The growing use of AI-generated content (AIGC) is creating new challenges for recognizing revenue under the accounting standards ASC 606 and IFRS 15. These rules were designed around the idea that companies can clearly identify what they are promising to deliver (their performance obligations), that there are enforceable rights and responsibilities between the parties, and that each promised item or service has a reasonably clear stand-alone selling price. However, AI-powered production settings do not fully conform to these assumptions. Generative systems operate at low to no marginal cost, are based on algorithmic and usage-based pricing models, and the intellectual property status of their outputs has not yet been legally established.

This paper presents a conceptual stress test of the five-step revenue model in AI-intensive environments. We argue that AI does not undermine the control-based structure of ASC 606 and IFRS 15; rather, it weakens the traditional evidentiary anchors that historically disciplined stand-alone selling price (SSP) estimation and transaction price allocation. As marginal cost ceases to function as an observable indicator of economic value, allocation decisions become increasingly estimation-based, expanding managerial discretion and increasing variability in reported revenues.

To overcome these tensions, we propose the AI Value Attribution Framework (AVAF), which is an interpretive clarification based on the existing Market Assessment Approach. AVAF emphasizes economic utility benchmarking because marginal cost is no longer a significant valuation signal. An illustrative example shows how the framework leads to improved representational faithfulness without the need to alter the existing standards. In conclusion, we suggest that additional clarification of SSP benchmarking and more AI-related disclosures would allow the maintenance of comparability and the minimization of the possible risk of economically biased reporting in AI-intensive industries.

Keywords: Artificial Intelligence; Revenue Recognition; ASC 606; IFRS 15; Stand-Alone Selling Price; Earnings Management.

The sweeping revolution in the financial technology space has significantly altered the hitherto traditional financial architecture, bringing in new diversified products and services, with efficient and innovative financial service delivery Against this premise, this study empirically investigates the effect of financial technology (i.e. fintech) on performance of listed banks in Nigeria for the period 2014-2024. Data for the ten biggest banks by total asset size in the Nigerian banking industry were utilized. Four major variables that capture innovations in fintech as the explanatory variables, internet banking, ATM, mobile banking, as welll as POS, were used, and Return on asset (ROA) is used to capture the dependent variable. The panel least squares estimation technique was utilized in the analysis. The findings indicate that financial technology has a positive and significant impact on bank performance in Nigeria. Specifically, internet banking services, mobile banking and ATM have positive and significant impact on ROA of banks in Nigeria. The evidence further show that POS service is positively related to ROA but not significant. Based on the findings, it is suggested that increased digital technology be employed in financial services in order to enhance bank financial performance in Nigeria.

Keywords: Financial technology, Digital revolution, Financial performance, innovations, Panel Least Squares.

With the help of a comparative systematic review of the literature, this paper determines structural design as the primary cause of pipeline problems and suggests hybrid solutions that can enhance access and retention. The developed economies continue to struggle with accounting talent supply, equity, and professional sustainability. This research paper compares professional accounting careers in the United States and the United Kingdom to learn how credential sequencing, time-to-earning, and risk allocation impact student participation, persistence, and equity outcomes. The U.S. (United States) system of front-loading has the strength of being technically rigorous, yet it carries a high opportunity cost that is disproportionately borne by underrepresented students. The U.K. (United Kingdom) paradigm of concurrent education and professional training also alters the allocation of risk--sharing of financial and career-related risks between students and employers--to initiate and support participation and retention, enhance signaling, and enable early development of professional identity. These findings suggest that the problem of the pipeline in the United States is more an issue of institutional design than professional quality deterioration, and that a solution might be found in mixed methods providing intensive education with early exposure to the profession to improve access and retention.

Keywords: Accounting talent pipeline, Credential sequencing, Opportunity cost, Professional socialization, United States, United Kingdom.

The prime objective of the present paper is to investigate whether an impact of the Environmental, Social and Governance (ESG) score on the financial performance of the Indian companies exists. In this study, 144 companies out of 225 companies whose ESG scores were published by CRISIL in June, 2021 for the period 2021-2022 have been taken as the sample companies. The influence of ESG scores of these companies on their financial performances have been examined using cross-sectional regression technique. The study reveals thata noticeable degree of positive impact of the ESG scores of the sample companies on their financial performance is present. The outcomes derived from the studywill help the investors in making their investment decisions and will also provide certain inputs to the companies which could be used by them while making their financing decisions.

Keywords: Environmental, Social and Governance (ESG) score, ESG activities, Corporate Social Responsibility (CSR), Financial Performance, and Cross-Sectional Regression Model.