Data engineering for fraud detection

Baesens, Bart; Höppner, Sebastiaan; Verdonck, Tim

doi:10.1016/j.dss.2021.113492

Cited by 96 publications

(82 citation statements)

References 49 publications

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“…The authors proposed an end-to-end model for the feature extraction from financial time series samples and price movement prediction, using convolutional and recurrent neurons-a multi-filter neural network. Baesens et al [69] stated that data engineering is crucial to improve the performance of the most of machine learning models. In their paper, a data engineering process consisting of several feature and instance engineering steps is proposed and demonstrated on a payment transactions dataset from a large European bank.…”

Section: Financial Transactionsmentioning

confidence: 99%

Follow the Trail: Machine Learning for Fraud Detection in Fintech Applications

Stojanović

Božić

Hofer-Schmitz

et al. 2021

Sensors

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Financial technology, or Fintech, represents an emerging industry on the global market. With online transactions on the rise, the use of IT for automation of financial services is of increasing importance. Fintech enables institutions to deliver services to customers worldwide on a 24/7 basis. Its services are often easy to access and enable customers to perform transactions in real-time. In fact, advantages such as these make Fintech increasingly popular among clients. However, since Fintech transactions are made up of information, ensuring security becomes a critical issue. Vulnerabilities in such systems leave them exposed to fraudulent acts, which cause severe damage to clients and providers alike. For this reason, techniques from the area of Machine Learning (ML) are applied to identify anomalies in Fintech applications. They target suspicious activity in financial datasets and generate models in order to anticipate future frauds. We contribute to this important issue and provide an evaluation on anomaly detection methods for this matter. Experiments were conducted on several fraudulent datasets from real-world and synthetic databases, respectively. The obtained results confirm that ML methods contribute to fraud detection with varying success. Therefore, we discuss the effectiveness of the individual methods with regard to the detection rate. In addition, we provide an analysis on the influence of selected features on their performance. Finally, we discuss the impact of the observed results for the security of Fintech applications in the future.

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Section: Financial Transactionsmentioning

confidence: 99%

Follow the Trail: Machine Learning for Fraud Detection in Fintech Applications

Stojanović

Božić

Hofer-Schmitz

et al. 2021

Sensors

View full text Add to dashboard Cite

show abstract

“…For example, one can consider the minimum/maximum/ average/most recent monetary value of transactions. Note that besides marketing analytics (Baesens et al 2002;Blattberg et al 2008), these features have also been successfully used in fraud analytics (Van Vlasselaer et al 2015;Baesens et al 2021).…”

Section: Domain Specific Feature Engineeringmentioning

confidence: 99%

Special issue on feature engineering editorial

et al. 2021

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In order to improve the performance of any machine learning model, it is important to focus more on the data itself instead of continuously developing new algorithms. This is exactly the aim of feature engineering. It can be defined as the clever engineering of data hereby exploiting the intrinsic bias of the machine learning technique to our benefit, ideally both in terms of accuracy and interpretability at the same time. Often times it will be applied in combination with simple machine learning techniques such as regression models or decision trees to boost their performance (whilst maintaining the interpretability property which is so often needed in analytical modeling) but it may also improve complex techniques such as XGBoost and neural networks. Feature engineering aims at designing smart features in one of two possible ways: either by adjusting existing features using various transformations or by extracting or creating new meaningful features (a process often called "featurization") from different sources (e.g., transactional data, network data, time series data, text data, etc.).

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“…This subsection discusses the application of Instance Engineering techniques in detail. The careful selection of instances is crucial for improving the predictive models' performance [19]. Not every observation makes a positive contribution to the dataset and hence it is important to detect and remove them to train the models with the instances that positively contribute to the model learning capacity.…”

Section: Instance Engineeringmentioning

confidence: 99%

A Real-Life Machine Learning Experience for Predicting University Dropout at Different Stages Using Academic Data

et al. 2021

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High levels of school dropout are a major burden on the educational and professional development of a country's inhabitants. A country's prosperity depends, among other factors, on its ability to produce higher education graduates capable of moving a country forward. To alleviate the dropout problem, more and more institutions are turning to the possibilities that artificial intelligence can provide to predict dropout as early as possible. The difficulty of accessing personal data and privacy issues that it entails force the institutions to rely on the Academic Data of their students to create accurate and reliable predictive systems. This work focuses on creating the best possible predictive model based solely on academic data, and accordingly, its capacity to infer knowledge must be maximised. Thus, Feature Engineering and Instance Engineering techniques such as dealing with redundancy, significance of the features, correlation, cardinality features, missing values, creation or elimination of features, data fusion, removal of unuseful instances, binning, resampling, normalisation, or encoding are applied in detail before the construction of well-known models such as Gradient Boosting, Random Forest, and Support Vector Machine along with an Ensemble of them at different stages: prior to enrolment, at the end of the first semester, at the end of the second semester, at the end of the third semester, and at the end of the fourth semester. Through the construction of these predictive models that serve as inputs to a decision support system, the application of effective dropout prevention policies can be applied.

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Data engineering for fraud detection

Cited by 96 publications

References 49 publications

Follow the Trail: Machine Learning for Fraud Detection in Fintech Applications

Follow the Trail: Machine Learning for Fraud Detection in Fintech Applications

Special issue on feature engineering editorial

A Real-Life Machine Learning Experience for Predicting University Dropout at Different Stages Using Academic Data

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