Unlocking Maximum ROI in Banking Through AI and Automation: AgileIntel’s Perspective

Generative AI and automation transform how banks compete, comply, and engage with customers. These technologies enable more innovative credit scoring, personalised service, stronger fraud prevention, and efficient regulatory reporting. For consulting firms, effective use of these innovations delivers measurable ROI for clients and strengthens their role as strategic partners in digital transformation. 

A recent McKinsey analysis on generative AI in banking reveals that generative AI will deliver up to US$340 billion in annual banking value globally, with nearly 80% of institutions deploying these technologies in at least one core function. 

The Strategic Value of Generative AI in Banking 

Generative AI, such as large language models and intelligent agents, is transforming banking operations. It improves customer interactions with AI-powered assistants and automates complex risk assessments, allowing banks to make faster, more informed decisions. 

JPMorgan Chase, a global leader in financial services, implemented generative AI for contract review and document analysis, automatically extracting key clauses from complex contracts and reducing manual review time by over 40%. This enabled compliance and legal teams to focus on strategic analysis, improving efficiency and reducing human error. 

Citibank has used AI to enhance fraud detection by analysing transaction patterns in real-time, identifying anomalies that indicate fraudulent activity. The AI system has decreased false positives and improved response times, protected the bank and its clients, and reduced operational costs. 

Automation: Driving Efficiency and Precision 

Automation technologies, particularly Robotic Process Automation (RPA) and intelligent workflow management, are central to modern banking operations. By automating repetitive tasks, banks reduce errors, accelerate processing times, and maintain compliance more effectively. 

For instance, Eurobank, a prominent Polish financial institution, implemented RPA across loan processing and KYC (Know Your Customer) operations. Automation reduced processing time by 30%, minimised errors, and freed employees to focus on higher-value work such as relationship management and advisory services. By integrating RPA into online sales processes, tasks that previously took up to 1.5 days were completed in just four hours. 

Bank of America integrated AI-driven automation in its back-office operations, streamlining data aggregation, report generation, and risk monitoring. This allowed the bank to improve accuracy while saving hundreds of labour hours per month, highlighting the tangible ROI that automation can deliver. 

Maximising ROI: Key Strategies for Banking Consultants 

Consultants must adopt a structured approach to ensure that AI and automation deliver measurable ROI: 

• Value-Centric Deployment: Prioritise initiatives that deliver quantifiable outcomes, whether through operational cost reduction, revenue growth, or improved customer engagement. 

• Integration into Core Functions: Embed AI and automation into essential banking processes such as underwriting, fraud monitoring, regulatory reporting, and client advisory. 

• Cross-Functional Collaboration: Foster collaboration across IT, operations, and business teams to ensure smooth adoption of new technologies. 

• Pilot, Measure, Scale: Launch pilot projects to validate results, refine solutions, and scale successful implementations across the organisation. 

This approach ensures that AI and automation investments translate into tangible business outcomes, reinforcing the strategic value of consulting engagements. 

Case Studies: Real-World Applications 

Globally, generative AI and automation deliver measurable ROI, improve efficiency, and enhance financial institutions' customer experience. The following examples show how leading banks have addressed challenges and created value through these technologies. 

Goldman Sachs is a leading global investment banking, securities, and investment management firm. In 2025, Goldman Sachs launched its proprietary generative AI tool, the GS AI Assistant, across its 46,500 employees. This tool assists in summarising complex documents, drafting content, and performing data analysis, significantly enhancing productivity and streamlining operations. Additionally, the firm has integrated AI into its risk and compliance monitoring systems, achieving high accuracy in fraud detection and regulatory compliance tasks. 

NatWest, headquartered in Edinburgh, Scotland, UK, is a retail and commercial bank providing a wide range of banking services in the UK. Natwest deployed AI-powered chatbots capable of handling routine inquiries across multiple channels and creating seamless responses. As a result, call-centre volumes decreased by 25%, while customer satisfaction scores improved. 

HSBC, one of the world's largest banking and financial services organisations, used AI for fraud detection across its global operations, reducing false positives by 20% and improving overall fraud prevention. Consulting efforts focused on creating dashboards and monitoring systems that provided real-time insights for compliance and operational teams. 

N26 is a digital-first bank offering mobile banking services across Europe and the United States. N26 implemented AI-driven anti-money laundering (AML) solutions across multiple jurisdictions, enabling rapid compliance checks and reducing human error, while dashboards provided visibility into suspicious activity. Consulting services helped N26 optimise workflow integration and compliance reporting, maximising operational efficiency and regulatory adherence. 

Navigating Challenges and Risk Mitigation 

While the benefits of AI and automation are significant, there are challenges to consider: 

• Data Quality and Integration: AI models rely on accurate, well-integrated data. Fragmented or poor-quality data can compromise outcomes. 

• Regulatory Compliance: AI initiatives must comply with strict banking regulations, data privacy and reporting standards. 

• Change Management: Employee adoption can be a barrier. Without proper training and engagement, ROI may be limited. 

• Model Transparency and Ethics: Generative AI decisions must be interpretable to ensure trust and regulatory compliance. 

Consultants can mitigate these risks by establishing governance frameworks, monitoring model performance, and facilitating change management initiatives, ensuring practical and sustainable AI adoption. 

AgileIntel Perspective: How We Help 

Consulting firms are critical in ensuring banks capture maximum ROI from generative AI and automation. From AgileIntel's perspective, the process includes: 

• Opportunity Assessment: Identifying areas where AI and automation can deliver the highest impact, whether operational, regulatory, or revenue-oriented. 

• Solution Design: Crafting tailored AI and automation frameworks that align with the bank's business goals and technological capabilities. 

• Implementation Support: Guiding seamless deployment across front-office and back-office functions, ensuring adoption and minimising operational disruption. 

• Monitoring & Optimisation: Establish governance, monitor KPIs, and continuously refine models and workflows to sustain ROI. 

By combining strategic insight with technological expertise, AgileIntel helps banks adopt AI and automation while achieving measurable gains in efficiency, cost reduction, and customer satisfaction. 

Looking Ahead: The Future of Banking Consulting 

As banks advance digital transformation, consultants will play a more strategic role. Generative AI and automation drive operational efficiency, decision-making, and revenue growth. Firms that guide banks through successful AI adoption and sustainable ROI will become trusted partners in the evolving financial sector. 

The future of banking is clear: AI and automation are critical drivers of growth, resilience, and competitive advantage in an increasingly complex and dynamic market.