Fraud detection for online banking for scalable and distributed data

Online fraud causes billions of dollars in losses for banks. Therefore, online banking fraud detection is an important field of study. However, there are many challenges in conducting research in fraud detection. One of the constraints is due to unavailability of bank datasets for research or the required characteristics of the attributes of the data are not available. Numeric data usually provides better performance for machine learning algorithms. Most transaction data however have categorical, or nominal features as well. Moreover, some platforms such as Apache Spark only recognizes numeric data. So, there is a need to use techniques e.g. One-hot encoding (OHE) to transform categorical features to numerical features, however OHE has challenges including the sparseness of transformed data and that the distinct values of an attribute are not always known in advance. Efficient feature engineering can improve the algorithm’s performance but usually requires detailed domain knowledge to identify correct features. Techniques like Ripple Down Rules (RDR) are suitable for fraud detection because of their low maintenance and incremental learning features. However, high classification accuracy on mixed datasets, especially for scalable data is challenging. Evaluation of RDR on distributed platforms is also challenging as it is not available on these platforms. The thesis proposes the following solutions to these challenges: • We developed a technique Highly Correlated Rule Based Uniformly Distribution (HCRUD) to generate highly correlated rule-based uniformly-distributed synthetic data. • We developed a technique One-hot Encoded Extended Compact (OHE-EC) to transform categorical features to numeric features by compacting sparse-data even if all distinct values are unknown. • We developed a technique Feature Engineering and Compact Unified Expressions (FECUE) to improve model efficiency through feature engineering where the domain of the data is not known in advance. • A Unified Expression RDR fraud deduction technique (UE-RDR) for Big data has been proposed and evaluated on the Spark platform. Empirical tests were executed on multi-node Hadoop cluster using well-known classifiers on bank data, synthetic bank datasets and publicly available datasets from UCI repository. These evaluations demonstrated substantial improvements in terms of classification accuracy, ruleset compactness and execution speed.Doctor of Philosoph

The Tessera D&R computational environment: Designed experiments for R-Hadoop performance and Bitcoin analysis

D&R is a statistical framework for the analysis of large complex data that enables feasible and practical analysis of large complex data. The analyst selects a division method to divide the data into subsets, applies an analytic method of the analysis to each subset independently with no communication among subsets, selects a recombination method that is applied to the outputs across subsets to form a result of the analytic method for the entire data. The computational tasking of D&R is nearly embarrassingly parallel, so D&R can readily exploit distributed, parallel computational environments, such as our D&R computational environment, Tessera.^ In the first part of this dissertation, I present a study of the performance of the Tessera D&R computational environment through designed experiments. ^ The base of the D&R computational environment is RHIPE, the R and Hadoop Integrated Programming Environment. R is a widely used interactive language for data analysis. Hadoop is a distributed, parallel computational environment consisting of a distributed file system (HDFS) and distributed compute engine (MapReduce). RHIPE is a merger of R and Hadoop.^ The D&R framework enables a fast embarrassingly parallel computation on a cluster for large complex data that can lead to a small computational elapsed times for the applications analytic methods to all of the data. However, the time depends on many factors. The system we study is very complex and the effects of factors are complex. There are interactions, but not well understood. So we run a full factorial experiment with replicates to enable an understanding. ^ In the second part of this dissertation, I present an analysis of the Bitcoin transaction data utilizing the Tessera D&R computational environment. ^ Bitcoin is a de-centralized digital currency system. There is no central authority in the Bitcoin system to issue new money, or validate the transfer of money; both of these tasks are accomplished through the joint work of participants in the Bitcoin network. In the past two years, the Bitcoin system has become very popular, mostly due to its ease of use and embedded anonymity in the system.^ The ease of use of Bitcoin is straightforward. The anonymity of the Bitcoin system, on the other hand, is rather debatable and has drawn much attention in its user community as well as the research community. We admit that a certain level of anonymity exists in the Bitcoin system, but it might not be as invulnerable as one would hope. For one thing, the entire history of Bitcoin transactions is publicly available, which provides an opportunity for passive analysis of Bitcoin usage such as ours.^ I present here a study of the general statistical properties of the usage of Bitcoin transactions and the usage of Bitcoin addresses. We have also built profiles for a few groups of popular addresses among which the addresses share similar behavior. Furthermore, we provide a passive analysis of the anonymity of Bitcoin system by proposing a classification model to identify payment and change in majority of the Bitcoin transactions

Methods to Improve Applicability and Efficiency of Distributed Data-Centric Compute Frameworks

The success of modern applications depends on the insights they collect from their data repositories. Data repositories for such applications currently exceed exabytes and are rapidly increasing in size, as they collect data from varied sources - web applications, mobile phones, sensors and other connected devices. Distributed storage and data-centric compute frameworks have been invented to store and analyze these large datasets. This dissertation focuses on extending the applicability and improving the efficiency of distributed data-centric compute frameworks

Advances in Data Mining Knowledge Discovery and Applications

Advances in Data Mining Knowledge Discovery and Applications aims to help data miners, researchers, scholars, and PhD students who wish to apply data mining techniques. The primary contribution of this book is highlighting frontier fields and implementations of the knowledge discovery and data mining. It seems to be same things are repeated again. But in general, same approach and techniques may help us in different fields and expertise areas. This book presents knowledge discovery and data mining applications in two different sections. As known that, data mining covers areas of statistics, machine learning, data management and databases, pattern recognition, artificial intelligence, and other areas. In this book, most of the areas are covered with different data mining applications. The eighteen chapters have been classified in two parts: Knowledge Discovery and Data Mining Applications

Monitoring and analysis system for performance troubleshooting in data centers

It was not long ago. On Christmas Eve 2012, a war of troubleshooting began in Amazon data centers. It started at 12:24 PM, with an mistaken deletion of the state data of Amazon Elastic Load Balancing Service (ELB for short), which was not realized at that time. The mistake first led to a local issue that a small number of ELB service APIs were affected. In about six minutes, it evolved into a critical one that EC2 customers were significantly affected. One example was that Netflix, which was using hundreds of Amazon ELB services, was experiencing an extensive streaming service outage when many customers could not watch TV shows or movies on Christmas Eve. It took Amazon engineers 5 hours 42 minutes to find the root cause, the mistaken deletion, and another 15 hours and 32 minutes to fully recover the ELB service. The war ended at 8:15 AM the next day and brought the performance troubleshooting in data centers to world’s attention. As shown in this Amazon ELB case.Troubleshooting runtime performance issues is crucial in time-sensitive multi-tier cloud services because of their stringent end-to-end timing requirements, but it is also notoriously difficult and time consuming. To address the troubleshooting challenge, this dissertation proposes VScope, a flexible monitoring and analysis system for online troubleshooting in data centers. VScope provides primitive operations which data center operators can use to troubleshoot various performance issues. Each operation is essentially a series of monitoring and analysis functions executed on an overlay network. We design a novel software architecture for VScope so that the overlay networks can be generated, executed and terminated automatically, on-demand. From the troubleshooting side, we design novel anomaly detection algorithms and implement them in VScope. By running anomaly detection algorithms in VScope, data center operators are notified when performance anomalies happen. We also design a graph-based guidance approach, called VFocus, which tracks the interactions among hardware and software components in data centers. VFocus provides primitive operations by which operators can analyze the interactions to find out which components are relevant to the performance issue. VScope’s capabilities and performance are evaluated on a testbed with over 1000 virtual machines (VMs). Experimental results show that the VScope runtime negligibly perturbs system and application performance, and requires mere seconds to deploy monitoring and analytics functions on over 1000 nodes. This demonstrates VScope’s ability to support fast operation and online queries against a comprehensive set of application to system/platform level metrics, and a variety of representative analytics functions. When supporting algorithms with high computation complexity, VScope serves as a ‘thin layer’ that occupies no more than 5% of their total latency. Further, by using VFocus, VScope can locate problematic VMs that cannot be found via solely application-level monitoring, and in one of the use cases explored in the dissertation, it operates with levels of perturbation of over 400% less than what is seen for brute-force and most sampling-based approaches. We also validate VFocus with real-world data center traces. The experimental results show that VFocus has troubleshooting accuracy of 83% on average.Ph.D

Efficient replication of large volumes of data and maintaining data consistency by using P2P techniques in Desktop Grid

Desktop Grid is increasing in popularity because of relatively very low cost and good performance in institutions. Data-intensive applications require data management in scientific experiments conducted by researchers and scientists in Desktop Grid-based Distributed Computing Infrastructure (DCI). Some of these data-intensive applications deal with large volumes of data. Several solutions for data-intensive applications have been proposed for Desktop Grid (DG) but they are not efficient in handling large volumes of data. Data management in this environment deals with data access and integration, maintaining basic properties of databases, architecture for querying data, etc. Data in data-intensive applications has to be replicated in multiple nodes for improving data availability and reducing response time. Peer-to-Peer (P2P) is a well established technique for handling large volumes of data and is widely used on the internet. Its environment is similar to the environment of DG. The performance of existing P2P-based solution dealing with generic architecture for replicating large volumes of data is not efficient in DG-based DCI. Therefore, there is a need for a generic architecture for replicating large volumes of data efficiently by using P2P in BOINC based Desktop Grid. Present solutions for data-intensive applications mainly deal with read only data. New type of applications are emerging which deal large volumes of data and Read/Write of data. In emerging scientific experiments, some nodes of DG generate new snapshot of scientific data after regular intervals. This new snapshot of data is generated by updating some of the values of existing data fields. This updated data has to be synchronised in all DG nodes for maintaining data consistency. The performance of data management in DG can be improved by addressing efficient data replication and consistency. Therefore, there is need for algorithms which deal with data Read/Write consistency along with replication for large volumes of data in BOINC based Desktop Grid. The research is to identify efficient solutions for data replication in handling large volumes of data and maintaining Read/Write data consistency using Peer-to-Peer techniques in BOINC based Desktop Grid. This thesis presents the solutions that have been carried out to complete the research

Business Analytics Using Predictive Algorithms

In today's data-driven business landscape, organizations strive to extract actionable insights and make informed decisions using their vast data. Business analytics, combining data analysis, statistical modeling, and predictive algorithms, is crucial for transforming raw data into meaningful information. However, there are gaps in the field, such as limited industry focus, algorithm comparison, and data quality challenges. This work aims to address these gaps by demonstrating how predictive algorithms can be applied across business domains for pattern identification, trend forecasting, and accurate predictions. The report focuses on sales forecasting and topic modeling, comparing the performance of various algorithms including Linear Regression, Random Forest Regression, XGBoost, LSTMs, and ARIMA. It emphasizes the importance of data preprocessing, feature selection, and model evaluation for reliable sales forecasts, while utilizing S-BERT, UMAP, and HDBScan unsupervised algorithms for extracting valuable insights from unstructured textual data

Development of a Machine Learning-Based Financial Risk Control System

With the gradual end of the COVID-19 outbreak and the gradual recovery of the economy, more and more individuals and businesses are in need of loans. This demand brings business opportunities to various financial institutions, but also brings new risks. The traditional loan application review is mostly manual and relies on the business experience of the auditor, which has the disadvantages of not being able to process large quantities and being inefficient. Since the traditional audit processing method is no longer suitable some other method of reducing the rate of non-performing loans and detecting fraud in applications is urgently needed by financial institutions. In this project, a financial risk control model is built by using various machine learning algorithms. The model is used to replace the traditional manual approach to review loan applications. It improves the speed of review as well as the accuracy and approval rate of the review. Machine learning algorithms were also used in this project to create a loan user scorecard system that better reflects changes in user information compared to the credit card systems used by financial institutions today. In this project, the data imbalance problem and the performance improvement problem are also explored

24th International Conference on Information Modelling and Knowledge Bases

In the last three decades information modelling and knowledge bases have become essentially important subjects not only in academic communities related to information systems and computer science but also in the business area where information technology is applied. The series of European – Japanese Conference on Information Modelling and Knowledge Bases (EJC) originally started as a co-operation initiative between Japan and Finland in 1982. The practical operations were then organised by professor Ohsuga in Japan and professors Hannu Kangassalo and Hannu Jaakkola in Finland (Nordic countries). Geographical scope has expanded to cover Europe and also other countries. Workshop characteristic - discussion, enough time for presentations and limited number of participants (50) / papers (30) - is typical for the conference. Suggested topics include, but are not limited to: 1. Conceptual modelling: Modelling and specification languages; Domain-specific conceptual modelling; Concepts, concept theories and ontologies; Conceptual modelling of large and heterogeneous systems; Conceptual modelling of spatial, temporal and biological data; Methods for developing, validating and communicating conceptual models. 2. Knowledge and information modelling and discovery: Knowledge discovery, knowledge representation and knowledge management; Advanced data mining and analysis methods; Conceptions of knowledge and information; Modelling information requirements; Intelligent information systems; Information recognition and information modelling. 3. Linguistic modelling: Models of HCI; Information delivery to users; Intelligent informal querying; Linguistic foundation of information and knowledge; Fuzzy linguistic models; Philosophical and linguistic foundations of conceptual models. 4. Cross-cultural communication and social computing: Cross-cultural support systems; Integration, evolution and migration of systems; Collaborative societies; Multicultural web-based software systems; Intercultural collaboration and support systems; Social computing, behavioral modeling and prediction. 5. Environmental modelling and engineering: Environmental information systems (architecture); Spatial, temporal and observational information systems; Large-scale environmental systems; Collaborative knowledge base systems; Agent concepts and conceptualisation; Hazard prediction, prevention and steering systems. 6. Multimedia data modelling and systems: Modelling multimedia information and knowledge; Contentbased multimedia data management; Content-based multimedia retrieval; Privacy and context enhancing technologies; Semantics and pragmatics of multimedia data; Metadata for multimedia information systems. Overall we received 56 submissions. After careful evaluation, 16 papers have been selected as long paper, 17 papers as short papers, 5 papers as position papers, and 3 papers for presentation of perspective challenges. We thank all colleagues for their support of this issue of the EJC conference, especially the program committee, the organising committee, and the programme coordination team. The long and the short papers presented in the conference are revised after the conference and published in the Series of “Frontiers in Artificial Intelligence” by IOS Press (Amsterdam). The books “Information Modelling and Knowledge Bases” are edited by the Editing Committee of the conference. We believe that the conference will be productive and fruitful in the advance of research and application of information modelling and knowledge bases. Bernhard Thalheim Hannu Jaakkola Yasushi Kiyok