Leveraging AI for Sustainability In Banking: A Systematic Review Of Integrated Approaches

 

Reeta Pradhan ¹, Dr. Namrata Gain ¹

 

¹ Department of Management, Bharti Vishwavidyalaya, Durg (C.G.), India

 

1. INTRODUCTION

1.1.    Sustainability in Banking

The world is changing, and stakeholders – from everyday consumers to major investors and business executives – are increasingly prioritizing climate action, ethical sourcing, and responsible business practices. Namita 2013 This growing commitment is driving a radical change in how companies operate, with a greater focus on aligning financial goals with social values. The development of Environmental, Social, and Governance (ESG) standards provides a consistent framework for measuring this alignment, reflecting an organization's financial considerations related to sustainability and ethics. Xiao et al. (2023) For banks and financial services, embracing ESG in no way a matter of simply being ethical; it has become a critical business necessity for asset managers, banks, and insurance providers alike. Although the roots of ESG can be traced back to socially responsible investing in the 1960s, its widespread prominence was significantly boosted at the 2020 Davos summit. Vargas (2024) Recognizing the need for a more organized approach, the World Economic Forum (WEF) and the International Business Council (IBC) spearheaded an initiative to establish a common set of measurements. Snowflake 2022 This effort aimed to provide companies with a standardized framework for reporting their outcomes related to ESG concerns. ESG factors are the benchmarks used to judge how sustainable and ethical a business or investment is. Each area signifies:

·        Environmental ("Planet"): This factor assesses a company's impact on the environment. It considers things like how much carbon they emit, how much energy they use, how they conserve natural resources, their water usage, and how they manage their waste.

·        Social ("People"): This factor looks at a company's impact on society. This includes their treatment of workers (labor standards, human rights, health and safety), the safety of their products, how they promote diversity and inclusion, their involvement with the local community, and how satisfied their customers are.

·        Governance ("Principles"): This factor examines how a company is managed. It focuses on things like how transparent and accountable they are, whether they comply with regulations, how they protect data, how they manage risks, how they compensate executives, and the composition of their board of directors.

 

1.1.1.  Banks as Funders of Sustainable Development

According to S, 2022 Indian banks are increasingly prioritizing the funding of projects that drive sustainability, including renewable energy, clean transport, water conservation, and waste management. Their contributions are making a real difference:

·        Powering Clean Energy: Banks are vital in the transition to renewable energy sources like solar, wind, and hydro. They provide the substantial long-term financing (loans, bonds, etc.) necessary for these projects, helping India reduce its reliance on fossil fuels and embrace cleaner energy.

·        Building a Greener Infrastructure: From smart cities to efficient public transit and green buildings, banks are fueling the development of sustainable infrastructure. They offer specialized financial products and form partnerships to support these environmentally conscious projects, leading to a smaller ecological footprint.

·        Fostering a Circular Economy: Banks are backing the shift towards a circular economy by funding businesses focused on reducing waste through reuse, recycling, and regeneration. This includes providing financing to companies that utilize recycled materials in their manufacturing processes, for example.

·        Driving Social Progress: Sustainable banking in India also emphasizes social well-being. Banks are investing in crucial sectors like healthcare, education, and affordable housing, aligning with the UN Sustainable Development Goals (SDGs) and promoting inclusive growth within communities.

 

 

 

1.2. Artificial Intelligence in Banking

Reflecting a global concern for sustainability across all industries, the Indian banking sector is increasingly embracing this approach. A notable trend is the integration of AI to achieve sustainability goals. Indian banks are adopting numerous innovative methods to shift their operational models towards greater sustainability, ultimately aiming to provide safer investment options aligned with environmental, social, and governance (ESG) 

Criteria The Global Treasure. (2024). Surprisingly, Artificial Intelligence (AI) has become a valuable asset in this endeavor, providing advanced analytics and machine learning tools for making insightful decisions that yield positive outcomes for both environmental sustainability and financial performance.3According to Chakraborty 2024 Environmental Responsibility forms a vital part of this strategy, with banks channeling support towards endeavors and capital allocations that seek to alleviate climate change, encourage renewable power, and curtail pollution. To achieve its goals, this strategy focuses on: Environmental Responsibility, requiring banks to back projects and investments that combat climate change, advance renewable energy, and lessen pollution, potentially through green financing or internal carbon reduction policies. Social Impact is addressed by financial institutions prioritizing loans to projects that uplift communities, such as affordable housing, healthcare, education, and small businesses, leading to greater economic inclusion and social advancement. Lastly, Governance ensures transparency, accountability, and ethical conduct through measures like robust risk management, responsible lending practices, and a commitment to long-term stability over short-term earnings.4

Potential Research Questions:

1)     How is artificial intelligence currently integrated into sustainable banking practices?

2)     What AI technologies are most commonly used to support sustainability in the banking sector?

3)     What are the benefits and risks of implementing AI for sustainability in financial institutions?

4)     What gaps exist in current research regarding AI-driven sustainability in banking?

 

2. Objectives

1)     To investigate how artificial intelligence (AI) technologies are integrated into the banking sector to support environmental, social, and governance (ESG) goals.

2)     To identify and analyze the core AI technologies—namely machine learning, natural language processing, and predictive analytics—that contribute to sustainable banking outcomes.

3)     To examine the challenges and limitations associated with the adoption of AI in sustainable banking, including data privacy, regulatory issues, and ethical concerns.

 

 

 

 

3. Material And Method

The research followed the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines [52] to conduct a systematic review. PRISMA offers a standardized methodology that enhances the quality, transparency, and replicability of the review process. The systematic review was conducted through a clearly defined process, including the specification of article selection criteria, formulation of the search strategy, and detailed procedures for data extraction and analysis. Broadly, the PRISMA approach is structured into several sequential steps, ensuring a rigorous and comprehensive synthesis of relevant literature.

·        Database Selection: Information searches were conducted using reputable online academic databases, specifically Elsevier's SCOPUS, which hosts over 23,500 peer-reviewed journals. Articles that were not available in full text were excluded from the review to ensure the completeness and reliability of the data. The study selection process was carried out in three distinct stages, as follows:

Relevant databases such as Scopus, Web of Science, EBSCOhost, and Google Scholar will be searched using keywords like "Indian banking,"Green Finance ","Risk Assessment "ESG (Environmental, Social, and Governance),"Artificial Intelligence (AI) "," and "sustainability."

·        Inclusion Criteria: Studies will be included based on the following criteria termed as IC:- IC1 Articles published in English language.

IC2 Published in peer-reviewed journals or reputable conferences.

IC3 Focused on the inclusion of AI in Indian banking industry. IC4 Discusses integration of AI with sustainability.

IC5 Employs quantitative or qualitative research methods.

Data Extraction: Relevant data will be extracted from the selected studies, including:

·        Author(s), year of publication, journal/conference.

·        Research methodology (quantitative or qualitative).

·        ESG practices discussed.

·        Key findings and conclusions.

·        Challenges and barriers faced in implementation.

 

Quality Assessment: The quality of the selected studies will be assessed using a standardized assessment tool (e.g., Cochrane Collaboration's Risk of Bias Tool).

Data Synthesis: The extracted data will be synthesized to identify common themes, trends, and emerging practices.

Table 1

Table 1 Variables used Showing Dependency Relationships
TYPE	VARIABLE	DEPENDENCY/RELATIONSHIP
1.INDEPENDENT	AI Technologies Implementation	Directly influences Banking Sustainability Outcomes; its impact may vary depending on mediating/moderating factors.
2.DEPENDENT	Banking Sustainability Outcomes	Resulting performance outcomes influenced by how effectively AI is applied and how integrated approaches and contextual factors are managed.
3.MEDIATING/ MODERATING	Integrated Approaches	Enhance or weaken the impact of AI Implementation on Sustainability Outcomes by providing structure, tools, and alignment with ESG frameworks.
	Regulatory Context	Can amplify or constrain the effect of AI by setting compliance standards and incentives for sustainability.
	Data Quality & Availability	Essential for AI effectiveness; poor data limits AI's impact on sustainability outcomes.
4.CONTEXTUAL FACTORS	Bank Size Location Digital Maturity	Larger banks may have more resources to implement          AI                    and sustainability strategies, affecting the outcome's scale. Geographic                regulations                    and market maturity influence how           AI               and sustainability efforts are implemented. Affects readiness to adopt AI solutions; more mature banks likely to realize stronger sustainability benefits through AI.

 

Figure 1

   

Figure 1 Prisma Flowchart

 

4. RESULTS

The academic literature search gave 23 articles can be considered as relevant to the topic used.The relation between different variables have been discussed in this 23 articles. They are articles discussing the relationship between AI implementation and banking sustainability outcomes were 9 out of 23 articles, the relationship between banking sustainability outcomes and AI moderated by integrated approaches were 4 out of 23, the relationship between banking sustainability outcomes and AI moderated by contextual factors are 4 out of 23, relationship of banking sustainability outcomes and AI in alignment with ESG framework were 6.

Table 2

Table 2 Grouping of Articles based on Relationship between Variables
S. No	Relationship between variables	Authors	Number of articles
1	AI technologies on banking sustainability outcomes.	V.Jain(30), V.M.Tatikonda(28),O.Elias(25),A.Raj(12),A.Fezal(18),R.Kaur(19),Dr.N.Rajput (1),Dr.A.Wahab(21),E.B. Gayu(23).	9
2	AI technologies moderated by integrated approaches on sustainability outcomes	RajeshS(5),Md.Kamruzzamanet(33),V.Veerla(22) ,S.B. Subramanyam (32).	4
3	AI technologies moderated by contextual factors on sustainability outcomes.	N.Fathima(39),I.Fdhila(14),N.Takachinko(17),Dr. G.Yoganandham(20).	4
4	AI technologies in alignment with ESG framework on sustainability outcomes.	R.Xiao.et.al.(2),AdalA.Darkiyan(29),K.Challa(26),0.Joseph(35),B.O.Adela kun(37),Dr.N.Patwardha n(42).	6

 

This shows that research related to the relationship between AI and maintaining sustainability in Indian banks is the recent trends more prevalent now-a-days, so the empirical evidence is sufficient to discuss this relationship. The study was done on research article from the year 2013-till date. However it is still rare to observe all the variables in a particular paper Table 2 Further ,23 relevant articles are reviewed based on the selection results to discuss the inter-connection between AI driven technology as green banking, risk assessment, predictive analysis, machine learning and fraud detection process etc. To drive sustainability in Indian banking system based on Table 3, a quantitative, qualitative and mixed qualitative and quantitative approach was uesd in the 23 selected articles in the study.

Table 3

Table 3 Tabulation of Different Researches Based on their Approach and Result
S. No.	Authors	Research Aim	Approach	Result
1	V. Jain (30)	This paper examines the use of AI in monitoring, identifying, and preventing regulatory violations, particularly in the areas of sanctions compliance and Anti-Money Laundering (AML) initiatives."	Qualitative	Positive.
2	V.M. Tatikonda -28	This research paper explores the significant impact of Robotic Process Automation (RPA) and Artificial Intelligence (AI) on the financial industry, with an emphasis on delivering personalized services.	Quantitative	Positive
3	Chandra shekher -43	This research examines the role of digital transformation in promoting financial inclusion, focusing on how banking services are extending to underserved populations and how customers are adjusting to these changes.	Qualitative	Positive
4	A. Raj -12	The paper aims to evaluate the various uses and adoption of AI in Indian banking.	Qualitative	Positive
5	A. Fezel -18	This study aims to offer theoretical perspectives on the role of Artificial Intelligence (AI) in advancing financial inclusion.	Qualitative	Positive
6	R. Kaur (19)	This study seeks to assess customer perceptions regarding the adoption of AI tools for diverse applications within the banking sector.	Quantitative	Positive.
7	Dr. N. Rajput -1	This study aims to examine the transformative shift in Indian banks toward embracing sustainable development practices.	Quantitative	Positive.
8	Dr. A. Wahab -21	This study aims to analyze the impact of Artificial Intelligence on key aspects of service quality in banking, such as risk assessment, fraud detection, and related functions.	Qualitative	Positive.
9	E. B. Gayu -23	This study seeks to explore the intricate relationship between AI technology and its role in enhancing banking and financial services.	Quantitative	Positive
10	Rajesh S -5	This paper examines the emerging opportunities for green banking in India, highlighting the growing demand for sustainable financial products, supportive regulatory frameworks, and the potential to enhance corporate social responsibility.	Qualitative	Positive
11	Md. Kamruzzam anet -33	The main aim of this study is to evaluate how digitalization in the banking industry drives the need for Artificial Intelligence in handling customer complaints and inquiries, ensuring regulatory compliance, and resolving various operational issues— either autonomously or alongside human support	Quantitative	Positive
12	S. B. Subramanyam (32)	This paper examines the transformative impact of Robotic Process Automation (RPA) and Artificial Intelligence (AI) in evolving the financial services industry into a more dynamic and intelligent ecosystem.	Mixed method approach.	Positive
13	V. Veerla (22)	This study aims to enhance strategic implementation in India’s banking sector through the effective use of Artificial Intelligence.	Qualitative	Positive
14	N. Fathima (39)	This study investigates the key factors affecting the adoption and implementation of AI in private banks	Quantitative	Positive
15	I.                  Fhadila (14)	This analysis seeks to redefine banking efficiency by incorporating economic, social, and environmental dimensions into its assessment	Quantitative	Positive
16	N. Takachinko (17)	This paper explores how incorporating AI's carbon footprint into the banking sector’s risk management frameworks supports alignment with sustainability objectives and regulatory mandates	Qualitative	Positive
17	Dr. G. Yoganandha m (20)	This study seeks to assess the emerging trends, potential opportunities, and key challenges associated with the implementation of AI in the banking sector	Quantitative.	Positive.
18	R. Xiao. et.al. (2)	This study seeks to examine current developments in sustainable finance and ESG investing, emphasizing the importance of understanding these evolving trends.	Qualitative	Positive
19	Adal A. Darkiyan et. Al (29)	The article addresses the challenge of enhancing the efficiency of integrating the regional banking system."	Quantitative	Positive
20	K. Challa (26)	This study aims to deliver innovative AI-driven solutions that promote sustainability.	Qualitative	Positive.
21	O. Joseph (35)	This study aims to investigate the key factors influencing the adoption of Robotic Process Automation to support sustainability efforts in the banking sector.	Qualitative	Positive
22	B. O. Adelakun -37	This study seeks to investigate the development of AI-driven models designed to enhance sustainable accounting practices, with an emphasis on environmental impact assessment and transparent reporting.	Quantitative	Positive
23	N. Patwardhan -42	This study explores the integration of Artificial Intelligence with the CAMELS framework to enhance sustainability within the banking sector.	Quantitative	Positive

 

5. RESEARCH GAP

The systematic literature review on AI-based technologies and sustainability outcomes in banking and finance reveals that various studies address different AI technologies and their relationship to sustainability in a fragmented manner. A comprehensive analysis encompassing all relevant variables within a single study is noticeably lacking. Furthermore, the adoption of AI technologies in Indian banks—particularly within the public sector—remains significantly behind that of their private sector counterparts. Many studies highlight short-term results of AI applications in sustainable banking. There's a lack of longitudinal studies assessing the long-term impact of AI on ESG goals, customer trust, and financial resilience.

 

6. DISCUSSION AND RECOMMENDATIONS FOR FUTURE RESEARCH

The study identified a positive correlation between the adoption of AI technology and sustainability outcomes in the banking and finance industry. Research suggests that the reliance on AI technologies significantly enhances attributes such as customer satisfaction, ESG factors, and growth within the financial and banking sectors (1, 7, 8, 9). Technologies like machine learning, predictive analytics, fraud detection, and risk assessment have proven to be key milestones in advancing the Indian banking and finance system (18, 19, 21). Additionally, the success of AI integration depends on how different banks have embedded these technologies into their operations (5, 13).

Contextual factors—such as whether a bank is public or private, its geographic location (urban or rural), its digital readiness, and its commitment to creating a sustainable ecosystem—also influence the successful implementation of AI. However, the use of AI brings about various challenges, including cyber threats, fraud, and digital theft, which need to be minimized for AI to become a transformative force in the future G. Govindham 2020.

Long-term studies that track the impact of AI adoption on sustainability outcomes, customer satisfaction, and financial growth over time would provide more conclusive evidence of its benefits and challenges. This could help understand the lasting effects of AI integration within the banking sector. Future research could focus on developing AI models that integrate ESG (Environmental, Social, and Governance) factors more effectively. This would allow banks to align AI-driven decision-making with sustainability goals, improving their social and environmental impact.

 

7. CONCLUSION

The systematic literature review offers a thorough overview of the relationship between AI-driven technologies and sustainability in the banking and finance sectors. Based on the established eligibility criteria, 23 articles were selected for analysis. While some studies highlighted that AI-driven technologies directly contribute to sustainability in banking, others emphasized that integrated and contextual factors play a significant role in shaping the adoption and effectiveness of AI in banks. One of the key concerns identified is ensuring compliance with Environmental, Social, and Governance (ESG) standards when implementing AI technologies. Achieving a balance between leveraging AI for operational benefits and adhering to ESG compliance is critical for the Indian banking ecosystem. Overcoming the challenges and risks associated with AI implementation will be essential for sustaining its growth and maximizing its positive impact on the sector.

 

CONFLICT OF INTERESTS

None. 

 

ACKNOWLEDGMENTS

None.

 

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