Internet Financial Fraud Detection Based on a Distributed Big Data Approach With Node2vec

Abstract

The rapid development of information technologies like Internet of Things, Big Data, Artificial Intelligence, Blockchain, etc., has profoundly affected people's consumption behaviors and changed the development model of the financial industry. The financial services on Internet and IoT with new technologies has provided convenience and efficiency for consumers, but new hidden fraud risks are generated also. Fraud, arbitrage, vicious collection, etc., have caused bad effects and huge losses to the development of finance on Internet and IoT. However, as the scale of financial data continues to increase dramatically, it is more and more difficult for existing rule-based expert systems and traditional machine learning model systems to detect financial frauds from large-scale historical data. In the meantime, as the degree of specialization of financial fraud continues to increase, fraudsters can evade fraud detection by frequently changing their fraud methods. In this article, an intelligent and distributed Big Data approach for Internet financial fraud detections is proposed to implement graph embedding algorithm Node2Vec to learn and represent the topological features in the financial network graph into low-dimensional dense vectors, so as to intelligently and efficiently classify and predict the data samples of the large-scale dataset with the deep neural network. The approach is distributedly performed on the clusters of Apache Spark GraphX and Hadoop to process the large dataset in parallel. The groups of experimental results demonstrate that the proposed approach can improve the efficiency of Internet financial fraud detections with better precision rate, recall rate, F1-Score and F2-Score.

Synergy of Blockchain Technology and Data Mining Techniques for Anomaly Detection

“…Zhou et al [116] proposed a financial fraud detection system by using Node2vec. To evaluate their proposal they used a data set provided from an Internet financial service provider in China.…”

Large scale data analysis using MLlib

“…These libraries are described in more detail below [106]- [108]. However, despite the fact that the most basic scheduling Spark modules are Apache Spark Streaming, which is fault tolerant and performs high level analytics, Apache Spark SQL performs relational queries for a variety of mining databases because it incorporates a data abstraction model known as data frames [109]. It is important to note that Apache Spark GraphX [110] is a high-level Apache Spark processing library that can handle two commonly used data structures utilizing distributed arithmetic models.…”

Automatic Detection Algorithm of Football Events in Videos

“…Football is one of the users' favorite sports videos, and extracting useful information from football game videos has attracted much attention. The analysis and retrieval of football game videos aim to analyze and research various football game videos, establish a bridge between low-level semantics and high-level semantics, and ultimately meet the needs of users [ 5 ]. However, the current detection of football videos is often limited by problems such as complicated background and low accuracy [ 6 ].…”