Fraud Detection Decision Support System for Indonesian Financial Institution

Lawrencia, Clarisca; Ce, Win

doi:10.1109/icimtech.2019.8843719

Cited by 3 publications

(4 citation statements)

References 4 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Many studies published about fraud detection have tried to detect fraud in datasets consisting of credit card transactions [47][48][49]. However, since fraud is a temporal and constantly evolving problem, the past links of the transaction to be decided are also of great importance.…”

Section: Fraud Detection On Transactional Datasetsmentioning

confidence: 99%

A Multi-Modal Profiling Fraud-Detection System for Capturing Suspicious Airline Ticket Activities

Aras,

Guvensan

2023

Applied Sciences

View full text Add to dashboard Cite

Although the most widely studied datasets in fraud-detection systems belong to the banking sector, the aviation industry is susceptible to fraud activities that seriously harm airline companies. Therefore, big airline companies have started to purchase or develop their own fraud-detection systems in order to prevent their financial loss and prestige decline. Chronological order and temporal flow are intrinsically of high importance in fraud detection in the banking sector as well as in airline sale channels. Therefore, the transactions in the datasets used in fraud-detection systems should be evaluated not only according to the information they contain but also according to the past transactions they are linked to. One of the best ways to raise awareness about the connected past transactions to the fraud-detection system is to profile the data fields whose historical data is important and dynamically place these profiles on each transaction. In this study, we first draw the baseline, i.e., the first touch in this field, for fraud detection in aviation and then introduce a novel multi-modal profiling mechanism based on deep learning for the detection of fraudulent airline ticket activities. We achieved great success by feeding the new features obtained from those profiles into a deep neural network that is fine-tuned by adjusting the well-known hyperparameters regarding the aviation data. Thanks to the combination of profiling and deep learning, the F1 score of the proposed system reaches up to 89.3% and 93.2% in terms of quantity-based success and cost-based success, respectively.

show abstract

Section: Fraud Detection On Transactional Datasetsmentioning

confidence: 99%

A Multi-Modal Profiling Fraud-Detection System for Capturing Suspicious Airline Ticket Activities

Aras,

Guvensan

2023

Applied Sciences

View full text Add to dashboard Cite

show abstract

“…Moreover, the proposed method in a study by Badal-Valero et al (2018), which combines Benford's law and machine learning algorithms including LR, DT, neural networks and RF, helped to detect ML characteristics Anti-money laundering systems in the context of a real Spanish court case. A decision support system was created by Lawrencia and Ce (2019) to support financial institutions in detecting suspicious transactions. Thus, the end result is displayed in a user-friendly dashboard form.…”

Section: Machine Learning: Supervised Learningmentioning

confidence: 99%

“…Further, Paula et al (2016) used deep learning on databases of the Secretariat of Federal Revenue of Brazil to identify exporting companies that had divergences in their 2014 2018) study, a data set was used based on one month of financial logs of a mobile money service company in Africa, which had more than six million transactions. In a study by Lawrencia and Ce (2019), data were collected from transactional logs of a multinational bank in Indonesia and from public data provided by Kaggle PaySim. Colladon and Remondi (2017) analyzed financial operations data related to a medium-large manufacturing company in Italy from November 2013 to June 2015.…”

Section: Data Sourcesmentioning

confidence: 99%

See 1 more Smart Citation

Anti-money laundering systems: a systematic literature review

Alsuwailem

Saudagar

2020

JMLC

View full text Add to dashboard Cite

Purpose This paper aims to understand and document the state of the art in the anti-money laundering (AML) systems literature. Design/methodology/approach A systematic literature review (SLR) is performed using the Saudi Digital Library. The outputs published as conference proceedings, workshop proceedings, journal articles and books were all considered. The final sample size after omitting out-of-scope selections was 27 documents, which mainly span from 2015 to 2020. Findings The sample is discussed based on a categorization, which demarcates solutions, machine learning, data sources, evaluation methods, implementation tools, sampling techniques and regions of study. Originality/value This SLR could serve as a useful basis for researchers and salient decision-makers, who are seeking to understand the nature and extent of the currently available research into AML systems.

show abstract

Artificial Intelligence for Risk Management

De Santis

2024

Artificial Intelligence in Accounting and Auditing

View full text Add to dashboard Cite

Fraud Detection Decision Support System for Indonesian Financial Institution

Cited by 3 publications

References 4 publications

A Multi-Modal Profiling Fraud-Detection System for Capturing Suspicious Airline Ticket Activities

A Multi-Modal Profiling Fraud-Detection System for Capturing Suspicious Airline Ticket Activities

Anti-money laundering systems: a systematic literature review

Artificial Intelligence for Risk Management

Contact Info

Product

Resources

About