[HTML][HTML] Financial fraud: a review of anomaly detection techniques and recent advances
With the rise of technology and the continued economic growth evident in modern society,
acts of fraud have become much more prevalent in the financial industry, costing institutions …
acts of fraud have become much more prevalent in the financial industry, costing institutions …
[HTML][HTML] How are reinforcement learning and deep learning algorithms used for big data based decision making in financial industries–A review and research agenda
V Singh, SS Chen, M Singhania, B Nanavati… - International Journal of …, 2022 - Elsevier
Data availability and accessibility have brought in unseen changes in the finance systems
and new theoretical and computational challenges. For example, in contrast to classical …
and new theoretical and computational challenges. For example, in contrast to classical …
Deep learning for anomaly detection: A survey
R Chalapathy, S Chawla - arXiv preprint arXiv:1901.03407, 2019 - arxiv.org
Anomaly detection is an important problem that has been well-studied within diverse
research areas and application domains. The aim of this survey is two-fold, firstly we present …
research areas and application domains. The aim of this survey is two-fold, firstly we present …
Deep learning for financial applications: A survey
Computational intelligence in finance has been a very popular topic for both academia and
financial industry in the last few decades. Numerous studies have been published resulting …
financial industry in the last few decades. Numerous studies have been published resulting …
Predicting clinical scores for Alzheimer's disease based on joint and deep learning
Alzheimer's disease (AD) is a progressive neurodegenerative disease that often grows in
middle-aged and elderly people with the gradual loss of cognitive ability. Presently, there is …
middle-aged and elderly people with the gradual loss of cognitive ability. Presently, there is …
[HTML][HTML] Deep learning-based monitoring of laser powder bed fusion process on variable time-scales using heterogeneous sensing and operando X-ray radiography …
Harnessing the full potential of the metal-based Laser Powder Bed Fusion process (LPBF)
relies heavily on how effectively the overall reliability and stability of the manufactured part …
relies heavily on how effectively the overall reliability and stability of the manufactured part …
[HTML][HTML] Short-term temperature forecasts using a convolutional neural network—An application to different weather stations in Germany
D Kreuzer, M Munz, S Schlüter - Machine Learning with Applications, 2020 - Elsevier
Local temperature forecasts for horizons up to 24 h are required in many applications. A
common method to generate such forecasts is the Seasonal Autoregressive Integrated …
common method to generate such forecasts is the Seasonal Autoregressive Integrated …
Fraud detection using the fraud triangle theory and data mining techniques: A literature review
M Sánchez-Aguayo, L Urquiza-Aguiar… - Computers, 2021 - mdpi.com
Fraud entails deception in order to obtain illegal gains; thus, it is mainly evidenced within
financial institutions and is a matter of general interest. The problem is particularly complex …
financial institutions and is a matter of general interest. The problem is particularly complex …
Deep learning methods for credit card fraud detection
TT Nguyen, H Tahir, M Abdelrazek, A Babar - arXiv preprint arXiv …, 2020 - arxiv.org
Credit card frauds are at an ever-increasing rate and have become a major problem in the
financial sector. Because of these frauds, card users are hesitant in making purchases and …
financial sector. Because of these frauds, card users are hesitant in making purchases and …
An efficient spatial-temporal convolution recurrent neural network surrogate model for history matching
Surrogate modeling has shown to be effective in improving the solving efficiency for history
matching in the development of oil and gas, but the traditional surrogate models are difficult …
matching in the development of oil and gas, but the traditional surrogate models are difficult …