Business Intelligence Technique As Well As Huge Information Analytics Pdf – The excitement and misunderstanding of such concepts, which quite often occurs with new philosophies in the field of information systems, can be quite problematic for academics and industry professionals and drag them in an unwanted direction beyond their attention; as a result of consuming additional resources, creating unwanted or biased products, and learning from conclusions based on false assumptions.
To avoid such questions and problems, I consulted more than 50 academic sources (journal articles and conference papers) and created a simple framework (figure below) to distinguish elements from a cluster of fuzzy concepts related to big data analysis. Concepts focused in this framework are: business intelligence, big data, data analytics, and knowledge discovery.
Business Intelligence Technique As Well As Huge Information Analytics Pdf
As shown in the figure above, we see knowledge discovery as an overarching concept that, in addition to other methods, involves analyzing data to discover or derive new knowledge. In addition, we view Data Analytics as a larger entity that encompasses various disciplines, including Big Data Analytics and Business Intelligence.
How Can Enterprises Get Started With Business Intelligence Automation?
The concept of Big Data is usually considered as part of Big Data Analytics. Considering the intentions, goals and underlying business philosophies, we see big data analytics and business intelligence on the same level. However, taking into account the technical structure, related software applications and data, we consider big data and business intelligence as concepts of the same level.
We also see a major difference between BI and Big Data in the focus on data. Big data encompasses unstructured, semi-structured and structured data, however, the focus is on unstructured data, while BI focuses on structured data. While BI requires DW and/or data marts to support reporting, Big Data can work with DW, but it doesn’t have to. In reports based on a BI system, there is a requirement to have structured master data and transactions. For example, in order to use or analyze sales transaction data in a meaningful and understandable way, we must have master data that describes the properties of the sales (such as store, location, or product description). The concept of Big Data does not fall under these requirements. For example, we do not need structured data to analyze the content of relevant e-mails or to analyze the appeals of individuals submitted to public administration bodies.
Update: July 28, 2017 This post is based on my research paper published in Lecture Notes in Business Information Processing, Volume 285. Springer. With the advancement of digital technology, the banking industry is getting huge benefits. It provides a data warehouse mechanism by storing data in branches and increases the number of access points to bank accounts. The banking system becomes stable in technically and customer-oriented transactions that are made on the Internet, at ATMs and machines for depositing cash and checks and electronic bank transfers. All transactions and related data are saved. Thus, banks today maintain large electronic data repositories as bank electronic storage. Data is constantly increasing in terms of size and dimension. Using big data analysis techniques, this huge data will be transformed into the most profitable asset of the banks. This data consists of interesting patterns and useful knowledge. Because of this, the banking industry has a huge opportunity to apply data mining techniques to individually identify such patterns and knowledge to assist in critical decision-making processes such as risk management, marketing, money laundering and fraud detection. This article discusses how the banking industry uses data mining techniques to effectively detect fraud.
Every year, banks lose millions of dollars due to various bank frauds. By detecting these frauds, banks can reduce or prevent losses. Fraud detection can be defined as the process of recognizing fraud separately from credible actions or transactions. In other words, fraud detection is a process that divides all transactions into two classes, such as legitimate and fraudulent. Investigating credit card transactions and financial statements are some of the most important areas of the banking industry that can benefit from fraud detection. Banks make loan decisions using financial statements provided by customers. Sometimes these statements provided may overstate profits, sales, and assets, or may understate liabilities and losses. These claims may have been verified, but the fraud I mentioned above is not easily detected by normal verification procedures.
Big Data Analysis, Business Intelligence, Technology Solutions Concept On Virtual Screen Stock Photo
Most banks are starting to use data mining techniques that they recognize as legitimate and fraudulent. Fraud detection is one of the most important and popular areas where data mining can be used to advantage. Fraud detection is becoming an increasingly important issue for many banks in the industry, and most banks are using data mining techniques to detect and report more fraud. Financial institutions have created two mechanisms to detect and recognize fraud. First, they use a data warehouse maintained by a third party and identify specific fraud information using data mining software. As a result, the bank can cross-reference the identified information with its database to detect signs of internal problems. The next approach is that the identification of fraud information is strictly based on the bank’s internal information. Most banks in the industry use both approaches partially as a hybrid. currently, “falcons fraud score” can be known as a system that successfully detects fraud. It is used by nine of the ten most popular banks in the industry. A data mining program also helps the banking industry focus on customer data analysis procedures to uncover information about behavior that could lead to fraud.
With the development of banking services, the banking industry had to face many losses due to various frauds taking place in the bank. According to the annual reports, credit card fraud (online/ATM) and financial statement fraud are the most significant areas, leading to losses of millions of dollars per year (prior to the implementation of a suitable business intelligence solution). As a solution, Banks effectively use business intelligence applications such as data warehousing, big data analytics and data mining in the fraud detection process to reduce fraud and losses.
The bank properly maintains and organizes data through a data warehouse program. This organized data is analyzed and identified spending patterns, credit information, behavior and other relevant information and clustered using big data analytical techniques. On the other hand, using data mining techniques, they understand the customer in terms of behavior, investment choices and customer demographics separately from big data. This customer knowledge is used to help customers and generate better profits. In addition, banks use this information to make better decisions about fraud detection, customer relationship management, and more. Most finance departments have improved fraud detection, fraud case efficiency and false positives through the use of data analytics. The top 10 banks adequately maintain fraud data for the industry in which the fraud occurred. According to this data, banks reported two categories of fraud, namely transaction-based fraud and financial statement-based fraud.
Sometimes stored transaction history and customer demographics provide information to defraud the bank. Data mining technologies help banks analyze these transactions individually and identify patterns that lead to fraud. Banks are more focused on fraud detection. Thus, it is important to find which transactions are not transactions that the user would make. Therefore, it is necessary to determine which transactions do not fit into a certain category or which transactions do not meet the standard, and which user actions correspond to their natural behavior and which do not correspond to their natural behavior automatically and intelligently. . Using appropriate data mining methods, they formally detect suspicious activity in the data. The system examines the customer’s transaction history to determine the previous locations where the customer completed their transactions using cluster analysis in data mining and compares these locations to the current location of transactions using anomaly detection techniques. If a transaction’s current location does not match their previous transactions, it will be flagged as a suspicious transaction. Not only did it have this, but it also studied user behavior patterns at the same time, organizing related transactions together using the technique of cluster analysis. To check for abnormal transactions, current transactions are compared to typical behavior patterns of a specified user using anomaly detection techniques. In addition, if it is a cyber credit card transaction, the system will scrutinize the websites of brick-and-mortar merchants where the cardholder regularly makes payments for services or goods, the geographic information where the cardholder has shipped the goods, email address, and phone number. which he regularly uses for his transactions. Current transactions that do not match any of these defined patterns and owner standards will be automatically and intelligently identified as untrusted transactions and appropriate action will be taken. This is a normal transaction detection procedure
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