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This paper describes about different data mining techniques used in financial data analysis. Financial data analysis is used in many financial institutes for accurate analysis of consumer data to find defaulter and valid customer. For this different data mining techniques can be used. The information thus obtained can be used for Decision making. I...
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Citations
... The work showed that data driven models built using Broad default definitions can outperform Narrow default definitions. A brief case study of different data mining techniques like Bayes Classification, bagging algorithm, Random forest, Decision tree, Random Forest and other techniques used in financial data analysis were applied in [23]. The work in [24] checks the applicability of the integrated model on a sample dataset taken from Banks in India. ...
Credit ratings are becoming one of the primary references for financial institutions of the country to assess credit risk in order to accurately predict the likelihood of business failure of an individual or an enterprise. Financial institutions, therefore, depend on credit rating tools and services to help them predict the ability of creditors to meet financial persuasions. Conventional credit rating is broadly categorized into two classes namely: good credit and bad credit. This approach lacks adequate precision to perform credit risk analysis in practice. Related studies have shown that data-driven machine learning algorithms outperform many conventional statistical approaches in solving this type of problem, both in terms of accuracy and efficiency. The purpose of this paper is to construct and validate a credit risk assessment model using Linear Discriminant Analysis as a dimensionality reduction technique to discriminate good creditors from bad ones and identify the best classifier for credit assessment of commercial banks based on real-world data. This will help commercial banks to avoid monetary losses and prevent financial crisis
... Bagging ML models reduce the changes in a black-box model, like in our case, the decision tree, by randomising its development method and constructing an ensemble as a result. Bagging ensemble models are generally good for resolving financial business problems associated with fraud detection or pricing [10]. ...
... The final prediction is the average performance of the sample on these learners. Bagging is also currently being used in the financial industry for deep learning models, including fraud detection, credit risk assessment and option pricing issues [9]. ...
... It has been shown that models constructed from default Wide definition will outperform models created from default Narrow meaning. Bank advance defaults risk assessment, kind of score and distinctive information mining procedures, for example, Choice Tree, Arbitrary Backwoods, Boosting, Bayes arrangement, Sacking calculation and different methods utilized in money related information investigation have been contemplated in [8]. An objective of the examination [9] is to assemble an unmistakable endurance model to test default chance and give trial proof utilizing the Italian financial framework. ...
There are different risks associated with bank loans nowadays, especially for banks to reduce their capital loss. Risk analysis and default assessment becomes crucial. Banks hold huge volumes of customer behavior-related data from which they are unable to reach the verdict when an applicant may or may not be defaulting. Descriptive analytics will help an organization know what has happened in the past, giving you past analysis using the stored data. It is necessary to know the past actions which help them to make statistical decisions using historical data. Data Mining is a capable area of data analysis which aims to extract useful knowledge from a tremendous amount of complex data sets. In this paper, Using predictive models you will help to identify the right customer. You need to evaluate the variables impacting credit risk using historical evidence from the customer of the bank, build actions to reduce the acquisition danger, and analyze the financial value of the project. The final model is used with the test data set for estimation, and the experimental results prove the utility of the designed model.
... They concluded by the projected future work aiming to propose a hybrid method combining the most interesting algorithms. Authors of [16] used different algorithms for Financial data analysis to assist banks to avoid credit defaulters. The paper classifies scoring into four types to work on it: application Scoring, behavioral Scoring, collection Scoring, and fraud detection. ...
... Criminal analysis entails identifying crimes as well as the criminals' connections to them. We get large amounts of criminal datasets from various crimes such as cybercrime, violent crimes, fraud detections, and drug offences [7,8] . In this discipline, data mining is used for applications like as counter-terrorism, criminality matching, and crime trends, among others. ...
... Digitalized banking generates vast amounts of transactional data every day [9]. Data mining is utilized in this filed for applications like financial banking are finding customer loyalty, issues loans and credit cards based on customers previous data, identifying stock market risks from historical data etc. ...
Advancement in technologies has led to the emergence of various new fields. Different fields like science, Engineering, Health, Business are generating, accumulating high amounts of data every day. Data mining is a vital area, which manages and extracts required information from these enormous amounts of data. This paper gives a gist of how data mining is utilized in different fields.
... The forecast of securities exchanges is viewed as a difficult errand of monetary time, arrangement expectation [4]. Securities exchange forecast is trying essentially on account of the unconventionality of the financial exchange with its loud and unstable condition, considering the solid association with various stochastic factors, for example, political occasions, papers just as quarterly and yearly reports [3]. Social media offers a robust outlet for people thoughts and feelings. ...
... Information mining is the way toward extricating the helpful information, examples and patterns from a lot of dataset. Information mining [3] is an interdisciplinary subfield of software engineering with a general target to separate information from an informational collection and change the information into a possible structure for further use. Close to the rough examination step, it in like manner incorporates database and the board perspectives, information pre-handling, model and surmising contemplations. ...
... The various information digging procedures are utilized for exploring money related information examination. Money related information investigation is utilized in numerous budgetary organizations for precise examination of purchaser information to discover defaulter and legitimate customer [3]. ...
Stock market analysis is a common economic activity that has been an attractive topic to research and used in different forms of day-to-day life in order to predict the stock prices. Techniques like major analysis, Statistical investigation, Time arrangement analysis and so on are reliably worthy forecast device. In this paper, Data mining, Machine learning (ML) and Sentiment analysis are techniques used for analyzing public emotions in order predict the future stock prices. The goal of a project is to review totally different techniques to predict stock worth movement victimization the sentiment analysis from social media, data processing. Sentiment classifiers are designed for social media text like product reviews, blog posts, and email corpus messages. In the company’s communication network, information mining calculation is utilized as to mine email correspondence records and verifiable stock costs. Implementing various Machine learning and Classification models such as Deep Neural network, Random forests, Support Vector Machine, the company can successfully implemented a company-specific model capable of predicting stock price movement with efficient accuracy.
... He also suggests that these methods should be utilized to gain a competitive edge. Chawan et al. [9] talk about the significance of data mining and the various kinds of financial data that are available through this process. They detail the different techniques involved, such as clustering, association rules, decision trees, and neural networks. ...
A powerful analytical tool, data mining has become a common method utilized by financial institutions to identify and manage risks. The paper aims to provide a comprehensive analysis of the various techniques used in the mining of data for financial and banking organizations. It also explores the applications of these techniques in the risk assessment process. Risk assessment is a vital part of the operations of financial institutions. It involves identifying, measuring, and mitigating the risks that can affect an institution's financial health. The paper covers the various types of data mining tools that are commonly utilized for assessing financial and banking risks, such as clustering, association rule mining, and classification. It also provides a review of the limitations and challenges associated with using such techniques. The paper also reviews the literature on the various applications of data mining in the financial and banking sectors. These include operational risk assessment, credit risk assessment, and fraud detection. The review provides an overview of each application's aspects, such as data sources, preprocessing techniques, algorithms, and the results. The paper then explores the future directions for the research on the use of data mining for the assessment of financial and banking risks. It covers the latest trends in the field of data analysis, such as the incorporation of artificial intelligence, machine learning, big data analytics, and more
... As a powerful analytical tool, data mining has gained popularity over the years and has been used for solving financial problems or assisting related tasks. Among them, utilizing (big) data to analyze the stock market have gained momentum among data-mining scholars [39], [40]. ...
Grounded on communication theories, we propose to use a data-mining algorithm to detect communication patterns within a company to determine if such patterns may reveal the performance of the company. Specifically, we would like to find out whether or not there exist any association relationships between the frequency of e-mail exchange of the key employees in a company and the performance of the company as reflected in its stock prices. If such relationships do exist, we would also like to know whether or not the company's stock price could be accurately predicted based on the detected relationships. To detect the association relationships, a data-mining algorithm is proposed here to mine e-mail communication records and historical stock prices so that based on the detected relationship, rules that can predict changes in stock prices can be constructed. Using the data-mining algorithm and a set of publicly available Enron e-mail corpus and Enron's stock prices recorded during the same period, we discovered the existence of interesting, statistically significant, association relationships in the data. In addition, we also discovered that these relationships can predict stock price movements with an average accuracy of around 80%. The results confirm the belief that corporate communication has identifiable patterns and such patterns can reveal meaningful information of corporate performance as reflected by such indicators as stock market performance. Given the increasing popularity of social networks, the mining of interesting communication patterns could provide insights into the development of many useful applications in many areas.
