Systems and Information Engineering Design Symposium 

About: Systems and Information Engineering Design Symposium is an academic conference. The conference publishes majorly in the area(s): Computer science & Population. Over the lifetime, 1111 publications have been published by the conference receiving 4576 citations.

Deep learning detecting fraud in credit card transactions

Abstract: Credit card fraud resulted in the loss of $3 billion to North American financial institutions in 2017. The rise of digital payments systems such as Apple Pay, Android Pay, and Venmo has meant that loss due to fraudulent activity is expected to increase. Deep Learning presents a promising solution to the problem of credit card fraud detection by enabling institutions to make optimal use of their historic customer data as well as real-time transaction details that are recorded at the time of the transaction. In 2017, a study found that a Deep Learning approach provided comparable results to prevailing fraud detection methods such as Gradient Boosted Trees and Logistic Regression. However, Deep Learning encompasses a number of topologies. Additionally, the various parameters used to construct the model (e.g. the number of neurons in the hidden layer of a neural network) also influence its results. In this paper, we evaluate a subsection of Deep Learning topologies — from the general artificial neural network to topologies with built-in time and memory components such as Long Short-term memory — and different parameters with regard to their efficacy in fraud detection on a dataset of nearly 80 million credit card transactions that have been pre-labeled as fraudulent and legitimate. We utilize a high performance, distributed cloud computing environment to navigate past common fraud detection problems such as class imbalance and scalability. Our analysis provides a comprehensive guide to sensitivity analysis of model parameters with regard to performance in fraud detection. We also present a framework for parameter tuning of Deep Learning topologies for credit card fraud detection to enable financial institutions to reduce losses by preventing fraudulent activity.

Crime prediction using Twitter sentiment and weather

Abstract: Social networking services have the hidden potential to reveal valuable insights when statistical analysis is applied to their unstructured data. As shown by previous research, GPS-tagged Twitter data enables the prediction of future crimes in a major city, Chicago, Illinois, of the United States. However, existing crime prediction models that incorporate data from Twitter have limitations in describing criminal incidents due to the absence of sentiment polarity and weather factors. The addition of sentiment analysis and weather predictors to such models would deliver significant insight about how crime. Our aim is to predict the time and location in which a specific type of crime will occur. Our approach is based on sentiment analysis by applying lexicon-based methods and understanding of categorized weather data, combined with kernel density estimation based on historical crime incidents and prediction via linear modeling. By testing our model’s ability to predict future crime on each area of the city, we observed that the model surpassed the benchmark model, which predicts crime incidents using kernel density estimation.

A real-time ergonomic monitoring system using the Microsoft Kinect

Abstract: Laborers in factories all across the world perform physically intensive tasks daily With every lift they put themselves at risk of injury Many still-frame modeling systems exist that can assess the different stresses and strains on the laborers body given his or her position These models are only usable by experts, and do not allow for real-time alerts In 1995, companies in the United States lost $50 billion due to injured employee absences and compensation settlements Companies are not only eager to reduce their overhead costs, but also aim to better society by offering more robust worker safety practices The focus of this project was to design a system that can be used in a training environment Our system is used to teach employees if their current lifting and carrying methods can be detrimental to their health Our system is designed to be used for longstanding employees as well as new hires This project's primary requirement was to implement a motion sensing device to aid in the analysis of ergonomics in an industrial environment To do this we proposed to make use of Microsoft Kinect© sensors The Kinect© is able to provide skeletal tracking at 30 frames/second for two individuals in the field of view To develop the system we selected the Microsoft software development kit (SDK) from a large variety of alternative professional and open source SDKs because of a variety of desirable features A static ergonomic model was integrated with the Kinect© software Multiple other software packages were assessed for compatibility with the Kinect© in an effort to enhance the Kinects'O ability to recognize objects and humans After development was complete the system was tested by analyzing our system's output using different skeletal lift positions to compare to the real results Our system provides real-time ergonomic analysis of lifts performed by humans This system lacks the ability to recognize specific individuals and objects necessary to customize the system to adequately evaluate a lift, and has not been tested in a factory environment In the future we hope to implement a dynamic ergonomic model so that it can recognize whole movements or gestures which lead to injury, rather than recognizing a single position Our system successfully outputs a number for the recommended weight limit as well as other methods to measure the strain on a worker's skeleton In a training environment the system will help individuals correct the problems with their lifting motions

Horse race analysis in credit card fraud—deep learning, logistic regression, and Gradient Boosted Tree

Abstract: Fraud detection is an industry where incremental gains in predictive accuracy can have large benefits for banks and customers. Banks adapt models to the novel ways in which “fraudsters” commit credit card fraud. They collect data and engineer new features in order to increase predictive power. This research compares the algorithmic impact on the predictive power across three supervised classification models: logistic regression, gradient boosted trees, and deep learning. This research also explores the benefits of creating features using domain expertise and feature engineering using an autoencoder—an unsupervised feature engineering method. These two methods of feature engineering combined with the direct mapping of the original variables create six different feature sets. Across these feature sets this research compares the aforementioned models. This research concludes that creating features using domain expertise offers a notable improvement in predictive power. Additionally, the autoencoder offers a way to reduce the dimensionality of the data and slightly boost predictive power.

Adversarial learning in credit card fraud detection

Abstract: Credit card fraud is an expensive problem for many financial institutions, costing billions of dollars to companies annually. Many adversaries still evade fraud detection systems because these systems often do not include information about the adversary's knowledge of the fraud detection mechanism. This project aims to include information about the “fraudster's” motivations and knowledge base into an adaptive fraud detection system. In this project, we use a game theoretical adversarial learning approach in order to model the fraudster's best strategy and pre-emptively adapt the fraud detection system to better classify these future fraudulent transactions. Using a logistic regression classifier as the fraud detection mechanism, we initially identify the best strategy for the adversary based on the number of fraudulent transactions that go undetected, and assume that the adversary uses this strategy for future transactions in order to improve our classifier. Prior research has used game theoretic models for adversarial learning in the domains of credit card fraud and email spam, but this project adds to the literature by extending these frameworks to a practical, real-world data set. Test results show that our adversarial framework produces an increasing AUC score on validation sets over several iterations in comparison to the static model usually employed by credit card companies.