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This study aims to identify and gauge the sustainability indicators (SUSIs) for sustainable Hydroelectric Power (HEP) project development. It examines major SUSIs under the social, economic and environmental (SEE) fronts and categorizes them under push and pull impacts which helps to identify challenges and opportunities associated with projects. Additionally, the study calculates an empirical sustainability index (SI) to assess the sustainability level of HEP. Finally, the study suggests mitigation measures across stakeholders, which will optimize government/developer/investor investments.

This paper examines the interaction of sustainable HEP development with SUSIs using Uttarakhand as a study area. Additionally, SI has been developed quantitatively. For the indicator classification, the authors conducted a literature review and secondary survey of all affected parties, including investors, developers, NGOs and villagers. The fuzzy logic theory (FLT) is used to determine the SI of the study area and classify projects in their level of sustainability. On the basis of expert opinion and literature review, mitigation measures are proposed across stakeholders.

The authors found that there is a mixed effect of SUSIs on HEP development across various projects in Uttarakhand. Furthermore, the authors suggest that index-based assessment and planned collaboration play a significant role in sustainable HEP development. Mitigation measures should be suggested to all affected stakeholders based on specific project issues, i.e. collaborations, training, public awareness campaigns, and initiatives by the government that would improve sustainability conditions.

In addition to supporting the ongoing and upcoming initiatives launched by the Government of India, including the Green Energy Corridor, independent power producers (IPPs); and the India-Renewable Resources Development Project with IDA and participates in Net zero target.

The structured, sustainable HEP planning suggested in the study will help to conserve society, economy, save resources and in parallel reduce the cost and time of developers and policymakers. This will also help to improve the socioeconomic status of the villagers and prolong the life of the project.

The innovative SI-based push-pull approach identifies a sustainable HEP project planning.

The study aims to identify the severe socioeconomic, environmental, and ecological impacts caused by the construction of mega and large hydro-power plants in Uttarakhand, India. In addition to identifying the attributes, the study creates an integrated index that will assist in the development of sustainable hydro-power.

The methodology used for this impact identification was based on extensive literature review, focused expert discussions and further validation through a primary survey among the stakeholders in the hydropower sector. The sustainability index (SI) was estimated using the fuzzy logic theory.

The study area SI shows that few projects are in extreme zones, and through suggestive measures, few project sites can be made viable for long-term sustainable project site. A Hydropower Sustainability Assessment Protocol–based conceptual model is also proposed for mitigation of impacts.

Hydropower plays an essential role in access to cleaner and cheaper sources of energy; it defines the usage of water resources toward inflation-free green energy and holds spectacular operational flexibility. Despite the significant advantages associated with hydroelectric power projects, there are adverse side effects as well. The water-based power sector industry contributes to any nation through both economic and environmental ways. Although one-third of the power business in India is carried out through water-based hydropower projects, recent trends in water-based hydropower projects show significant socioeconomic and environmental impacts that create a debate about the sustainability of these projects.

The study aims to overview the different types of internal-led cyber fraud that have gained mainstream attention in recent major-value fraud events involving prominent Indian banks. The authors attempted to identify and classify cyber frauds and its drivers and correlate them for optimal mitigation planning.

The methodology opted for the identification and classification is through a detailed literature review and focus group discussion with risk and vigilance officers and cyber cell experts. The authors assessed the future of cyber fraud in the Indian banking business through the machine learning–based k-nearest neighbor (K-NN) approach and prioritized and predicted the future of cyber fraud. The predicted future revealing dominance of a few specific cyber frauds will help to get an appropriate fraud prevention model, using an associated parties centric (victim and offender) root-cause approach. The study uses correlation analysis and maps frauds with their respective drivers to determine the resource specific effective mitigation plan.

Finally, the paper concludes with a conceptual framework for preventing internal-led cyber fraud within the scope of the study. A cyber fraud mitigation ecosystem will be helpful for policymakers and fraud investigation officers to create a more robust environment for banks through timely and quick detection of cyber frauds and prevention of them.

Additionally, the study supports the Reserve Bank of India and the Government of India's launched cyber security initiates and schemes which ensure protection for the banking ecosystem i.e. RBI direct scheme, integrated ombudsman scheme, cyber swachhta kendra (botnet cleaning and malware analysis centre), National Cyber Coordination Centre (NCCC) and Security Monitoring Centre (SMC).

Structured and effective internal-led plans for cyber fraud mitigation proposed in this study will conserve banks, employees, regulatory authorities, customers and economic resources, save bank authorities’ and policymakers’ time and money, and conserve resources. Additionally, this will enhance the reputation of the Indian banking industry and extend its lifespan.

The innovative insider-led cyber fraud mitigation approach quickly identifies cyber fraud, prioritizes it, identifies its prominent root causes, map frauds with respective root causes and then suggests strategies to ensure a cost-effective and time-saving bank ecosystem.

This paper aims to focus on the different types of insider-led cyber frauds that gained mainstream attention in recent large-scale fraud events involving prominent Indian banking institutions. In addition to identifying and classifying cyber fraud, the study maps them on a severity scale for optimal mitigation planning.

The methodology used for identification and classification is an analysis of a detailed literature review, a focus group discussion with risk and vigilance officers and cyber cell experts, as well as secondary data of cyber fraud losses. Through machine learning-based random forest, the authors predicted the future of insider-led cyber frauds in the Indian banking business and prioritized and predicted the same. The projected future reveals the dominance of a few specific cyber frauds, which will make it easier to develop a fraud mitigation model based on a victim-centric approach.

The paper concludes with a conceptual framework that can be used to ensure a sustainable cyber fraud mitigation ecosystem within the scope of the study. By using the findings of this research, policymakers and fraud investigators will be able to create a more robust environment for banks through timely detection of cyber fraud and prevent it appropriately before it happens.

The study focuses on fraud, risk and mitigation from a victim-centric perspective and does not address it from the fraudster’s perspective. Data availability was a challenge. Banks are recommended to compile data that can be used for analysis both by themselves and other policymakers.

The structured, sustainable cyber fraud mitigation suggested in the study will provide an agile, quick, proactive, stakeholder-specific plan that helps to safeguard banks, employees, regulatory authorities, customers and the economy. It saves resources, cost and time for bank authorities and policymakers. The mitigation measures will also help improve the reputational status of the Indian banking business and prolong the banks’ sustenance.

The innovative cyber fraud mitigation approach contributes to the sustainability of a bank’s ecosystem quickly, proactively and effectively.

This study aims to gauge the effect of rural–urban migration and its challenges on the urban development of Bengaluru. This study examines the driving forces behind urbanization and its impact on social, economic and environment areas. The research helps to establish the sustainable city planning, after evaluation of specific challenges of zones, and this mitigation will optimize government investment and reduce cost.

Bengaluru is used as a study area to examine the interaction of migration and urban development. The study covers the period between 2011 and 2019. Panel data analysis is applied to measure the effect of urban development indicators on urban migration. The authors applied the integrated urban metabolism analysis tool to quantify the urban development indicators and identified the most critical areas for migrants. Later, authors proposed mitigation measures based on alternate scenario approach.

The authors found that there is a mixed effect of urban migration on urban development across various zones in Bengaluru. Besides, the authors suggest how planned collaboration should play a significant role in urban planning and optimize city planning judiciously. Effective mitigation measures should be developed based on zone-specific core issues, and practical trainings, research, public awareness campaigns and skill up-gradation of migrants would enhance the socio-economic and environmental conditions.

The study will support the ongoing and upcoming initiatives launched by the Government of India i.e. Awas Yojna, Atmanirbhar Bharat and Swach Bharat.

The structured city planning suggested in the study will help to save wastage of resources and cost and time of developers and policymakers. This will also help to upgrade the status of migrants and enhance the ambience of city around social, economic and environment fronts.

The first of its kind of innovative model mapped in the study area establishes a link between strategic city planning under rural–urban migration and urban development.

This study addresses the ever-increasing cyber risks confronting the global banking sector, particularly in India, amid rapid technological advancements. The purpose of this study is to de velop an innovative cyber fraud (CF) response system that effectively controls cyber threats, prioritizes fraud, detects early warning signs (EWS) and suggests mitigation measures.

The methodology involves a detailed literature review on fraud identification, assessment methods, prevention techniques and a theoretical model for fraud prevention. Machine learning-based data analysis, using self-organizing maps, is used to assess the severity of CF dynamically and in real-time.

Findings reveal the multifaceted nature of CF, emphasizing the need for tailored control measures and a shift from reactive to proactive mitigation. The study introduces a paradigm shift by viewing each CF as a unique “fraud event,” incorporating EWS as a proactive intervention. This innovative approach distinguishes the study, allowing for the efficient prioritization of CFs.

The practical implications of such a study lie in its potential to enhance the banking sector’s resilience to cyber threats, safeguarding stability, reputation and overall risk management.

The originality stems from proposing a comprehensive framework that combines machine learning, EWS and a proactive mitigation model, addressing critical gaps in existing cyber security systems.

Banks are exposed to many challenges to name a few i.e. growing market competition, political environment, market forces of demand and supply, technological changes, frauds and poor management. The banking sector devasted experiences of fraud have impacted all facets of the Banking, Financial Services and Insurance. In continuation, this study aims to revolve around themes of different types of frauds, especially insider frauds that have gained mainstream attention in recent major value fraud events with prominent Indian banks. This study will identify the types and drivers of insider frauds.

The methodology opted for the study is through confidential primary survey and focused group discussion with risk officers of banks who are associated with Indian banks for more than three years, further to understand the relation between type of Insider frauds and originating drivers were paired based on the principal component analysis.

Finally, the paper concludes with the conceptual mitigation framework for different types of insider fraud and driver pairs within the scope of this paper. This paper thought will support policymakers of the Indian banking system to create a more robust environment within the banking system via timely detection of frauds so that up to an extent it can be squared before it appears.

The study is innovative in the area of banks’ internal fraud management, where original data collected through a primary survey contributes to the conclusion of fraud management for various Indian banks.

The purpose of this study is to measure the impact of technology spend on bank profitability focusing on Indian public and private sector commercial banks. The study also assesses the longevity duration of impact for both public and private sector banks. The trade-off is created between the bank’s profitability and technology spent across four identified columns: increase product sales, reduce cost, enhance employee efficiency and optimally use existing resources.

The metrics of four columns converted to measurable 11 financial alarming indicators and impact is again checked on profitability indicator ROE. The data used for empirical analysis for the study are between the year 2003 and 2019, across 12 public and 15 private banks. The interface between technology spend and profitability is measured using panel vector autoregression (VAR) and panel vector error correction model (VECM) and further the link between 11 financial indicators and profitability measures is established using panel data analysis.

The study found that there is a mixed effect of technology spent on profitability and performance of Indian banks, where public sector banks were found to be more unstructured over private sector banks. The study advises the optimal technology spend strategies to gain enhanced productivity for banking business which are to name a few – planned technology reserves, customer awareness campaigns for products, robust employee-customer motivation policy and customized technologies aligned to existing infrastructure.

The data was original and extracted from the Reserve bank of India website and respective banks’ annual reports.

This study gauges the profitability and performance of Indian commercial banks under the technology advancements. In this study, the authors identified three domains that give advantage to banks due to technology incorporation, that is, increased sales revenue, reduced operating expenses, and increased employee productivity. The authors assess the effect of these domains on banks’ profitability and performance. This study is conducted for the period between the years 2003 and 2018 across 34 public and private banks for empirical analysis. The authors examined the impact of investment in technology on the profitability using panel data analysis and evaluated the long-term effect of technology investment using the vector error correction model. This study found that there is a mixed effect of technology spend on the profitability and performance of Indian banks, where private sector banks are more aggressive in technology investment as compared to the public sector banks. This study recommends an optimal technology-related strategy to gain improved productivity for the banking business, that is, planned technology reserves, customer awareness campaigns about technology-enabled products, and robust employee–customer motivation policy.