Modern society depends on IT services, but unfortunately, IT services are not always dependable. Cyber incidents occur all the time, caused by bad design or by bad incentives. To address the latter cause, disclosure of cyber incidents has been proposed. Learning about incidents, buyers will find it worthwhile to select and pay for secure vendors, thus contributing to better overall security. While this logic has solid theoretical foundations in the economics of negative externalities and asymmetric information, the practice of cyber incident disclosure is only just emerging, as is empirical research on its effects. However, valuable lessons might be learned from the literature on the more mature practice of CO2e emissions disclosure. Based on the extant literature on CO2e emissions disclosure, two hypotheses about cyber incident disclosure are derived: First, it is likely that increased cyber incident disclosure would increase the costs of equity and debt for companies with many and/or severe cyber incidents, and also expose them to shareholder activism as well as to decreasing demand. Second, these effects will be smaller for cyber incident disclosure than the corresponding effects for CO2e emissions disclosure. The article is concluded with a discussion of implications and future work.

Continuous auditing research has grappled with the challenge of managing the abundance of detected exceptions in internal audit applications for the past 30 years. A key issue in continuous auditing involves the uncontrolled proliferation of exceptions, where the sheer volume makes manual follow-up impractical, undermining the viability of the technology. The root cause of this problem is the combination of strong class imbalance and the predominant rule-based systems design. Prior investigations have attempted ad hoc remedies like introducing additional layers to prioritize the most suspicious exceptions or aggregating data. Currently, there is no universal method to address this prioritization challenge, leaving internal auditors without a means to focus specifically on exceptions most likely to represent genuine faults. Our research explores the origin of this prioritization dilemma and proposes a systems design that can deal appropriately with class imbalance. This solution allows full control of the exception volume by a simple approach in machine learning called thresholding and combined with methods to interpret the output of a continuous auditing system our design effectively focuses the internal auditors' attention on the most significant exceptions. We discuss the implications of thresholding for practice and the literature.

Sustainability reporting standards state that material information should be disclosed, but materiality is not easily nor consistently defined across companies and sectors. Research finds that materiality assessments by reporting companies and sustainability auditors are uncertain, discretionary, and subjective. This chapter investigates a machine learning approach to sustainability reporting materiality assessments that has predictive validity. The investigated assessment methodology provides materiality assessments of disclosed as well as non-disclosed sustainability items consistent with the impact materiality GRI (Global Reporting Initiative) reporting standard. Our machine learning model estimates the likelihood that a company fully complies with environmental responsibilities. We then explore how a state-of-the-art model interpretation method, the SHAP (SHapley Additive exPlanations) developed by Lundberg and Lee (A unified approach to interpreting model predictions. Advances in Neural Information Processing Systems, 2017-December, pp 4766–4775, 2017), can be used to estimate impact materiality. 

Big data and analytics offer new opportunities and challenges for academics and practitioners in all business disciplines including accounting and auditing. In the backdrop of increasing growth of emerging technologies, the organizations in public, private and not-for-profit sectors are embracing digital economy and the fourth industrial revolution journey. This requires knowledge of better practice examples, lessons learned and future directions in addressing the new challenges and seizing new opportunities. In this chapter, we discuss the implications of data analytics, artificial intelligence and machine learning on the accounting and auditing practices. We focus on the technological, social, political, economic, institutional, and behavioral aspects of these technologies in the public, private, non-governmental and hybrid contexts. We present state-of-the-art research directions on philosophical, theoretical, methodological, and practical issues, new developments and innovations of big data, analytics, artificial intelligence, machine learning, blockchain, cryptocurrencies and other emerging technologies related to accounting and auditing.

Purpose: The purpose of this study is to develop a method to assess social performance. Traditionally, environment, social and governance (ESG) rating providers use subjectively weighted arithmetic averages to combine a set of social performance (SP) indicators into one single rating. To overcome this problem, this study investigates the preconditions for a new methodology for rating the SP component of the ESG by applying machine learning (ML) and artificial intelligence (AI) anchored to social controversies. Design/methodology/approach: This study proposes the use of a data-driven rating methodology that derives the relative importance of SP features from their contribution to the prediction of social controversies. The authors use the proposed methodology to solve the weighting problem with overall ESG ratings and further investigate whether prediction is possible. Findings: The authors find that ML models are able to predict controversies with high predictive performance and validity. The findings indicate that the weighting problem with the ESG ratings can be addressed with a data-driven approach. The decisive prerequisite, however, for the proposed rating methodology is that social controversies are predicted by a broad set of SP indicators. The results also suggest that predictively valid ratings can be developed with this ML-based AI method. Practical implications: This study offers practical solutions to ESG rating problems that have implications for investors, ESG raters and socially responsible investments. Social implications: The proposed ML-based AI method can help to achieve better ESG ratings, which will in turn help to improve SP, which has implications for organizations and societies through sustainable development. Originality/value: To the best of the authors’ knowledge, this research is one of the first studies that offers a unique method to address the ESG rating problem and improve sustainability by focusing on SP indicators. 

We develop a new methodology for computing environmental, social, and governance (ESG) ratings using a mode of artificial intelligence (AI) called machine learning (ML) to make ESG more transparent. The ML algorithms anchor our rating methodology in controversies related to non-compliance with corporate social responsibility (CSR). This methodology is consistent with the information needs of institutional investors and is the first ESG methodology with predictive validity. Our best model predicts what companies are likely to experience controversies. It has a precision of 70–84 per cent and high predictive performance on several measures. It also provides evidence of what indicators contribute the most to the predicted likelihood of experiencing an ESG controversy. Furthermore, while the common approach of rating companies is to aggregate indicators using the arithmetic average, which is a simple explanatory model designed to describe an average company, the proposed rating methodology uses state-of-the-art AI technology to aggregate ESG indicators into holistic ratings for the predictive modelling of individual company performance. Predictive modelling using ML enables our models to aggregate the information contained in ESG indicators with far less information loss than with the predominant aggregation method.

We use machine learning with a cross-sectional research design to predict governance controversies and to develop a measure of the governance component of the environmental, social, governance (ESG) metrics. Based on comprehensive governance data from 2,517 companies over a period of 10 years and investigating nine machine-learning algorithms, we find that governance controversies can be predicted with high predictive performance. Our proposed governance rating methodology has two unique advantages compared with traditional ESG ratings: it rates companies' compliance with governance responsibilities and it has predictive validity. Our study demonstrates a solution to what is likely the greatest challenge for the finance industry today: how to assess a company's sustainability with validity and accuracy. Prior to this study, the ESG rating industry and the literature have not provided evidence that widely adopted governance ratings are valid. This study describes the only methodology for developing governance performance ratings based on companies' compliance with governance responsibilities and for which there is evidence of predictive validity.

Institutional investors seek to make environmentally sustainable investments using environment, social, governance (ESG) ratings. Current ESG ratings have limited validity because they are based on idiosyncratic scores derived using subjective, discretionary methodologies. We discuss a new direction for developing corporate environmental performance (CEP) ratings and propose a solution to the limited validity problem by anchoring such ratings in environmental controversies. The study uses a novel machine learning approach to make the ratings more comprehensive and transparent, based on a set of algorithmic approaches that handle nonlinearity when aggregating ESG indicators. This approach minimizes the rater subjectivity and preferences inherent in traditional ESG indicators. The findings indicate that controversies as proxies for non-compliance with environmental responsibilities can be predicted well. We conclude that environmental performance ratings developed using our machine learning framework offer predictive validity consistent with institutional investors’ demand for socially responsible investment screening.