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The purpose of this study is to delve into the intricate terrain of assimilating sustainability practices into digital accounting and finance, centring on the transformative dynamics introduced by artificial intelligence (AI)-enabled FinTech. The primary objective is to scrutinize critical lacunae in existing literature, exploring how organizations can meticulously construct comprehensive sustainability frameworks. Simultaneously, the study investigates the protracted repercussions of AI-enabled FinTech on the enduring sustainability paradigms.

Executing a systematic literature review, the research engaged in the meticulous identification and assessment of a voluminous pool of 1,158 articles. Using a judicious two-phase strategy, the scrutiny distilled a mere 64 pertinent articles, subjecting them to rigorous evaluation encompassing methodologies, contributions and overall quality. The Fuzzy Delphi method was used to elicit expert opinions and facilitate consensus-building, leveraging fuzzy logic to accommodate uncertainties in the data.

The review navigates the convoluted impact of AI across diverse sectors, accentuating its transformative imprint on realms such as health care, finance and transportation. Specifically, in the financial domain, the discerning eye of AI-enabled FinTech optimizes investment portfolios, augments risk assessment, propels financial inclusion and streamlines the intricate landscape of sustainability reporting. The study meticulously pinpoints research gaps encompassing investment optimization, risk management, financial inclusion, sustainability reporting and ethical considerations within the intricate milieu of AI-enabled FinTech. This research contributes to the existing body of knowledge by synthesizing intricate thematic strands, discerning overarching trends and spotlighting critical voids in the synthesis of sustainability practices and AI-enabled FinTech. The findings resonate with far-reaching implications, emphasizing the exigency of comprehensive investigations into the longitudinal sustainability ramifications instigated by AI-enabled FinTech.

The study underscores the imperative of crafting robust ethical frameworks for the equitable and transparent deployment of AI solutions within the intricate landscape of FinTech. Moreover, this research stands poised to shape organizational strategies, inform regulatory frameworks and guide investment decisions, thereby catalyzing the cultivation of conscientious and sustainable financial practices.

This study examines the impact of caste on salary levels and job positions within Kerala’s Information Technology sector, aiming to challenge the meritocratic perception of this critical area for India’s economic growth.

The study utilises a dataset of 24,590 employees from 21 IT firms, classified by ownership into Indian and foreign firms. Caste-based disparities are analysed by identifying employees with upper-caste surnames and distinguishing between Kerala Upper Caste and non-Kerala Upper Caste. Generalised Linear Model (GLM) are used to quantify salary disparities and provide deeper insights into how caste, gender and ownership influence salaries.

The findings reveal significant wage gaps, with individuals bearing upper-caste surnames earning more than their non-upper-caste counterparts, especially in Indian-owned firms. Kerala Upper Caste employees enjoy a salary premium, which is reduced in foreign-owned firms. Moreover, upper-caste individuals are likelier to hold senior roles, indicating potential barriers for non-upper-caste employees.

These results highlight the need for targeted policies to address caste-based inequalities, promoting inclusiveness and fairness in the IT workplace. Wage-setting practices and promotion criteria, particularly the recent trend of employee recommendations for recruitment, may risk amplifying existing disparities if not carefully managed. Industry leaders must recognise and mitigate these risks to ensure equitable employment practices. Limitation: The study’s reliance on surname-based caste identification may underestimate the extent of caste disparities. Further, the absence of additional human capital controls, and focus on Kerala limit the generalizability of findings.

This study is the first to analyse caste dynamics within the IT sector using a rich dataset of corporate firms and wages, offering a novel methodological approach to understanding how social identity intersects with economic outcomes.

The purpose of the study is to find out the impact of Digital Financial Inclusion (DFI) on economic growth [(Industrial Production Index (INDP)] of Bangladesh.

Using the monthly data over the period 2018 M12 to 2021 M12, this study applied the Auto-regressive Distributed Lag (ARDL) model to assess the effect of DFI indicators on INDP. The secondary data was collected from the Bangladesh Bank and CEIC Global Economic Data.

The study found that the majority of DFI indicators are positively associated with INDP. From the short-run ARDL, it is seen that one unit positive increase in Point of Sales Transactions (POST) can increase the INDP by 0.055 units. From the long-run ARDL, it is seen that POST and e-commerce transactions (ECOMT) have a significant positive impact, while Automated Teller Machine Transactions (ATMT) have a significant negative effect on INDP. One unit increase in POST and ECOMT increases INDP by 0.13544 and 0.11611 units, respectively.

During the era of the fourth industrial revolution, the findings will be beneficial for policymakers, financial technology service providers, manufacturers, consumers, corporations and investors as they pave the way for a more inclusive approach to financial transactions for economic growth.

The study’s novelty is that it explored the influential DFI indicators and shed light on both short-run and long-run relationships between the indicators and macro-economy from the context of a developing nation.

The peer review history for this article is available at: https://publons.com/publon/10.1108/IJSE-04-2023-0306