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Data science and AI in FinTech: An overview [article]

Longbing Cao, Qiang Yang, Philip S. Yu
2021 arXiv   pre-print
The research on data science and AI in FinTech involves many latest progress made in smart FinTech for BankingTech, TradeTech, LendTech, InsurTech, WealthTech, PayTech, RiskTech, cryptocurrencies, and  ...  The DSAI research for enabling smart banking includes DSAI techniques for detecting, analyzing and managing a bank's network risk and risk contagion; modeling, detecting and managing banking risk and fraud  ...  Sequential and hierarchical hybridization Time-series analysis plus classification, macro/microeconomic dependency modeling, deep sequential modeling-based event detection Financial review-based fraud  ... 
arXiv:2007.12681v2 fatcat:jntzuwaktjg2hmmjypi5lvyht4

Data science for building energy management: A review

Miguel Molina-Solana, María Ros, M. Dolores Ruiz, Juan Gómez-Romero, M.J. Martin-Bautista
2017 Renewable & Sustainable Energy Reviews  
Expected energy loads, transportation, and storage as well as user behavior influence the quantity and quality of the energy consumed daily in buildings.  ...  The work also discusses the challenges and opportunities that will arise with the advent of fully connected devices and new computational technologies. * Corresponding author Email addresses: miguelmolina  ...  Neural networks were also employed for abnormalities and fraud detection in energy consumption in Galván et al. [62] . Filho et al.  ... 
doi:10.1016/j.rser.2016.11.132 fatcat:kc2qhyvbxjcdzczfx5upxmtaxi

Anomaly detection techniques for streaming data–An overview

Saranya Kunasekaran, Chellammal Suriyanarayanan
2020 Malaya Journal of Matematik  
Detecting anomaly in right time facilitates the appropriate control actions for the anomaly in right time. There are several techniques for detecting anomaly.  ...  In this paper, an overview of different techniques for detection of anomaly is presented.  ...  In [30] , Hierarchical Temporal Memory (HTM) which is based on online sequence memory is proposed as a novel solution for anomaly detection over streams. algorithm.  ... 
doi:10.26637/mjm0s20/0133 fatcat:blyjw2z4q5datacu7y4lavwchq

Social Fraud Detection Review: Methods, Challenges and Analysis [article]

Saeedreza Shehnepoor, Roberto Togneri, Wei Liu, Mohammed Bennamoun
2021 arXiv   pre-print
Many studies proposed approaches based on user behaviors and review text to address the challenges of fraud detection.  ...  The supervised approaches for fraud detection are introduced and categorized into two sub-categories; classical, and deep learning.  ...  So fraud detection is important to provide spam-free platforms for users.  ... 
arXiv:2111.05645v1 fatcat:qp3zuv74lbaq3hw2ajxm6lfkim

Deep Learning for Insider Threat Detection: Review, Challenges and Opportunities [article]

Shuhan Yuan, Xintao Wu
2020 arXiv   pre-print
The existing studies show that compared with traditional machine learning algorithms, deep learning models can improve the performance of insider threat detection.  ...  on feature engineering, are hard to accurately capture the behavior difference between insiders and normal users due to various challenges related to the characteristics of underlying data, such as high-dimensionality  ...  Hence, the model explainability is a key to provide the insight of the model to domain-expert so that further actions can be conducted with high confidence. • Lack of Testbed.  ... 
arXiv:2005.12433v1 fatcat:bmmog7g47vfmpmzdvd4tqd5v7u

A Sequence Mining-Based Novel Architecture for Detecting Fraudulent Transactions in Healthcare Systems

Irum Matloob, Shoab Ahmed Khan, Rukaiya Rukaiya, Muazzam A. Khan Khattak, Arslan Munir
2022 IEEE Access  
Hence, to introduce transparency in health support programs, there is a need to develop intelligent fraud detection models for tracing the loopholes in existing procedures, so that the fraudulent medical  ...  The sequence rule engine generates frequent sequences along with confidence values for each hospital's specialty and compares them with the actual patient values.  ...  The model detected 69% frauds correctly but unable to detect cases with overdoses of medicines.  ... 
doi:10.1109/access.2022.3170888 fatcat:tf6tm4o4znf6zfsprhup5cn2ci

Graph based anomaly detection and description: a survey

Leman Akoglu, Hanghang Tong, Danai Koutra
2014 Data mining and knowledge discovery  
Finally, we present several real-world applications of graph-based anomaly detection in diverse domains, including financial, auction, computer traffic, and social networks.  ...  Detecting anomalies in data is a vital task, with numerous high-impact applications in areas such as security, finance, health care, and law enforcement.  ...  Government is authorized to reproduce and distribute reprints for Government purposes notwithstanding any copyright notation here on.  ... 
doi:10.1007/s10618-014-0365-y fatcat:rfjn7bwdgra5faorwbdkkb45ze

Graph-based Anomaly Detection and Description: A Survey [article]

Leman Akoglu and Hanghang Tong and Danai Koutra
2014 arXiv   pre-print
Finally, we present several real-world applications of graph-based anomaly detection in diverse domains, including financial, auction, computer traffic, and social networks.  ...  Detecting anomalies in data is a vital task, with numerous high-impact applications in areas such as security, finance, health care, and law enforcement.  ...  Government is authorized to reproduce and distribute reprints for Government purposes notwithstanding any copyright notation here on.  ... 
arXiv:1404.4679v2 fatcat:y6nsswymcfc2pa7qe7zrjzc7wq

Fake Reviews Detection: A Survey

Rami Mohawesh, Shuxiang Xu, Son N. Tran, Robert Ollington, Matthew Springer, Yaser Jararweh, Sumbal Maqsood
2021 IEEE Access  
Further, we conduct a benchmark study to investigate the performance of different neural network models and transformers that have not been used for fake review detection yet.  ...  The experimental results on two benchmark datasets show that RoBERTa performs about 7% better than the state-of-the-art methods in a mixed domain for the deception dataset with the highest accuracy of  ...  TABLE 11 . 11 Summary of other neural network models in One domain, Mix domain and Cross-domain for Fake Reviews Detection.  ... 
doi:10.1109/access.2021.3075573 fatcat:p33ialjjjrelfavcpicty44zxy

Financial Cybercrime: A Comprehensive Survey of Deep Learning Approaches to Tackle the Evolving Financial Crime Landscape

Jack Nicholls, Aditya Kuppa, Nhien-An Le-Khac
2021 IEEE Access  
Sequences of financial activity were modeled using a 2) Fraud Detection and Anti-money Laundering (AML) discrete-time Markov chain model.  ...  The EU anti-fraud for many domains applying unsupervised anomaly detection office [65] have stated that customs fraud is financially dam-  ... 
doi:10.1109/access.2021.3134076 fatcat:lm2upcaoabbnbie6r4sfzhjh4y

Survey for Trust-aware Recommender Systems: A Deep Learning Perspective [article]

Manqing Dong, Feng Yuan, Lina Yao, Xianzhi Wang, Xiwei Xu, Liming Zhu
2020 arXiv   pre-print
A significant remaining challenge for existing recommender systems is that users may not trust the recommender systems for either lack of explanation or inaccurate recommendation results.  ...  filter untruthful noises (e.g., spammers and fake information) or enhance attack resistance; explainable recommender systems that provide explanations of recommended items.  ...  They consider a cross-domain recommendation problem [94] , where they are trying to recommend a top-K items list from information-domain to users in social-domain.  ... 
arXiv:2004.03774v2 fatcat:q7mehir7hbbzpemw3q5fkby5ty

Reinforcement learning on graphs: A survey [article]

Nie Mingshuo, Chen Dongming, Wang Dongqi
2022 arXiv   pre-print
Graph mining tasks arise from many different application domains, ranging from social networks, transportation to E-commerce, etc., which have been receiving great attention from the theoretical and algorithmic  ...  In addition, we create an online open-source for both interested scholars who want to enter this rapidly developing domain and experts who would like to compare GRL methods.  ...  FinEvent [139] for social network modeling tasks allows transferring cross-lingual social event detection through modeling social messages into heterogeneous graphs.  ... 
arXiv:2204.06127v2 fatcat:7wf6qxnxzza7xbiwjgjmrsrdjq

COMPARATIVE STUDY OF TESTING METHOD AND TOOLS FOR WEB SITES

Ms.AmiShaileshkumar Desai, Dr.Sanjay Buch
2016 International Journal of Advanced Research  
In this review we discuss the security testing methods as well as model issues of web services. Development based on SOA is still required for providing the unique security or proper testing.  ...  Because People may unaware have fraud and crime happened online or they have less command on English language. So, threats are increasing day by day.  ...  Prepare model using UML. Explain steps how to work with this model. Using GIBES model author identify the most common theft are as follows: Keylogger Screenlogger Phishing Spyware Social Engineering.  ... 
doi:10.21474/ijar01/2340 fatcat:gad6lkpuifbf3e7ccu65rtrrq4

AI in Finance: Challenges, Techniques and Opportunities [article]

Longbing Cao
2021 arXiv   pre-print
This area has been lasting for decades with both classic and modern AI techniques applied to increasingly broader areas of finance, economy and society.  ...  The comparison, criticism and discussion of classic vs. modern AI techniques for finance are followed.  ...  Techniques for behavior modeling, behavior informatics, user modeling, event modeling, historical event analysis, event detection, positive (occurring) and negative (non-occurring) sequence analysis [  ... 
arXiv:2107.09051v1 fatcat:g62cz4dqt5dcrbckn4lbveat3u

A Novel text2IMG Mechanism of Credit Card Fraud Detection: A Deep Learning Approach

Abdullah Alharbi, Majid Alshammari, Ofonime Dominic Okon, Amerah Alabrah, Hafiz Tayyab Rauf, Hashem Alyami, Talha Meraj
2022 Electronics  
This not only provides convenience to the end-user but also increases the frequency of online credit card fraud.  ...  Recent studies have proposed machine learning (ML)-based solutions for detecting fraudulent credit card transactions, but their detection scores still need improvement due to the imbalance of classes in  ...  Acknowledgments: We deeply acknowledge Taif University for Supporting this research through Taif University Researchers Supporting Project number (TURSP-2020/231), Taif University, Taif, Saudi Arabia.  ... 
doi:10.3390/electronics11050756 fatcat:dcjhgnoc5ncnzjfdxapqz5pgly
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