How to Leverage AI for Banking Efficiency
Artificial intelligence (AI) continues to reshape the global financial services industry, blending rapid innovation with strict regulatory oversight. Banking institutions, fintechs, insurers and wealth-management platforms are leveraging AI for fraud detection, risk modelling, customer insights, personalisation and efficiency.
According to KBV Research, the global AI in banking market is expected to reach US$132.9 billion by 2030, signaling one of the strongest growth trajectories in the financial sector. This momentum is driven by increased deployment of customer-facing AI systems, including virtual assistants, credit-decisioning models and automated service platforms.
High Adoption, Higher Scrutiny
Despite fast adoption, visible use of AI in financial services requires much stricter evaluation. Even a single AI-generated mistake about loan eligibility, repayment obligations, or investment risk can misinform customers, result in regulatory violations, damage trust or trigger compliance investigations.
Trust remains fragile: PwC’s 2024 consumer trust research indicates that 40% of customers have stopped buying from a company due to trust issues, and only four in ten are willing to forgive a company even after it resolves a mistake. This puts immense pressure on financial institutions to ensure AI-generated content is accurate, compliant and responsibly governed.
Data Foundations Still the Biggest Barrier
Industry leaders consistently cite data quality and system integration as the major obstacles preventing AI from scaling further. McKinsey’s 2024 State of AI report found that 70% of high-performing organisations face significant data-related challenges when implementing generative AI.
Common issues include siloed data, legacy infrastructure, inconsistent metadata and strict data-handling requirements. These limitations make AI in finance particularly sensitive, as regulatory compliance and contextual accuracy are non-negotiable.
Skills Gap Growing Faster Than AI Advancements
While AI capabilities are accelerating, financial services organisations are struggling to keep up from a skills perspective. Today’s teams must possess expertise in AI testing, data governance, hallucination detection, workflow design, compliance readiness and AI literacy across all functions.
Without this mix of expertise, even well-funded AI programs face scaling and governance issues.
The Rise of AI-First Customer Discovery
A key trend shaping financial marketing in 2025 is the shift towards AI-first discovery. Customers increasingly encounter financial brands through AI summaries, chatbots and virtual assistants before visiting traditional websites.
Gartner predicts that traditional search volume will fall by 25% by 2026 due to AI-powered experiences. Ahrefs analysis shows that AI Overviews significantly reduce click-through rates on organic results. This means financial brands must optimise content not only for search engines but also for AI-driven discovery platforms.
Priorities for 2026
Based on market research and expert roundtable insights, six major priorities emerge for financial institutions. These are: (1) Build robust AI governance frameworks—develop clear policies for oversight, risk management and accountability; (2) Modernise data infrastructure and governance—ensure data is high-quality, accessible and compliant; (3) Track brand visibility inside AI-powered search and discovery systems—monitor how your brand appears within AI tools and summaries; (4) Maintain strict human oversight for customer-facing AI—review all customer interactions powered by AI to avoid errors or bias; (5) Scale internal AI adoption before external deployment—focus on internal use and education to build readiness; (6) Invest in AI literacy across marketing, compliance and operations—train staff to understand AI risks and opportunities.
- Modernise data infrastructure and governance.
- Track brand visibility inside AI-powered search and discovery systems.
- Maintain strict human oversight for customer-facing AI.
- Scale internal AI adoption before external deployment.
- Invest in AI literacy across marketing, compliance and operations.
CEO Insight: Farhad Divecha on Real AI Challenges
AccuraCast Group CEO Farhad Divecha notes that finance organisations are not struggling to adopt AI—they’re struggling to identify which AI tools deliver real value. He emphasises that CMOs in finance face high regulatory pressure and cannot release AI-generated content without rigorous testing. A single inaccurate message can have legal consequences or harm market stability.
Conclusion
The financial services industry is disciplined in its innovation. Institutions that adopt AI with strong governance, quality data, and effective AI discovery strategies will gain an edge. In 2026, competitive advantage will come from accuracy, compliance, and trust—not just speed.
Disclaimer:
Some content in this article is sourced from research studies, financial-industry reports, and expert commentary.