AI and the Future of Financial Services: The View from Davos

A new report from the World Economic Forum surveys the current state of AI in banking and financial services, finding both optimism and reasons for alarm.

By David Evans, Chief Content Officer

Published on January 28th, 2025 in Artificial Intelligence

The report: Artificial Intelligence in Financial Services

Source: World Economic Forum, in collaboration with Accenture

Why we picked this report: Released to coincide with this year’s Davos gathering, this WEF report will likely find its way onto the reading lists of many leaders — not just those in the financial industry itself, but also in governments, regulatory bodies and others who have an interest in the future of financial services.

Executive Summary

Artificial intelligence is rapidly transforming the financial services industry, with investments projected to reach $97 billion by 2027. This recent report by the World Economic Forum and Accenture, part of a promised series on AI in financial services, argues that banks and other financial institutions are uniquely positioned to capitalize on AI due to their data-rich, language-heavy operations.

Furthermore, while early AI adoption focused primarily on efficiency gains, the reports says more than two-thirds of financial services executives now believe AI will directly contribute to revenue growth, revolutionizing everything from customer experiences and product innovation to risk management and compliance.

However, significant challenges remain, including regulatory uncertainty, workforce transformation, and emerging risks like AI-generated misinformation and deepfakes. To succeed, financial institutions must balance innovation with responsible AI practices, prioritizing data sovereignty, self-governance, and comprehensive workforce reskilling while maintaining robust security measures against evolving threats.

Key Takeaways

  • Financial services firms spent $35 billion on AI in 2023, making it one of the most AI-invested industries.
  • Many organizations are sensitive to the need to balance innovation with responsibility — 84% are implementing or planning AI governance frameworks to ensure ethical and secure deployment.
  • Organizations are also prioritizing workforce transformation, with 90% of leaders believing significant adjustments to reskilling strategies are needed to support AI implementation.

What we liked about this report: The report provides useful context by benchmarking AI efforts in financial services against other industries. The data reinforces the idea that the financial sector is likely to lead many others in AI implementation. It also provides a detailed roadmap of what processes and functions in financial services may be most impacted by AI – and which won’t.

What we didn’t: Most of the report’s observations will be familiar to anyone already following the development of AI in the financial sector. Almost a third of the report focuses on the three R’s – risk, responsibility and regulation, reflecting the WEF’s familiar bias toward institutional control, as well as its political and social priorities. For example, the top three near-term global risks (in the WEF’s view) are misinformation, extreme weather, and societal polarization.

Transforming Financial Services Through AI

The financial services industry stands at the forefront of AI adoption, leveraging its data-rich environment and language-heavy operations to drive innovation across banking, insurance, and capital markets. The sector is witnessing unprecedented transformation in how services are delivered and value is created, particularly in retail banking, where AI is revolutionizing everything from customer onboarding to portfolio management and risk assessment.

Initial AI implementations focused primarily on cost reduction and operational efficiency. However, the industry is now pivoting toward revenue generation and customer experience enhancement. Financial institutions are using AI to create personalized services, improve risk assessment, and develop innovative products that were previously unfeasible.

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The AI Implementation Landscape

Financial institutions are deploying AI across multiple fronts, with particular emphasis on customer service, risk management, and fraud detection. Advanced applications include AI-powered virtual assistants providing 24/7 customer support, automated underwriting systems, and sophisticated fraud detection mechanisms. The technology’s ability to process vast amounts of data efficiently has revealed numerous opportunities for automating routine tasks while enabling more strategic decision-making.

In retail banking specifically, AI is transforming front-office operations through intelligent customer relationship management, automated credit scoring, and personalized product recommendations. Middle-office functions are benefiting from enhanced risk management and compliance monitoring, while back-office operations are seeing improved efficiency through automated document processing and reconciliation.

Emerging Technologies and Integration

The financial services sector is witnessing the emergence of several key technological developments that are shaping its AI future. Small language models (SLMs) are gaining traction for specific, targeted applications where efficiency and speed are crucial. These models, trained on smaller, industry-specific data sets, are proving particularly effective for tasks like customer service and product documentation.

Retrieval-augmented generation (RAG) is emerging as a critical technology for improving the accuracy and reliability of AI systems. By combining large language models with institution-specific knowledge bases, RAG enables more precise and contextually appropriate responses to customer queries while maintaining compliance with regulatory requirements.

Despite the opportunities, financial institutions face significant challenges in AI implementation. The rise of AI-generated misinformation and deepfakes poses new security threats, with a 223% increase in deepfake-related tools on dark web forums in early 2024. Additionally, organizations must address data privacy concerns, regulatory compliance, and workforce transformation needs.

Financial institutions are particularly concerned about the potential for AI-enabled fraud and market manipulation. The ability of AI to create convincing deep-fake videos and voice recordings has already led to several high-profile fraud attempts, including a recent case where a finance worker was deceived into transferring $25 million through a fake video call.

Dig deeper:

The Human Element and Workforce Transformation

Success in AI implementation requires more than just technology investment. With 90% of leaders acknowledging the need for significant reskilling initiatives, organizations must focus on preparing their workforce for an AI-augmented future. This includes developing new skills like prompt engineering and ensuring all employees can effectively collaborate with AI systems.

The transformation extends beyond technical training. Organizations are finding they need to cultivate a new mindset among employees at all levels, from executive leadership to front-line staff. This cultural shift involves embracing AI as a collaborative tool rather than viewing it as a threat to job security.

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Building a Responsible AI Framework

Financial institutions are increasingly prioritizing responsible AI practices, with 84% implementing or planning governance frameworks. These frameworks address critical areas including data sovereignty, ethical considerations, and security measures. Organizations must balance innovation with responsibility to maintain trust and comply with evolving regulations.

The development of responsible AI frameworks encompasses several key dimensions:

  • Organizational: Creating structures that promote responsible AI development and usage
  • Operational: Establishing governance systems and controls
  • Technical: Ensuring systems are explainable and trustworthy
  • Reputational: Aligning AI initiatives with organizational values and ethical principles

The regulatory environment for AI in financial services continues to evolve, with various jurisdictions taking different approaches. The European Union’s AI Act, the U.S. President’s Executive Order on AI, and initiatives from bodies like the Monetary Authority of Singapore are creating a complex regulatory landscape that financial institutions must navigate.

Financial institutions must particularly focus on ensuring their AI systems comply with existing regulations around consumer protection, fair lending, and anti-discrimination laws, while also preparing for emerging AI-specific regulations.

Looking Ahead

As AI continues to mature, financial services organizations must maintain flexible technology strategies while monitoring emerging innovations like small language models, AI agents, and quantum computing integration. Success will require careful balance between managing investment costs, accelerating time to market, and building in-house capabilities while maintaining robust security measures and ethical standards.

The future of AI in financial services will likely be characterized by:

  • Increased personalization of financial products and services
  • Enhanced risk management through predictive analytics
  • Greater automation of routine tasks and decisions
  • Improved fraud detection and security measures
  • More sophisticated customer interaction systems
  • Deeper integration of AI across all business functions

The key to success will lie in organizations’ ability to leverage these technologies while maintaining customer trust and regulatory compliance, all while preparing their workforce for an AI-augmented future.

Editor’s note: This article was prepared with AI language software and edited for clarity and accuracy by The Financial Brand editorial team.

About the Author

Profile PhotoDavid Evans is an experienced, strategic leader of global content programs. Core skill sets include the creation, management, execution of multiplatform content strategies, with a focus on quality and user experience and leadership of complex organizations, often matrixed and multi-function, frequently international, as well as complex ecosystems of external partners, vendors, and platforms.

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