This study uses publicly available information for European firms and recent machine learning algorithms to predict future revenues in an IFRS context, examining the benefits of predictive analytics for both preparers and users of these financial projections. For this purpose, the study evaluates the prediction quality of the forecasting models applied and compares them with each other and with the prediction quality of sell-side financial analysts’ forecasts. Our empirical results, based on 3,000 firm-year observations from 2010 to 2019, demonstrate that machine learning provides comparably accurate or even more accurate revenue forecasts than financial analysts. Therefore, the study highlights the considerable potential of machine learning and predictive analytics for improving the forecasting process in general and, in particular, to increase the accuracy, transparency, and objectivity of the forecasts. Since the latter also reduce information asymmetry between firms and investors, machine learning and predictive analytics contribute to capital market efficiency.

Drug discovery is usually a rule-based process that is carefully carried out by pharmacists. However, a new trend is emerging in research and practice where artificial intelligence is being used for drug discovery to increase efficiency or to develop new drugs for previously untreatable diseases. Nevertheless, so far, no study takes a holistic view of AI-based drug discovery research. Given the importance and potential of AI for drug discovery, this lack of research is surprising. This study aimed to close this research gap by conducting a bibliometric analysis to identify all relevant studies and to analyze interrelationships among algorithms, institutions, countries, and funding sponsors. For this purpose, a sample of 3884 articles was examined bibliometrically, including studies from 1991 to 2022. We utilized various qualitative and quantitative methods, such as performance analysis, science mapping, and thematic analysis. Based on these findings, we furthermore developed a research agenda that aims to serve as a foundation for future researchers.

Climate-related reporting has become an integral part of firms’ disclosure. In this context, firms’ greenhouse gas (GHG) emissions are of major importance to stakeholders and management. For measuring GHG emissions, a global standard has been established with the GHG Protocol. This standard contains an important accounting policy option that significantly affects firms’ reported emissions by allowing them to use different consolidation approaches: the equity share, operational control, and financial control approach. However, there is limited evidence on firms’ use of these approaches, resulting in a lack of foundation for discussing the approaches’ sufficiency to support achieving environmental sustainability. Therefore, this paper aims to close this research gap by empirically investigating the approaches’ relevance using 16,604 firm-year observations between 2009 and 2019. We demonstrate that the operational control approach is used by most firms and that its predominance substantially increased during the last decade. However, the predominant use of the operational control approach is not fully compatible with societal and political sustainability goals as expressed in recent sustainability regulations. Therefore, policy makers need to critically assess whether current GHG reporting supports achieving their goals. Furthermore, we develop a research agenda to encourage future researchers to contribute to improvements in GHG reporting.

After cardiovascular diseases, cancer is responsible for the most deaths worldwide. Detecting a cancer disease early improves the chances for healing significantly. One group of technologies that is increasingly applied for detecting cancer is artificial intelligence. Artificial intelligence has great potential to support clinicians and medical practitioners as it allows for the early detection of carcinomas. During recent years, research on artificial intelligence for cancer detection grew a lot. Within this article, we conducted a bibliometric study of the existing research dealing with the application of artificial intelligence in cancer detection. We analyzed 6450 articles on that topic that were published between 1986 and 2022. By doing so, we were able to give an overview of this research field, including its key topics, relevant outlets, institutions, and articles. Based on our findings, we developed a future research agenda that can help to advance research on artificial intelligence for cancer detection. In summary, our study is intended to serve as a platform and foundation for researchers that are interested in the potential of artificial intelligence for detecting cancer.

PurposeThe accurate prediction of incoming cash flows enables more effective cash management and allows firms to shape firms' planning based on forward-looking information. Although most firms are aware of the benefits of these forecasts, many still have difficulties identifying and implementing an appropriate prediction model. With the rise of machine learning algorithms, numerous new forecasting techniques have emerged. These new forecasting techniques are theoretically applicable for predicting customer payment behavior but have not yet been adequately investigated. This study aims to close this research gap by examining which machine learning algorithm is the most appropriate for predicting customer payment dates.Design/methodology/approachBy using various machine learning algorithms, the authors evaluate whether customer payment behavior patterns can be identified and predicted. The study is based on real-world transaction data from a DAX-40 firm with over 1,000,000 invoices in the dataset, with the data covering the period 2017–2019.FindingsThe authors' results show that neural networks in particular are suitable for predicting customers' payment dates. Furthermore, the authors demonstrate that contextual and logical prediction models can provide more accurate forecasts than conventional baseline models, such as linear and multivariate regression.Research limitations/implicationsFuture cash flow forecasting studies should incorporate naïve prediction models, as the authors demonstrate that these models can compete with conventional baseline models used in existing machine learning research. However, the authors expect that with more in-depth information about the customer (creditworthiness, accounting structure) the results can be even further improved.Practical implicationsThe knowledge of customers' future payment dates enables firms to change their perspective and move from reactive to proactive cash management. This shift leads to a more targeted dunning process.Originality/valueTo the best of the authors' knowledge, no study has yet been conducted that interprets the prediction of incoming payments as a daily rolling forecast by comparing naïve forecasts with forecasts based on machine learning and deep learning models.