Fraud Detection In Mobile Communications Networks Using User Profiling And Classification Techniques

Fraud detection is an important application, since network operators lose a relevant portion of their revenue to fraud. The intentions of mobile phone users cannot be well observed except through the call data. The call data is used in describing behavioural patterns of users. Neural networks and probabilistic models are employed in learning these usage patterns from call data by detecting changes in established usage patterns or to recognize typical usage patterns of fraud. The methods are shown to be effective in detecting fraudulent behaviour by empirically testing the methods with data from real mobile communications networks.Keywords: Call data, fraud detection, neural networks, probabilistic models, user profilin

Probabilistic XGBoost Threshold Classification with Autoencoder for Credit Card Fraud Detection

Due to the imbalanced data of outnumbered legitimate transactions than the fraudulent transaction, the detection of fraud is a challenging task to find an effective solution. In this study, autoencoder with probabilistic threshold shifting of XGBoost (AE-XGB) for credit card fraud detection is designed. Initially, AE-XGB employs autoencoder the prevalent dimensionality reduction technique to extract data features from latent space representation. Then the reconstructed lower dimensional features utilize eXtreame Gradient Boost (XGBoost), an ensemble boosting algorithm with probabilistic threshold to classify the data as fraudulent or legitimate. In addition to AE-XGB, other existing ensemble algorithms such as Adaptive Boosting (AdaBoost), Gradient Boosting Machine (GBM), Random Forest, Categorical Boosting (CatBoost), LightGBM and XGBoost are compared with optimal and default threshold. To validate the methodology, we used IEEE-CIS fraud detection dataset for our experiment. Class imbalance and high dimensionality characteristics of dataset reduce the performance of model hence the data is preprocessed and trained. To evaluate the performance of the model, evaluation indicators such as precision, recall, f1-score, g-mean and Mathews Correlation Coefficient (MCC) are accomplished. The findings revealed that the performance of the proposed AE-XGB model is effective in handling imbalanced data and able to detect fraudulent transactions with 90.4% of recall and 90.5% of f1-score from incoming new transactions

Show Me Your Claims and I'll Tell You Your Offenses: Machine Learning-Based Decision Support for Fraud Detection on Medical Claim Data

Health insurance claim fraud is a serious issue for the healthcare industry as it drives up costs and inefficiency. Therefore, claim fraud must be effectively detected to provide economical and high-quality healthcare. In practice, however, fraud detection is mainly performed by domain experts resulting in significant cost and resource consumption. This paper presents a novel Convolutional Neural Network-based fraud detection approach that was developed, implemented, and evaluated on Medicare Part B records. The model aids manual fraud detection by classifying potential types of fraud, which can then be specifically analyzed. Our model is the first of its kind for Medicare data, yields an AUC of 0.7 for selected fraud types and provides an applicable method for medical claim fraud detection

Detection of Non-Technical Losses: The Project MIDAS

The MIDAS project began in 2006 as collaboration between Endesa, Sadiel, and the University of Seville. The objective of the MIDAS project is the detection of Non-Technical Losses (NTLs) on power utilities. The NTLs represent the non-billed energy due to faults or illegal manipulations in clients’ fa cilities. Initially, research lines study the application of techniques of data mining and neural networks. After several researches, the studies are expanded to other research fields: expert systems, text mining, statistical techniques, pattern recognition, etc. These techniques have provided an automated system for detection of NTLs on company databases. This system is in the test phase, and it is applied in real cases in company databases