Intelligent Systems in Accounting, Finance and Management最新文献

The use of Robotic Process Automation (RPA) is rapidly growing in the professional services sector such as auditing. Despite the great benefits RPA can offer, RPA failure rates are still high. In this research, we draw on socio-technical systems theory to examine the challenges of using RPA in the various phases of auditing and how auditors address these challenges. By interviewing experienced partners and auditors in auditing firms and technology companies, we show that challenges arise when there is a lack of fit between the technical requirements of the RPA tool (technical subsystem) and the skills and knowledge of the auditors and clients (social subsystem). We discuss our findings and provide valuable practical implications and opportunities for future research.

Google, Facebook, OpenAI, and others have released access to versions of language chatbots that they have developed. These chatbots have been trained on massive amounts of text using neural networks for language processing. Using an approach similar to security penetration testing, this paper investigates and compares three different chatbots, assessing potential strengths and limitations of these systems. The paper presents several findings, including a comparison of those systems across answers to common questions, an analysis of the use of names and activities to guide discussion in two systems, an analysis of the extent of differences in responses arising from “regeneration” of a question, the determination of a weakness in a system of knowing “who” invented something, development of a potential new subfield, sensitive topic classifiers, and an analysis of some of the implications of these findings. As part of this analysis, I find emerging topics in chatbots, such as “topic stalemate” and the use of sensitive topic classifiers.

Wild price fluctuations of cryptocurrencies make it difficult for investors to maintain stable asset values. This study investigates the hedging properties of US dollar (USD)-pegged stablecoins against bitcoin returns. We analyzed the hedging abilities of the three largest stablecoins—namely, Tether, USD Coin, and Binance USD—using the dynamic conditional correlation–generalized autoregressive conditional heteroskedasticity, dummy variable regression, vector autoregression, and impulse response functions. We found that stablecoins are generally negatively correlated with bitcoin returns, indicating that they can be effective hedging instruments against high-volatility crypto assets. Among the stablecoins, Binance USD offers the largest risk reduction, and Tether was a weak safe haven during the COVID-19 crisis period. Crypto investors can diversify their portfolios by holding stablecoins.

In recent years, many initial coin offerings (ICOs) scams have been reported, attracting attention to this relatively new and unregulated ICO market, which lacks disclosure requirements and therefore suffers from intensifying problems of information asymmetry inherent in crowdfunding. As a prospectus-type document, an ICO white paper serves as a major means of voluntary disclosure practices adopted by ventures seeking external financing. Given the importance of an ICO white paper and the difficulty of assessing its quality, we propose to benchmark it against white paper content for security token offerings (STOs)—a more regulated ICO subset. Using the similarity of ICO white papers with STO white papers to proxy for disclosure quality, we document that the ICO campaigns that have white papers more similar to STO white papers are more likely to raise funding successfully. Our findings provide implications for policymakers, ICO fundraisers, and investors on the importance of white paper quality.

This paper provides some basic definitions associated with digital transformation in organizations and applies those definitions to accounting, electronic commerce, and supply chains. I also drill down on the dimensions associated with digital transformation, including digital everywhere, integration (across applications and with customers and partners), and the need to reengineer processes. I examine several examples of processes ranging from digitization to digital transformation. I also examine the role of people in digitally transformed organizations and some technologies that are important to continued evolution of digitally transformed organizations. Further, we explore a number of scenarios of digital transformation. Finally, these investigations result in the determination of a number of emerging research issues.

This study conducts a pilot test analyzing reports from New York, New Jersey, and California and uses textual analytics to reengineer government performance audit reporting. It advocates a performance audit database that can facilitate easier access and extract relevant information from lengthy reports in a timely manner. The study presents a framework to identify the commonalities and differences in terminologies used by sampled states, evaluates and extracts relevant content from the reports according to Generally Accepted Government Auditing Standards requirements, and constructs a taxonomy specific to government performance audits. Furthermore, this study investigates the disclosure quality by examining linguistic and similarity features, such as report length, specificity, readability, comprehensibility, and content similarity. This paper raises attention to a key legislative task that requires reporting reforms.

The collaborative filtering (CF) recommendation algorithm predicts the purchases of specific users based on their characteristics and purchase history. This study empirically analyzes the possibility of applying CF to the insurance industry using real customer data from South Korea. Using three different CF models, we examined the relevance of applying the CF model to insurance products under various situations by comparing them with logistic-regression-based recommendation models. Through experiments, we empirically show that CF models apply to the insurance industry, especially when customer purchase information is added to the model.