• Corpus ID: 33467754

Anomaly detection in banking operations

  title={Anomaly detection in banking operations},
  author={Chilukuri Krishna Mohan and Kishan G. Mehrotra},
This paper presents an overview of anomaly detection algorithms and methodology, focusing on the context of banking operations applications. The main principles of anomaly detection are first presented, followed by listing some of the areas in banking that can benefit from anomaly detection. We then discuss traditional nearest-neighbor and clustering-based approaches. Time series and other sequential data analysis approaches are described. The problems posed by categorical data are also… 

Figures and Tables from this paper



Application of Anomaly Detection Techniques to Identify Fraudulent Refunds

Anomaly detection is a concept widely applied to numerous domains. Several techniques of anomaly detection have been developed over the years, in practice as well as research. The application of this

Survey of Clustering Based Financial Fraud Detection Research

This paper surveys clustering techniques used in fraud detection over the last ten years, shortly reviewing each one.

Ensemble Algorithms for Unsupervised Anomaly Detection

This paper proposes ensemble methods to improve the performance of individual anomaly detection algorithms, including density-based and rank-based algorithms and considers sequential methods in which one detection method is followed by the other.

Anomaly detection on time series

  • Mingyan Teng
  • Computer Science
    2010 IEEE International Conference on Progress in Informatics and Computing
  • 2010
This work proposes an instance-based anomaly detection algorithm based on some existing outlier detection algorithms, and proposes a local instance summarization approach to reduce the number of distance computation of time series, so that abnormal time series can be efficiently detected.

Arima model for network traffic prediction and anomaly detection

This paper presents the use of a basic ARIMA model for network traffic prediction and anomaly detection and decomposes traffic signals into two parts normal variations that follow certain law and are predictable and, anomalies that consist of sudden changes and are not predictable.

Evolution of Space-Partitioning Forest for Anomaly Detection

The aims of this study were to give some mathematical analysis of the random partitioning trees, and explore optimizing forests for anomaly detection using evolutionary algorithms.

A comprehensive survey of numeric and symbolic outlier mining techniques

This survey discuses practical applications of outlier mining, provides a taxonomy for categorizing related mining techniques, and provides a comprehensive review of these techniques with their advantages and disadvantages.

An Online Anomalous Time Series Detection Algorithm for Univariate Data Streams

Empirical evidence shows that this novel online anomalous time series detection algorithm performs very well, while being efficient in terms of time complexity, when compared to approaches previously discussed in the literature.

A Survey of Outlier Detection Methodologies

A survey of contemporary techniques for outlier detection is introduced and their respective motivations are identified and distinguish their advantages and disadvantages in a comparative review.

Anomaly Detection in Time Series of Graphs using ARMA Processes

A dynamic communication network is characterised as a series of graphs with vertices representing IP addresses and edges representing information exchange between these entities weighted by packets sent to create time series of network changes by sequentially comparing graphs from adjacent periods.