different companies. Network modeling which comprises back-propagation or BP networks, recurrent neural networks, and probabilistic neural networks has been the most highly utilized data mining method in the prediction of the stock market.
5. Portfolio Management
This is a significant topic in the investment sector. It is how customers select which securities to keep in their investment portfolios and distribution of money across different asset categories like domestic vs overseas securities and equities versus bonds. The basic objective is to choose a risk assets collection from which to create a portfolio to maximize return and minimize risk to get a fixed return while lowering risk.
6. Foreign Exchange Market
International financial competitiveness induces countries to free their markets and garner foreign investment. Rates of foreign exchange have become highly volatile as the activities of companies have become multinational. Exchange rates is fluctuating constantly and to purchase products and services made in a different country, firstly one has to purchase the currency of that particular country.
Conclusion
The usage of data mining in finance has increased in the era of Big Data. With their automation and power, algorithms can manage huge amounts of data and gather value. Data mining methods are applied in several financial applications like real estate services, payment prediction, loan risk analysis, and mortgage scoring.
Besides the fact data mining has been widely used in the finance sector, there are still many concerns and challenges which must be correctly addressed to attain effective financial management for institutions and individuals. The complexity of performance vitals, privacy, accuracy, huge datasets, regulator training, and market impacts are part of the issues. Techniques of data mining are changing and evolving, have shown marked improvement in financial applications, and will continue to flourish in the growing knowledge-based economy.
