This paper examines the integration of AI's carbon footprint into the risk management frameworks (RMFs) of the banking sector, emphasising its importance in aligning with sustainability goals and regulatory requirements. As AI becomes increasingly central to banking operations, its energy-intensive processes contribute significantly to carbon emissions, posing environmental, regulatory, and reputational risks. Regulatory frameworks such as the EU AI Act, Corporate Sustainability Reporting Directive (CSRD), Corporate Sustainability Due Diligence Directive (CSDDD), and the Prudential Regulation Authority's SS1/23 are driving banks to incorporate environmental considerations into their AI model governance. Recent advancements in AI research, like the Open Mixture-of-Experts (OLMoE) framework and the Agentic RAG framework, offer more efficient and dynamic AI models, reducing their carbon footprint without compromising performance. Using these technological examples, the paper outlines a structured approach for banks to identify, assess, and mitigate AI's carbon footprint within their RMFs, including adopting energy-efficient models, utilising green cloud computing, and implementing lifecycle management.

Many of the world's biggest banks lack transparency in how they are developing the artificial intelligence (AI) that could be used in future decision-making and risk management. A study of the 23 largest banks in Europe, the US and Canada found eight that have not publicly reported their responsible AI principles. Benchmarking company Evident assessed banks in four areas of responsible AI, using public data to create its AI Index. It looked at the banks' creation of AI leadership roles, publication of ethical principles, collaborations with other organisations and publication of original research. Evident CEO Alexandra Mousavizadeh said AI could provide better risk management and decision-making across the global banking sector.

Technology has changed our society, and banks and other financial institutions have digitalized their operations at a rapid pace as well. However, the financial crime compliance units of these institutions still rely mainly on heavy manual processes. The banking compliance units' key reason for their cautious approach in the utilisation of AI and automation has been uncertainty about technology. Do regulators approve machine-based decision-making, and is machine learning logic fair in identifying suspicious activities? However, there is a clear need for utilising technology in financial crime compliance.

Artificial intelligence in the banking sector makes banks efficient, trustworthy, helpful, and more understanding. It is strengthening the competitive edge of modern banks in this digital era. The growing impact of AI in banking sector minimizes operational costs improves customer support and process automation. Besides, AI in banking also helps users to select loan amounts at an attractive interest rate. The AI technology in the banking sector allows banks to update processes automatically and work under existing regulatory compliance. In this blog, we briefly explained a few core use cases of Artificial Intelligence in the banking sector. Let's have a look into What AI can do for the banking sector?

The introduction of AI and Low-code in banking has improved adaptability and resilience by bringing process improvement, saving crucial resources and time. The technologies are scalable and allow the institutions to make critical changes to meet future market demands.

Thanks to the advancements in natural language processing (NLP), including LaMDA, GPT-3, GPT-Neo, BERT, and large pre-trained transformer-based language models (PLM), conversational AI has achieved state-of-the-art performance on many tasks. In addition, it has fueled a paradigm shift in NLP. As per Mordor Intelligence, the global ChatBot (conversational AI or virtual assistant) market is expected to touch $100 billion by 2026, growing at a compound annual growth rate of 34.75 percent. Technology has seeped into almost every industry and is changing how businesses and individuals interact and perform everyday tasks. The top five industries that benefit from chatbots include banking and finance, travel, real estate, education, and healthcare.

Every time you receive a call from your bank after making a purchase using your credit card, it's generally AI- powered systems running in the background assisting your bank with fraud detection. These calls -- along with push ads or SMS verifications are a form of two- factor authentication initiated to validate the identity of the person who has made the transaction. AI also has the power to identify strange or out of the ordinary purchase patterns and behaviors, which can also be used to warn banks whenever any potentially suspicious transaction is conducted at the client's end. Not just that, AI can also prioritize suspected fraudulent activity so that investigations can be on the base of urgency or significance. ML strategies which are developed by using the true data of consumers -- can remember the usual spending patterns of the clients so that whenever it spots an anomaly, it can raises a flag, thereby making the AI system more equipped for identifying fraud.

In recent years, we can witness that artificial intelligence is becoming a need in every domain of the industry, and AI's different domains, such as computer vision, natural language processing, and predictive modelling, are helping humans solve their use cases and problems more effectively and without the intervention of the humans. We can also enjoy the intervention of AI in our daily life, and humans are becoming more curious about this intervention. Banking sectors are also positively affected by the intervention of AI. In this article, we will cover some of the critical use cases of AI in the banking sector that is helping humans advance the banking sector. This sector implies AI-enabled models to assist the customer during onboarding.

Any financial institution should be able to update with the latest customer demands. Especially when it uses security and customer services. However, scalability and on demand customization are some challenges that need to be resolved. Particularly, small institutions are under high pressure to modernize their IT systems. Customers demand an excellent online experience, as everything is always changing the banking industry. Evolving with new technologies, the application of machine learning in banking is occurring at a faster pace.

The introduction of artificial intelligence in the banking sector makes banks efficient, helpful and more understanding. The growing impact of AI in this sector reduces operational costs, improves customer support and process automation. AI-based applications help banks by reducing costs thereby increasing productivity. Also, intelligent algorithms are able to spot inconsistency and fraudulent information in a matter of seconds. According to reports, nearly 80 percent of banks are aware of the potential benefits that AI presents to their sector.