Next Generation of Data Mining in Finance

The escalating threat of fraud in financial institutions is a global issue, with the Malaysian sector being no exception. This study focuses on the implementation and efficacy of Data Mining and Machine Learning methodologies in identifying and mitigating fraudulent activities within these institutions. The paper critically reviews existing literature, bridging the gap between advanced technology application and fraud management. Fraudulent transactions in the financial sector are dynamic and sophisticated, requiring advanced detection techniques. Traditional approaches often struggle to manage this complexity effectively, demonstrating a need for more advanced and adaptive strategies. This is where Data Mining and Machine Learning techniques, renowned for their predictive and analytical prowess, can significantly contribute. Data Mining, the process of uncovering patterns and correlations within large datasets, is a useful tool for detecting anomalies that may suggest fraud. The study assesses various data mining techniques, such as clustering, classification, and association, and explores their application in detecting fraudulent transactions. Findings indicate that these techniques can substantially enhance fraud detection rates while minimizing false positives. Furthermore, Machine Learning, an artificial intelligence subset, has shown immense potential in fraud detection. Its ability to learn from and make decisions based on data makes it a viable solution for fraud detection. This paper explores both supervised and unsupervised learning algorithms and their efficacy in identifying fraud in the Malaysian financial sector. Results suggest that machine learning models, when correctly implemented, can significantly improve the accuracy of fraud detection. The review underscores the importance of employing advanced technologies like Data Mining and Machine Learning to combat financial fraud effectively. It also suggests future research directions, emphasizing the need for context-specific, localized models considering Malaysia's unique socio-economic environment. Moreover, the development of hybrid models, integrating both data mining and machine learning, could offer improved results. In conclusion, this study sets a precedent for further exploration into the application of advanced analytical tools in fraud detection in the Malaysian financial sector. The potential these technologies offer for improving accuracy and adaptability in fraud detection systems is substantial and warrants thorough investigation. Keywords: Fraud Detection, Malaysian Financial Institutions, Data Mining, Machine Learning, Financial Fraud Management

As the number of patients treated in-home are increasing exponentially mainly in countries such as Japan, US A and Europe etc., these people often enter into a critical situation that may require help immediately (e.g. when facing an accident, or becoming depressed). Researches and Advances in computing areas and the Internet of Things (IOT) have provided efficient and cheap equipments including wireless communication and cameras, such as smart phones or embedded devices that enable the deployment of Health S mart Homes (HS H) that can provide medical treatment of patients in their homes. The images captured from these cameras can help nurses or caretakers of patients to provide timely help. The use of such patient images and emotional detection to assist patients and elderly people within home is provided in this article. Internet of Things and Cloud computing can work together, which can address to the Big Data problems. Big data is actually a term used for a huge data set that performs operations like storage of data, analysis, sharing, transfer, predictive analysis; updating etc. data set can grow rapidly. Data sets have different analytics on data that involve process of inspecting, transformation and modeling of data so as to discover new information, and to reach on some particular decision. There are some particular data analysis techniques from which data mining is a popular one which focuses on modeling and discovery of new facts. The discovered knowledge helps in predictive analysis purposes as well as text analysis. Further on data mining is performed on data set which involves discovery of patterns by applying different operations at databases, and using artificial intelligence, machine learning, statics etc. Keywords: Cloud computing, IOT, AI, face recognition, big data, data mining.

This study aimed to identify patients with the highest risk of periodontal disease (PD), and to provide recommendations for the effective use and application of data mining (DM) techniques when establishing evidence-based dental-care policies for vulnerable groups at a high risk of PD. This study used the SEMMA (Sample, Explore, Modify, Model, and Assess) methodology to construct DM models based on data acquired from the fifth and sixth Korea National Health and Nutrition Examination Surveys (2000-2015). We analyzed the sociodemographic and comorbidity variables that influence PD by applying the popular DM techniques of decision-tree, neural-network, and regression models, and also attempted to improve the predictive power and reliability by comparing the results obtained by these three models. Our comparisons of the three DM algorithms confirmed that the average squared error, misclassification rate, receiver operating characteristic index, Gini coefficient, and Kolmogorov-Smirnov test results were the most appropriate for the decision-tree model. The analysis of the decision-tree model revealed that age and smoking status exert major effects on the risk of PD, and that stress and education level exert effects in rural areas, whereas education level, sex, hyperlipidemia, and alcohol intake exert effects in urban areas. We demonstrated that the decision-tree model is an effective DM technique for identifying the complex risk factors for PD. These results are expected to be helpful in improving the equality and efficacy of dental-care policies for vulnerable groups at a high risk of PD.

The online version contains supplementary material available at 10.1007/s40200-021-00884-2.

In general, obesity is calculated using the BMI, which is found to have limitations of use when we insert only one type of non-quantitative data as a crisp value, which is commonly used, or even create an obesity forecasting model using data mining techniques. If there is an input of weight and height data as a forecasting factor in various types of data, such as crisp value, estimated value and Fuzzy Linguistic Term value, etc. The algorithm will not possibly work under the data mining technique with such input data. This research presented the solution for this problem in order to enable data mining techniques under limitations of inserted data, including crisp value, estimated value and fuzzy linguistic term value, for forecasting obesity. The researchers created a fuzzy database system in the form of Conceptual Meta Schema to support fuzzy attributed data storage. In addition, there were 3 types of fuzzy attribute matching as follows; Type 1 was matching crisp value ​​with fuzzy linguistic term value, Type 2 was matching estimated value with fuzzy linguistic term value, and Type 3 was matching fuzzy linguistic term value with fuzzy linguistic term value, respectively. The attribute node from these fuzzy attributes would be selected to be used in the work of data mining techniques. Therefore, in this research, the researchers presented and compared the fuzzy model of working under the algorithm of data mining techniques in the form called fuzzy neural network and fuzzy decision tree techniques. The research result, it was found that the appropriate models for predicting obesity were fuzzy neural network algorithm with the neural network structure being 31-3-3, momentum at 0.2, learning rate at 0.3, under dividing data to test with cross-validation folds = 10 yielded accuracy value, precision value, recall value and f-measure at 84.3%, 82.7%, 84.3%, and 82.8%, respectively. Keywords: Obesity, Fuzzy Neural Net, Fuzzy Decision Tree, Fuzzy Attribute Matching, Forecasting Model

The trends of data mining on healthcare data for improving medical services have increased because of the electronic healthcare record(EHR) system, which collects a massive amount of data on a daily basis. In the current scenario, hospital maintains its EHR system and stores the detailed information of patients. Data mining for healthcare improvement requires the data from all the EHR systems located at a different location to be stored at the central data mining server. Collection of healthcare data at some untrusted central data mining server raises privacy threats. Healthcare data contains patients’ private information and sharing this information for data mining creates privacy issues. Most of the previous research either focused on k-anonymity technique which causes information loss and decreases data mining accuracy or privacy preserving data mining which is focused on only specific data mining technique. We adopt source anonymous technique as privacy preserving scheme and present a novel scheme for healthcare data collection and mining in this paper. Our scheme collects data from all EHR systems without any information loss and stores at a single central data mining server, also ensuring privacy is preserved. Central data mining server helps to analyze the collected data with different data mining techniques (Association rule mining, Classification, Clustering, etc.) without the involvement of EHR systems. Our scheme is collusion resilient against central data mining server and EHR systems. Theoretical and experimental analysis show the efficiency of our scheme in terms of computation and communication cost. The experimental results using Heart disease dataset show the advantage to EHR systems using the proposed approach in terms of disease prediction accuracy.

Clustering is an important technique in data mining. In this paper, we introduce a new clustering algorithm. This algorithm, based on granular computing, constructs a hypergraph (simplicial complex) by the hypergraph bisection algorithm. It will discover the similarities and associations among documents. In some experiments on Web data, the proposed algorithm is used; the results are quite satisfactory. © 2009 Wiley Periodicals, Inc.

Replication is a widely used method for achieving high availability in database systems. The data replication approach can be used for solutions such as sharing data among remote offices, sharing data among dispersed users, make server data more accessible, distribute solution updates, back up data, provide internet or intranet replication. The main aim of this research is to provide a better technique for solving the drawbacks that currently exist in the literary works of data replica environment. Here, we intend to propose data replication system based on data mining techniques. That is in proposed method FDDRA (data mining based dynamic replication algorithm), we will use a data mining with combination of replication algorithm and data grid policy for data replication in the data mining environment through monitoring all data process. The data replication will be done by identifying the frequently used data patterns in the large database of a node. This will be done by Apriorialgorithm. The system availability and replica will be measured and identifying the location in which the replicated data will be stored. The result shows the efficiency of our algorithm in terms latency time and shortest path of nodes.

The objective of data mining is to find the useful information from the huge amounts of data. Many researchers have been proposed the different algorithms to find the useful patterns but one of the most important drawbacks they have found that data mining techniques works for single data table. This technique is known as traditional data mining technique. In this era almost all data available in the form of relational database which have multiple tables and their relationships. The new data mining technique has emerged as an alternative for describing structured data such as relational data base, since they allow applying data mining in multiple tables directly, which is known as Multi Relational data mining. To avoid the more number joining operations as well as the semantic losses the researchers bound to use Multi Relational Data Mining approaches. In this paper MRDM focuses multi relational association rule, Multi relational decision tree construction, Inductive logic program (ILP) as well three statistical approaches. We emphasize each MR-Classification approach as well as their characteristics, comparisons as per the statistical values and finally found the most research challenging problems in MRDM.

Data mining is the process of posing queries and extracting patterns, often previously unknown from large quantities of data using pattern matching or other reasoning techniques. Data mining has many applications in security including for national security as well as for cyber security. The threats to national security include attacking buildings, destroying critical infrastructures such as power grids and telecommunication systems. Data mining techniques are being investigated to find out who the suspicious people are and who is capable of carrying out terrorist activities. Cyber security is involved with protecting the computer and network systems against corruption due to Trojan horses, worms and viruses. Data mining is also being applied to provide solutions such as intrusion detection and auditing. The first part of the presentation will discuss my joint research with Prof. Latifur Khan and our students at the University of Texas at Dallas on data mining for cyber security applications For example; anomaly detection techniques could be used to detect unusual patterns and behaviors. Link analysis may be used to trace the viruses to the perpetrators. Classification may be used to group various cyber attacks and then use the profiles to detect an attack when it occurs. Prediction may be used to determine potential future attacks depending in a way on information learnt about terrorists through email and phone conversations. Data mining is also being applied for intrusion detection and auditing. Other applications include data mining for malicious code detection such as worm detection and managing firewall policies. This second part of the presentation will discuss the various types of threats to national security and describe data mining techniques for handling such threats. Threats include non real-time threats and real-time threats. We need to understand the types of threats and also gather good data to carry out mining and obtain useful results. The challenge is to reduce false positives and false negatives. The third part of the presentation will discuss some of the research challenges. We need some form of real-time data mining, that is, the results have to be generated in real-time, we also need to build models in real-time for realtime intrusion detection. Data mining is also being applied for credit card fraud detection and biometrics related applications. While some progress has been made on topics such as stream data mining, there is still a lot of work to be done here. Another challenge is to mine multimedia data including surveillance video. Finally, we need to maintain the privacy of individuals. Much research has been carried out on privacy preserving data mining. In summary, the presentation will provide an overview of data mining, the various types of threats and then discuss the applications of data mining for malicious code detection, cyber security and national security. Then we will discuss the consequences to privacy.

Data mining is the process of posing queries and extracting patterns, often previously unknown from large quantities of data using pattern matching or other reasoning techniques. Data mining has many applications in security including for national security as well as for cyber security. The threats to national security include attacking buildings, destroying critical infrastructures such as power grids and telecommunication systems. Data mining techniques are being investigated to find out who the suspicious people are and who is capable of carrying out terrorist activities. Cyber security is involved with protecting the computer and network systems against corruption due to Trojan horses, worms and viruses. Data mining is also being applied to provide solutions such as intrusion detection and auditing. The first part of the presentation will discuss my joint research with Prof. Latifur Khan and our students at the University of Texas at Dallas on data mining for cyber security applications For example; anomaly detection techniques could be used to detect unusual patterns and behaviors. Link analysis may be used to trace the viruses to the perpetrators. Classification may be used to group various cyber attacks and then use the profiles to detect an attack when it occurs. Prediction may be used to determine potential future attacks depending in a way on information learnt about terrorists through email and phone conversations. Data mining is also being applied for intrusion detection and auditing. Other applications include data mining for malicious code detection such as worm detection and managing firewall policies. This second part of the presentation will discuss the various types of threats to national security and describe data mining techniques for handling such threats. Threats include non real-time threats and real-time threats. We need to understand the types of threats and also gather good data to carry out mining and obtain useful results. The challenge is to reduce false positives and false negatives. The third part of the presentation will discuss some of the research challenges. We need some form of real-time data mining, that is, the results have to be generated in real-time, we also need to build models in real-time for realtime intrusion detection. Data mining is also being applied for credit card fraud detection and biometrics related applications. While some progress has been made on topics such as stream data mining, there is still a lot of work to be done here. Another challenge is to mine multimedia data including surveillance video. Finally, we need to maintain the privacy of individuals. Much research has been carried out on privacy preserving data mining. In summary, the presentation will provide an overview of data mining, the various types of threats and then discuss the applications of data mining for malicious code detection, cyber security and national security. Then we will discuss the consequences to privacy.

Metabolic syndrome (MS) is a condition associated with metabolic abnormalities that are characterized by central obesity (e.g. waist circumference or body mass index), hypertension (e.g. systolic or diastolic blood pressure), hyperglycemia (e.g. fasting plasma glucose) and dyslipidemia (e.g. triglyceride and high-density lipoprotein cholesterol). It is also associated with the development of diabetes mellitus (DM) type 2 and cardiovascular disease (CVD). Therefore, the rapid identification of MS is required to prevent the occurrence of such diseases. Herein, we review the utilization of data mining approaches for MS identification. Furthermore, the concept of quantitative population-health relationship (QPHR) is also presented, which can be defined as the elucidation/understanding of the relationship that exists between health parameters and health status. The QPHR modeling uses data mining techniques such as artificial neural network (ANN), support vector machine (SVM), principal component analysis (PCA), decision tree (DT), random forest (RF) and association analysis (AA) for modeling and construction of predictive models for MS characterization. The DT method has been found to outperform other data mining techniques in the identification of MS status. Moreover, the AA technique has proved useful in the discovery of in-depth as well as frequently occurring health parameters that can be used for revealing the rules of MS development. This review presents the potential benefits on the applications of data mining as a rapid identification tool for classifying MS.

Cloud computing is seen as an emerging technology for big data mining and analytics. Cloud computing can provide data mining results in the form of a Software As a Service (SAS). Both performance and quality of mining are fundamentals criteria for the use of a data mining application provided by a Cloud computing environment. In this paper, we propose a Cloud computing framework, which is responsible to distribute and schedule a Cluster-Based data mining application and its data set. The main goal of our proposed framework for scheduling of Big Data Mining (FSBD) is to decrease the overall execution time of the application with minimum loss in mining quality. We consider the Cluster-based data mining technique as a pilot application for our framework. The results show an important speedup with a minimum loss in quality of mining. We obtained a ratio of 2 of the normalized actual makespan vis-a-vis the ideal makespan. The quality of mining scales well with the number of clusters and the increasing size of the dataset. The results are promising, encouraging the adoption of the framework by Cloud providers.

Increasing the products service life and reducing the number of service visits are becoming more and more top priorities for Original Equipment Manufacturer companies (OEM’s). Condition-based maintenance is often proposed as a solution to reach this goal. However, this latter, is often hampered by the lack of the right information that gives a good indication of the health of the equipment. Furthermore, the processing power needed to compute this information is often not afforded by machine’s processor. In this paper, a remote platform which connects the OEM’s to the customer’s premises is described, allowing thus a local computation of available information. Two approaches are then combined to process the optimal maintenance time. First, data mining techniques and reliability estimation are applied to historical databases of machines running in the field in order to extract the relevant features together with their associated thresholds. Second, prediction algorithm is applied to the selected features in order to estimate the optimal time to preventively perform a maintenance action. The proposed method has been applied to a database of more than 2000 copy machines running in the field and proved to identify easily the relevant features to be forecasted and to offer an accurate prediction of the maintenance action.

Diabetes is the common chronic disease and a major health challenge in all population. Gestational diabetes mellitus (GDM) is a type of diabetes developed in women at the time of pregnancy. We present a Data mining (DM) approach to identify the risk factors of Gestational diabetes mellitus (GDM) using different data mining techniques. Dataset used for analysis contains the details of the pregnant women admitted the local hospital of Mysuru, India. The data mining techniques used are k-means clustering, J48 Decision Tree, Random-Forest and Naive-Bayes classifier. Classification accuracy is enhanced by using feature subset selection wrapper approach. Data imbalanced problem is handled by using Synthetic Minority Over-sampling Technique (SMOTE). The performances of the algorithms have been measured and compared in terms of Accuracy.