Bank fraud is one of the most threatening problems that any human society is facing due to its destructive effects. This refers to the intentional use of false information to defraud another person or organization of money or assets. The banking industry has used law-based systems for decades to detect fraud and humanly investigate transactions. Law-based systems include algorithms that perform various types of detection operations that are manually written by fraud experts. These systems require manual adjustment of scenarios, which challenge the implicit recognition of trading correlations that point to fraud. Given the inherent weaknesses of the law-based fraud detection approach in banks and the limited data used on commonly supervised machine learning algorithms, there is an urgent need for new fraud detection techniques or systems that can counteract the rapid rise of fraud and money laundering. This research uses a review approach and with the aim of examining the methods of detecting bank fraud, examines the research literature and describes the relevant results.

News reporters are a special group in the jurisprudence and hadith of Shia Islam, whose way of thinking has cast a shadow over the people and seminaries for years. The founder of this sect was Mohammad Amin Esterabadi, who, by residing in Medina and writing the book "Fawaed al-Madaniyah, " spread his beliefs in Iraq and Iran. He considered deduction and ijtihad to be "innovation" and rejected them, believing that only the apparent meaning of hadith should be adhered to and that only the infallible Imams (AS) could interpret the Quran and hadith, which is beyond the capacity of ordinary people. He also disregarded reason and consensus. His opinions were similar to Sunni Ahl al-Hadith to some extent, although he differed from them in terms of belief. The thought of Esterabadi news reporting led to more attention being paid to hadith books, but it resulted in harmful effects such as regression, sectarianism, and superficiality, which hindered rational thinking. Of course, the thought of Esterabadi news reporting was limited and restricted by the efforts of scholars, but its effects can still be seen to some extent: the emergence of modern news reporting in today's world from Egypt to Iraq, Syria, Saudi Arabia, Afghanistan, etc., and cultural news reporting among elites and people are examples of this category.

In each journal, the editorial board receives many articles but more than 70% of them are rejected. This happens because there is no real correlation among the variables in these articles or the variables and perceived relations are fake, which means playing with the variables nonexistent in reality. This rejection occurs mainly as a result of the researchers' misinterpretation of the interdisciplinary studies, their having no expertise in the subject, their paying no attention to co-authorship in different disciplines, and their being fond of the statistical outputs. Furthermore, these researchers have no real acceptable and sensible idea and no valid variable, think in isolate space, have few partners, lack the necessary motivation for research, and have hesitation in delivering their paper, and so on. To conclude, the researchers must concentrate more on the recognition of the differences between aim, necessity and profit, and the importance of prioritization.

Fake news presents a complex regulatory challenge in the increasingly democratized and intermediated on-line information ecosystem. Inaccurate information is readily created by actors with varying goals, rapidly distributed by platforms motivated more by financial incentives than by journalistic norms or the public interest, and eagerly consumed by users who wish to reinforce existing beliefs. Yet even as awareness of the problem grew after the 2016 U. S. presidential election, the meaning of the term “fake news” has become increasingly disputed and diffused. This Article first addresses that definitional challenge, offering a useful taxonomy that classifies species of fake news based on two variables: their creators’ motivation and intent to deceive. In particular, it differentiates four key categories of fake news: satire, hoax, propaganda, and trolling. This analytical framework can provide greater rigor to debates over the issue. Next, the Article identifies key structural problems that make each type of fake news difficult to address, albeit for different reasons. These include the ease with which authors can produce user generated content online and the financial stakes that platforms have in highlighting and disseminating that material. Authors often have a mixture of motives in creating content, making it less likely that a single solution will be effective. Consumers of fake news have limited incentives to invest in challenging or verifying its content, particularly when the material reinforces their existing beliefs and perspectives. Finally, fake news rarely appears alone: it is frequently mingled with more accurate stories, such that it becomes harder to categorically reject a source as irredeemably flawed. Then, the Article classifies existing and proposed interventions based upon the four regulatory modalities catalogued by Larry Lessig: law, architecture (code), social norms, and markets. It assesses the potential and shortcomings of extant solutions. Finally—and perhaps most importantly—the Article offers a set of model interventions, classified under the four regulatory modalities, that can reduce the harmful effects of fake news while protecting interests such as free expression, open debate, and cultural creativity. It closes by assessing these proposed interventions based upon data from the 2020 election cycle.

Objective: This paper aims to provide a detailed overview of the most recent methods and strategies used for detecting fake news, especially in the context of rapid advancements in artificial intelligence and machine learning. With the widespread reach of fake news across social media and other digital platforms, this review focuses on identifying and evaluating effective approaches that can help tackle this growing problem.Methods: Given the importance of detecting fake news, this paper reviews and compares various approaches utilized in this field. To this end, by studying articles published in online libraries and document repositories such as IEEE, Scopus, Elsevier, and others, we first explore different methods for detecting fake news. Then, we compare the various approaches of human-based detection with those of automated detection.Results: The review shows that while conventional techniques like feature extraction and rule-based systems offer a good starting point, they often fall short when dealing with the complexity of modern disinformation. Deep learning models trained on large datasets have demonstrated promising results in detecting fake news, yet they still struggle with the subtlety of human-generated content and real-time applications. This highlights the need for more comprehensive solutions that can address these challenges.Conclusions: The findings suggest that an integrated approach—one that combines language analysis, machine learning, and network-based methods—is essential for building effective fake news detection systems. As the field progresses, future research should focus on improving hybrid models, refining data quality, and incorporating user-centric insights to combat the spread of disinformation better. Combining large language models (LLMs) with context-aware systems offers a promising path for achieving higher precision in detecting both machine-generated and human-created fake news.

The ever - increasing expansion of relations and human contacts in different fields, especially in economy, has caused that monetary and banking dealings also proportionally increase and develop. The emphasis of Islamic jurisprudence on the unlawfulness of usurious transactions among the followers of Islam and it's undisputed reasons has raised this question for many people as to the range of the domain of this prohibition the kinds of transactions and economic relations they can maintain.This paper tries to introduce a way for the researchers at the country's economic and banking field in which cases they can enter usurious transactions, and where they cannot. At the beginning of this paper the writer has tried to explicate the relevant terms in jurisprudence, law and economy.

In Legal Terminology, Collateral is as back of loan repayment or for obligation responsibility. Despite that Collateral in economics has different functions. In this paper we focus on Economic functions of Secured Transaction, and its role in Microeconomics and Macroeconomics. The Economic efficiency of the collateral is cleared by economic analysis and the theory of efficient breach that comprehensive secured transaction is able to make efficiental location of goods and services and finally strong economic functions. In studying this collaterally Institution we get that this formality can effect on positive role in economic development by access to credit easily. So for increasing Economic officiency of secured Transaction, we need to revise our laws and regulations that have been mentioned in international reports about this approach.