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Message from IDC
Opportunities for GenAI in Financial Services
The financial services sector has been utilizing artificial intelligence and machine learning across various applications, such as fraud detection and customer engagement, for many years. In 2024 alone, global investments in AI for financial services surpassed $37 billion. This indicates that AI is a well-established technology in sectors like banking, insurance, and capital markets. However, the significant potential of advanced AI technologies, particularly generative AI (GenAI), is prompting financial institutions to explore how GenAI can enhance customer personalization, boost productivity, and accelerate product development. IDC has identified nearly 70 potential use cases for GenAI that could advance these goals in the financial services industry. Financial institutions have outlined key objectives, which include:
Top 5 Anticipated Business Outcomes from AI Initiatives:
- Cost reduction
- Enhanced employee productivity
- Better customer experiences
- Increased speed of operations
- Profit growth
Nonetheless, the highly regulated nature of the financial services industry raises concerns over potential challenges. Numerous existing and emerging regulations, such as the European Union’s AI Act, emphasize customer protection and institutional risk management. These regulatory concerns add to the already stringent data privacy and security laws in various regions, including the EU’s General Data Protection Regulation (GDPR) and the Financial Modernization Act of 1999 (also known as the Gramm-Leach-Bliley Act and the Personal Data Protection Act in Singapore). In April 2023, security and data sensitivity were highlighted by 56% and 51% of respondents, respectively, as critical factors determining whether a workload can be transitioned to the public cloud.
The Role of Private AI
Trust is undoubtedly the cornerstone of financial services, and Private AI can address concerns regarding safety and confidentiality while maximizing the effectiveness of AI and GenAI in enhancing institutional operations and unlocking new business opportunities, all while adhering to compliance and risk management standards. Some prime use cases for Private AI include:
- Fraud detection and prevention: Private AI models can assess transaction patterns to identify and thwart fraud.
- Personalized customer experiences: Financial institutions can leverage Private AI to tailor their services to individual customer needs.
- Back-office efficiency: Customer service agents can utilize Private AI to expedite information retrieval and resolve more inquiries daily, yielding noticeable efficiency and cost reductions.
- Risk management and compliance: Private AI models facilitate institutions in risk analysis and forecasting, covering areas from credit risk to market fluctuations.
- Document processing automation: Private AI can manage sensitive documents within the bank’s infrastructure, streamlining loan processing and KYC verification while ensuring data safety.
- Code generation: Private AI can produce code in multiple programming languages, ensuring secure and efficient software development.
This approach to Private AI emphasizes data and model control through encryption, anonymization, secure governance, and on-premises deployment, resulting in enhanced data privacy and security compared to other models. It ensures compliance with local regulations, better data governance, and a reduction in institutional risk.
Key Aspects of Private AI Include:
- Control over sensitive data: All data subject to confidentiality regulations and potential industrial risks remains under the institution’s control, even in a distributed AI environment.
- Protection of competitive advantage: In investment banking, complex data serves as intellectual property that provides a competitive edge. Maintaining control over data and models is essential for institutional success, making Private AI a critical asset.
- Controlled collaboration: While Private AI restricts external access to sensitive data, it fosters collaboration within the institution through model galleries. Enhanced access control within these galleries promotes innovation without compromising security.
Evaluating Private AI
Private AI can enable financial services to innovate using AI and GenAI without compromising their foundational commitments to trust, security, and compliance. As IT and business leaders in financial services consider adopting Private AI, they should focus on the following strategic transformation areas:
- Adopt a platform approach to AI service delivery, enabling the exploration and application of new AI models and services in alignment with evolving market needs. This should include automation and integrated tools that minimize reliance on specialized internal expertise.
- Prioritize governance, regulatory compliance, and risk management skills, including third-party risk assessments, while collaborating with IT partners to maximize AI capabilities across the organization.
- Develop a scalability and resilience strategy that recognizes data and AI as core business capabilities, avoiding siloed approaches that hinder efficiency.
- Formulate partnerships with top-notch vendors that demonstrate credibility in the financial services industry and have a proven track record of delivering improvements in key performance indicators like cost reduction and profits.
IDC advocates that financial institutions create a strategy to effectively balance private and public AI resources to leverage both while maintaining or enhancing compliance and risk management. Furthermore, IT organizations must evolve beyond a focus solely on technical service provision to encompass governance, security, and compliance across the platforms and capabilities they develop. The IT sector can offer hardware, software, and services that underpin banking objectives with safe and reliable technologies.