Realtime Fraud Detection in the Banking Sector Using Data Mining Techniques/Algorithm (original) (raw)
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A Model Based on Clustering and Association Rules for Detection of Fraud in Banking Transactions
World Congress on Electrical Engineering and Computer Systems and Science, 2018
In recent years, fraud in banking transactions has turned into a serious problem for which different supervised and unsupervised algorithms have been suggested. In this paper, a semi-supervised combined model based on clustering algorithms and association rule mining is devised in order to detect frauds and suspicious behaviors in banking transactions. To this end original and non-fraud transaction data of the customers is collected for the analysis. Next, repetitive patterns of customer behaviors are extracted through association rules and used as normal rules so that any new transaction must conform to at least one of these rules. In behavior analysis component, a fuzzy clustering algorithm is employed to extract the normal behavior patterns of customers. Abnormal transactions belong to none of these clusters and will be recognized as high risk. The final understanding of a transaction will be gained through combining the results of association rules and clustering patterns. Findings suggest that the employment of both rule-based and clustering-based components leads to the detection of more frauds while fewer alarms will go off.
Data Mining Techniques and its Applications in Banking Sector
Data mining is becoming strategically important area for many business organizations including banking sector. It is a process of analyzing the data from various perspectives and summarizing it into valuable information. Data mining assists the banks to look for hidden pattern in a group and discover unknown relationship in the data. Today, customers have so many opinions with regard to where they can choose to do their business. Early data analysis techniques were oriented toward extracting quantitative and statistical data characteristics. These techniques facilitate useful data interpretations for the banking sector to avoid customer attrition. Customer retention is the most important factor to be analyzed in today's competitive business environment. And also fraud is a significant problem in banking sector. Detecting and preventing fraud is difficult, because fraudsters develop new schemes all the time, and the schemes grow more and more sophisticated to elude easy detection. This paper analyzes the data mining techniques and its applications in banking sector like fraud prevention and detection, customer retention, marketing and risk management.
Data mining is a sum of process to find anomalies, patterns, correlations which can assist banks to look for hidden patterns in a group and discover unknown relationship in the data. In this competitive world, governments, private companies, large organizations and all businesses, predict their future plans using various methods of data mining. Banks are an integral part of a country's economy, contributing to both people and governments. In case of money transaction bank is the most essential media in our country. In this circumstance, some vested peoples gratify their evil task. These individual acts make a big issue for our country's economy. In this paper, we have discussed about the comparative study on several data mining classification techniques that are generally used to classify suspicious transactions, included Naïve Bayes, MLP (is the particular feed forward method of ANN), sequential minimal optimization (SMO) and decision tree based J48 & random forest algorithm. we have found the random forest algorithm performance is better than others to classify banking transactions.
Data Mining Applications In Banking Sector For Effective Service Delivery
Data Mining plays important roles in many organizations especially the customer service oriented establishments like banks. It is helpful in scrutinizing the collected data and delivering it into an understandable pattern. In the present scenario, banking is an emerging sector where large volumes of electronic data are being maintained. The important task in banking is handling huge transactional data and making decisions regarding customer retention, fraud detection and prevention, risk and marketing management. But making decisions by manual is time consuming and error prone. To process these data in an effective manner, data mining techniques and methods are pertinent. By using these techniques several interesting patterns and knowledge base can be retrieved. These techniques facilitate useful data interpretations for the banking sector to avoid customer attrition or churns. Customer retention is the most important factor to be analyzed in today's competitive business environment. And also fraud is a significant problem in banking sector. Detecting and preventing fraud is difficult, because fraudsters develop new schemes all the time, and the schemes grow more and more sophisticated to elude easy detection. This paper analyzes the data mining techniques and its applications in banking sector like fraud prevention and detection, customer retention, marketing and risk management and business performance.
APPLICATION OF DATA MINING IN BANKING AND FINANCE
Presently, banks and other financial institutions have to preserve the huge electronic data through fixation of reliable information in the data warehouses. It is illogical to detect the trend or pattern buy a human being so as to know the required information available from the huge data sources. The big giants in this field are very much fast in diagnosing this concept, resultantly the software market worldwide for data mining is expected to surpass ten billion United states dollar. This message is proposed for the financial institutions who would like to know the probable applications of data mining so as to increase their core business performance. In this paper, the wide application of data mining techniques say fraud detection, risk management, client profiling and consumer care has been discussed, where artificial intelligence techniques may be applied by banks to work efficiently.
Data Mining in Banking and Its Applications-A Review
Journal of Computer Science
Banking systems collect huge amounts of data on day to day basis, be it customer information, transaction details, risk profiles, credit card details, limit and collateral details, compliance and Anti Money Laundering (AML) related information, trade finance data, SWIFT and telex messages. Thousands of decisions are taken in a bank daily. These decisions include credit decisions, default decisions, relationship start up, investment decisions, AML and Illegal financing related. One needs to depend on various reports and drill down tools provided by the banking systems to arrive at these critical decisions. But this is a manual process and is error prone and time consuming due to large volume of transactional and historical data. Interesting patterns and knowledge can be mined from this huge volume of data that in turn can be used for this decision making process. This article explores and reviews various data mining techniques that can be applied in banking areas. It provides an overview of data mining techniques and procedures. It also provides an insight into how these techniques can be used in banking areas to make the decision making process easier and productive.
In today"s globalization and cut throat competition the banks are struggling to gain a competitive edge over each other. Apart from execution of business processes, the creation of knowledge base and its utilization for the benefit of the bank is becoming a strategy tool to compete. In recent years the ability to generate, capture and store data has increased enormously. The information contained in this data can be very important. The wide availability of huge amounts of data and the need for transforming such data into knowledge encourage IT industry to use data mining. The banking industry around the world has undergone a tremendous change in the way business is conducted. The banking industry has started realizing the need of the techniques like data mining which can help them to compete in the market. Leading banks are using Data Mining (DM) tools for customer segmentation and profitability, credit scoring and approval, predicting payment default, marketing, detecting fraudulent transactions, etc. This paper provides an overview of the concept of DM and highlights the applications of data mining to enhance the performance of some of the core business processes in banking industry.
A multi-algorithm data mining classification approach for bank fraudulent transactions
African Journal of Mathematics and Computer Science Research, 2017
This paper proposes a multi-algorithm strategy for card fraud detection. Various techniques in data mining have been used to develop fraud detection models; it was however observed that existing works produced outputs with false positives that wrongly classified legitimate transactions as fraudulent in some instances; thereby raising false alarms, mismanaged resources and forfeit customers' trust. This work was therefore designed to develop a hybridized model using an existing technique Density-Based Spatial Clustering of Applications with Noise (DBSCAN) combined with a rule base algorithm to reinforce the accuracy of the existing technique. The DBSCAN algorithm combined with Rule base algorithm gave a better card fraud prediction accuracy over the existing DBSCAN algorithm when used alone.
Survey on Fraud Detection in banking using Novel Strategy
2020
Now a day’s online payment gaining popularity because of easy and convenience use of ecommerce. It became very easy mode of payment. People choose online payment and e-shopping; because of time convenience, transport convenience, etc. As the result of huge amount of e-commerce use, there is a vast increment in credit card fraud also. Machine Learning has been successfully applied to finance databases to automate analysis of huge volumes of complex data. Machine Learning has also played a salient role in the detection of credit card fraud in online transactions. Fraud detection in credit card is a big problem, it becomes challenging due to two major reasons–first, the profiles of normal and fraudulent behaviors change frequently and secondly due to reason that credit card fraud data sets are highly skewed. KeywordsData Analysis, Fraud in credit card, naïve bayes, Machine Learning, Security.
Data Mining. Concepts And Applications In Banking Sector
2018
The concept of banking refers to the multitude of services and products that commercial banks offer to clients and include besides transactional accounts both passive and active products. Due to the increased competitiveness in banking, the relationship between the bank and the client has become an essential factor for the strategy in order to increase customer satisfaction. Currently the banking system is able to store impressive amounts of data that they collect daily, from customer data and transaction details to data on their transactional or risk profile. The process through which large amounts of data are analyzed, extracted, identified and the information obtained using mathematical and statistical models are interpreted is known as data mining. The discovery of knowledge from data involves identifying some models, some patterns with which certain events or possible risks are anticipated. This process helps banks to develop strategies in areas such as customer retention and l...