scholarly journals A semantic rule based digital fraud detection

2021 ◽  
Vol 7 ◽  
pp. e649
Author(s):  
Mansoor Ahmed ◽  
Kainat Ansar ◽  
Cal B. Muckley ◽  
Abid Khan ◽  
Adeel Anjum ◽  
...  

Digital fraud has immensely affected ordinary consumers and the finance industry. Our dependence on internet banking has made digital fraud a substantial problem. Financial institutions across the globe are trying to improve their digital fraud detection and deterrence capabilities. Fraud detection is a reactive process, and it usually incurs a cost to save the system from an ongoing malicious activity. Fraud deterrence is the capability of a system to withstand any fraudulent attempts. Fraud deterrence is a challenging task and researchers across the globe are proposing new solutions to improve deterrence capabilities. In this work, we focus on the very important problem of fraud deterrence. Our proposed work uses an Intimation Rule Based (IRB) alert generation algorithm. These IRB alerts are classified based on severity levels. Our proposed solution uses a richer domain knowledge base and rule-based reasoning. In this work, we propose an ontology-based financial fraud detection and deterrence model.

Author(s):  
Prof. Sangeetha J. ◽  
Jegatheesh B. S. ◽  
Balaji B ◽  
Hemnath N

Fraud detection is an emerging topic of notable importance. Data mining strategies have been applied most considerably to the detection of insurance fraud, monetary fraud and financial fraud. This project will mainly focus on detecting fraudulent credit card transactions. Fraud detection in telecommunication systems, particularly the case of extraordinary imposed fraud, providing an anomaly detection technique supported by way of a signature schema, fraud deals with cases regarding criminal purposes that typically are different to identify, have additionally attracted a a tremendous deal of attention in latest years. The use of credit cards has dramatically increased because of a fast advancement inside the electronic commerce technology. Credit card will become the most popular mode of payment for each on line as properly as ordinary purchase, in instances of fraud related to it are also growing day through day. In this research sequence of operations in credit card transaction processing using a Fuzzy rule based classifier and accuracy is improved in the detection of frauds compared to other algorithms. A Naïve Bayes is initially trained with the everyday behaviour of a card holder. If an incoming credit card transaction is not accepted by the trained version with sufficiently excessive probability, it’s considered to be fraudulent. At the same time, it ensures that true transactions aren’t rejected. Supervised learning requires prior type to anomalies. In this research fuzzy rule primarily based category set of rules used for modelling real world credit card information statistics and detecting the anomaly usage of credit card information’s. Whenever anomaly credit card usage detected the system will capture the anomaly user face and freeze the anomaly user system. Django framework is used for web app creation.


2020 ◽  
Vol 2020 ◽  
pp. 1-5 ◽  
Author(s):  
Sajjad Daliri

Financial fraud is among the main problems undermining the confidence of customers in addition to incurring economic losses to banks and financial institutions. In recent years, along with the proliferation of fraud, financial institutions began looking for ways to find a suitable solution in the fight against fraud. Given the advanced and varied changes in methods of fraud, extensive research has been conducted to detect fraud. In this paper, the Artificial Neural Network technique and Harmony Search Algorithm are used to detect fraud. In the proposed method, hidden patterns between normal and fraudulent customers’ information are searched. Given that fraudulent behavior could be detected and stopped before they take place, the results of the proposed system show that it has an acceptable capability in fraud detection.


2022 ◽  
Vol 24 (3) ◽  
pp. 0-0

In this digital era, people are very keen to share their feedback about any product, services, or current issues on social networks and other platforms. A fine analysis of these feedbacks can give a clear picture of what people think about a particular topic. This work proposed an almost unsupervised Aspect Based Sentiment Analysis approach for textual reviews. Latent Dirichlet Allocation, along with linguistic rules, is used for aspect extraction. Aspects are ranked based on their probability distribution values and then clustered into predefined categories using frequent terms with domain knowledge. SentiWordNet lexicon uses for sentiment scoring and classification. The experiment with two popular datasets shows the superiority of our strategy as compared to existing methods. It shows the 85% average accuracy when tested on manually labeled data.


2021 ◽  
Vol 18 (1) ◽  
pp. 39-58
Author(s):  
Abdulazeem Abozaid

Since its inception a few decades ago, the industry of Islamic banking and finance has been regulating itself in terms of Sharia governance. Although some regulatory authorities from within the industry, such as Accounting and Auditing Organization for Islamic Financial Institutions (AAOIFI) and Islamic Financial Services Board (IFSB), the Islamic banking and finance industry remains to a great extent self-regulated. This is because none of the resolutions or the regulatory authorities' standards are binding on the Islamic financial institution except when the institution itself willingly chooses to bind itself by them. Few countries have enforced some Sharia-governance-related regulations on their Islamic banks. However, in most cases, these regulations do not go beyond the requirement to formulate some Sharia controlling bodies, which are practically left to the same operating banks. Furthermore, some of the few existing regulatory authorities' standards and resolutions are conflicted with other resolutions issued by Fiqh academies. The paper addresses those issues by highlighting the shortcomings and then proposing the necessary reforms to help reach effective Shariah governance that would protect the industry from within and help it achieve its goals. The paper concludes by proposing a Shariah governance model that should overcome the challenges addressed in the study.Pada awal berdiri, Lembaga Keuangan Syariah merupakan lembaga keuangan yang menerapkan Hukum Syariah secara mandiri dalam sistem operasionalnya. Ia tidak tunduk pada peraturan lembaga keuangan konvensional, sehingga dapat terus berkomiten dalam menerapkan Hukum Syariah secara benar. Selanjutnya, muncullah beberapa otoritas peraturan yang berasal dari pengembangan Lembaga Keuangan Syariah. Diantaranya adalah Islamic Financial Services Board (IFSB) dan Accounting and Auditing Organization for Islamic Financial Institutions (AAOIFI). Hal ini tidak menyimpang dari kerangka peraturan Hukum Syariah, sebab standar peraturan dan keputusan yang dikeluarkan ditujukan khusus untuk Lembaga Keuangan Syariah saja. Beberapa Negara telah menerapkan peraturan tata kelola Hukum Syariah pada Bank Syariah mereka. Namun dalam banyak kasus, peraturan yang diterapkan tidak mampu mengontrol Lembaga Keuangan Syariah tersebut secara penuh. Sehingga, secara praktis proses pengawasan diserahkan kepada lembaga keuangan yang beroperasi. Akan tetapi, beberapa standar dan keputusan yang dikeluarkan oleh sebagian pemangku kebijakan bertentangan dengan keputusan yang dikeluarkan oleh beberapa akademi Fiqh. Artikel ini ditulis untuk menyoroti permasalahan yang timbul pada tata kelola Lembaga Keuangan Syariah, khususnya kekurangan yang tampak pada sistem tata kelola. Kemudian, penulis akan mengajukan usulan tentang efektifitas tata kelola Lembaga Keuangan Syariah yang bebas dari permasalahan.


2017 ◽  
Vol 176 (3) ◽  
pp. 45-52
Author(s):  
Akshansh Sinha ◽  
Shivam Mokha

Author(s):  
Yunpeng Li ◽  
Utpal Roy ◽  
Y. Tina Lee ◽  
Sudarsan Rachuri

Rule-based expert systems such as CLIPS (C Language Integrated Production System) are 1) based on inductive (if-then) rules to elicit domain knowledge and 2) designed to reason new knowledge based on existing knowledge and given inputs. Recently, data mining techniques have been advocated for discovering knowledge from massive historical or real-time sensor data. Combining top-down expert-driven rule models with bottom-up data-driven prediction models facilitates enrichment and improvement of the predefined knowledge in an expert system with data-driven insights. However, combining is possible only if there is a common and formal representation of these models so that they are capable of being exchanged, reused, and orchestrated among different authoring tools. This paper investigates the open standard PMML (Predictive Model Mockup Language) in integrating rule-based expert systems with data analytics tools, so that a decision maker would have access to powerful tools in dealing with both reasoning-intensive tasks and data-intensive tasks. We present a process planning use case in the manufacturing domain, which is originally implemented as a CLIPS-based expert system. Different paradigms in interpreting expert system facts and rules as PMML models (and vice versa), as well as challenges in representing and composing these models, have been explored. They will be discussed in detail.


Sign in / Sign up

Export Citation Format

Share Document