Detecting Deception in Asynchronous Text

Glancy and Yadav (2010) developed a computational fraud detection model (CFDM) that successfully detected financial reporting fraud in the text of the management’s discussion and analysis (MDA) portion of annual filings with the United States Securities and Exchange Commission (SEC). This work extends the use of the CFDM to additional genres, demonstrates the generalizability of the CFDM and the use of text mining for quantitatively detecting deception in asynchronous text. It also demonstrates that writers committing fraud use words differently from truth tellers

FINANCIAL STATEMENT FRAUD DETECTION USING TEXT MINING: A SYSTEMIC FUNCTIONAL LINGUISTICS THEORY PERSPECTIVE

Fraudulent financial information made by public companies not only cause significant financial loss to broad shareholders but also result in a great loss of confidence to capital market. Conventional auditing practices, which primarily focus on statistical analysis of structured financial ratios in auditing process, work not so well with the presence of misleading financial reports. This research tries to tap the power of huge amount of largely ignored textual contents in financial statements. With the theoretical guidance of Systemic Functional Linguistics theory (SFL), we develop a systematic text analytic framework for financial statement fraud detection. Seven information types, i.e., topics, opinions, emotions, modality, personal pronouns, writing style, and genres are identified based on ideational, interpersonal, and textual metafunctions in SFL. Under the analytic framework, Latent Dirichlet Allocation algorithm, computational linguistics, term frequency-inverse document frequency method, are integrated to create a synergy for extracting both word-level and document-level features. All these features serve as the input of Liblinear Support Vector Machine classifier. Finally, with application to detect fraud in 1610 firm-year samples from U.S. listed companies, the analytic framework makes a classification with average accuracy at 82.36% under ten-fold cross validation, much better than baseline method using financial ratios

The Dark Side of Dark Mode: How Does Screen Display Mode Affect Financial Crimes

There is an emerging trend in digital interface design to include the dark mode (i.e., font in white against a dark background) in addition to the traditional default light mode (i.e., font in black against a white background). While this innovation was motivated by usability considerations, it is unknown whether and how different screen display modes can influence user behaviours. Drawing on the findings from environmental psychology, we propose that screen display mode can influence users’ moral decision making. Specifically, we focus on users’ decisions to conduct financial crimes and predict that users are more likely to conduct financial crimes when using dark (vs. light) mode. We propose perceived anonymity as the underlying mechanism and theorize the moderating effect of screen size. Two laboratory experiments were designed to test on two financial crimes, namely, insurance fraud and insider trading. The potential theoretical and practical contributions are discussed

Agent based modelling as a decision support system for shadow accounting

We propose the use of agent based modelling to create a shadow account, that is, a secondary account of a business which is used to audit or verify the primary ac¬count. Such a model could be used to test the claims of industries and businesses. For example, the model could determine whether a business is generating enough funds to pay minimum wage. Parameters in the model can be set by observation or a range of values can be tested to determine points at which enough revenue could be generated. We illustrate the potential of agent based modelling as a tool for shadow accounting with a case study of a car wash business

A New Hybrid Method For Credit Card Fraud Detection On Financial Data

Credit card fraud is a major issue in financial administrations. Billions of dollars are lost because of credit card misrepresentation consistently. There is an absence of research contemplates on breaking down certifiable Visa information attributable to privacy issues. In this paper, AI algorithms are utilized to identify Visa misrepresentation. Standard models are right off the bat utilized. At that point, half breed strategies which use AdaBoost and greater part casting ballot techniques are connected. To assess the model adequacy, a freely accessible credit card informational collection is utilized. At that point, a genuine Visa informational index from a money related organization is investigated. What's more, commotion is added to the information tests to further survey the robustness of the algorithms

A novel hybrid mechanism for credit card fraud detection on financial data

Credit card fraud is a difficult issue in budgetary services. Billions of dollars are lost because of credit card fraud consistently. There is an absence of research thinks about on investigating genuine Master card data inferable from secrecy issues. In this project, machine learning algorithms are used to recognize credit card fraud. Standard models are right off the bat used. At that point, half and half techniques which use AdaBoost and larger part casting a voting method are connected. To assess the model viability, a freely credit card data collection is used. Then, a real-world credit card data set from a financial institution is analyzed

THE DETECTION OF FRAUDULENT FINANCIAL STATEMENTS: AN INTEGRATED LANGUAGE MODEL

Among the growing number of Chinese companies that went public overseas, many have been detected and alleged as conducting financial fraud by market research firms or U.S. Securities and Exchange Commission (SEC). Then investors lost money and even confidence to all overseas-listed Chinese companies. Likewise, these companies suffered serious stock sank or were even delisted from the stock exchange. Conventional auditing practices failed in these cases when misleading financial reports presented. This is partly because existing auditing practices and academic researches primarily focus on statistical analysis of structured financial ratios and market activity data in auditing process, while ignoring large amount of textual information about those companies in financial statements. In this paper, we build integrated language model, which combines statistical language model (SLM) and latent semantic analysis (LSA), to detect the strategic use of deceptive language in financial statements. By integrating SLM with LSA framework, the integrated model not only overcomes SLM’s inability to capture long-span information, but also extracts the semantic patterns which distinguish fraudulent financial statements from non-fraudulent ones. Four different modes of the integrated model are also studied and compared. With application to assess fraud risk in overseas-listed Chinese companies, the integrated model shows high accuracy to flag fraudulent financial statements

Artificial Intelligence & Machine Learning in Finance: A literature review

In the 2020s, Artificial Intelligence (AI) has been increasingly becoming a dominant technology, and thanks to new computer technologies, Machine Learning (ML) has also experienced remarkable growth in recent years; however, Artificial Intelligence (AI) needs notable data scientist and engineers’ innovation to evolve. Hence, in this paper, we aim to infer the intellectual development of AI and ML in finance research, adopting a scoping review combined with an embedded review to pursue and scrutinize the services of these concepts. For a technical literature review, we goose-step the five stages of the scoping review methodology along with Donthu et al.’s (2021) bibliometric review method. This article highlights the trends in AI and ML applications (from 1989 to 2022) in the financial field of both developed and emerging countries. The main purpose is to emphasize the minutiae of several types of research that elucidate the employment of AI and ML in finance. The findings of our study are summarized and developed into seven fields: (1) Portfolio Management and Robo-Advisory, (2) Risk Management and Financial Distress (3), Financial Fraud Detection and Anti-money laundering, (4) Sentiment Analysis and Investor Behaviour, (5) Algorithmic Stock Market Prediction and High-frequency Trading, (6) Data Protection and Cybersecurity, (7) Big Data Analytics, Blockchain, FinTech. Further, we demonstrate in each field, how research in AI and ML enhances the current financial sector, as well as their contribution in terms of possibilities and solutions for myriad financial institutions and organizations. We conclude with a global map review of 110 documents per the seven fields of AI and ML application.   Keywords: Artificial Intelligence, Machine Learning, Finance, Scoping review, Casablanca Exchange Market. JEL Classification: C80 Paper type: Theoretical ResearchIn the 2020s, Artificial Intelligence (AI) has been increasingly becoming a dominant technology, and thanks to new computer technologies, Machine Learning (ML) has also experienced remarkable growth in recent years; however, Artificial Intelligence (AI) needs notable data scientist and engineers’ innovation to evolve. Hence, in this paper, we aim to infer the intellectual development of AI and ML in finance research, adopting a scoping review combined with an embedded review to pursue and scrutinize the services of these concepts. For a technical literature review, we goose-step the five stages of the scoping review methodology along with Donthu et al.’s (2021) bibliometric review method. This article highlights the trends in AI and ML applications (from 1989 to 2022) in the financial field of both developed and emerging countries. The main purpose is to emphasize the minutiae of several types of research that elucidate the employment of AI and ML in finance. The findings of our study are summarized and developed into seven fields: (1) Portfolio Management and Robo-Advisory, (2) Risk Management and Financial Distress (3), Financial Fraud Detection and Anti-money laundering, (4) Sentiment Analysis and Investor Behaviour, (5) Algorithmic Stock Market Prediction and High-frequency Trading, (6) Data Protection and Cybersecurity, (7) Big Data Analytics, Blockchain, FinTech. Further, we demonstrate in each field, how research in AI and ML enhances the current financial sector, as well as their contribution in terms of possibilities and solutions for myriad financial institutions and organizations. We conclude with a global map review of 110 documents per the seven fields of AI and ML application.   Keywords: Artificial Intelligence, Machine Learning, Finance, Scoping review, Casablanca Exchange Market. JEL Classification: C80 Paper type: Theoretical Researc