When it comes to artificial intelligence adoption, finance organizations are leading the way with substantial investments that outpace other industries and unlock new opportunities in risk management, customer experience, and operational efficiency. But with this rapid growth comes new challenges.
Presidio’s AI Readiness Report reveals that 66% of finance IT leaders are prioritizing AI investments—a rate higher than the all-industry average. However, the push to integrate AI at scale comes with critical considerations. How can financial services, from accounting and banking to investment management ensure AI-driven insights remain secure, compliant, and reliable? And what steps should leaders take to maximize AI’s potential without introducing unintended risks?
AI Investment: A Competitive Imperative in Finance
AI adoption is a strategic priority for finance leaders, with investments aimed at strengthening security and enhancing efficiency:
- 66% rank AI as a top investment priority, exceeding the 63% industry-wide rate.
- 65% cite cybersecurity as a primary AI focus, underscoring concerns about financial fraud and data breaches.
- 47% believe AI is essential for maintaining a competitive edge, compared to 35% in healthcare and 17% in government.
With AI transforming fraud detection, risk modeling, and customer interactions, financial institutions that fail to keep pace risk losing market share in an increasingly AI-driven landscape.
How Financial Services Firms Are Using AI
Finance organizations are deploying AI across a range of mission-critical functions, from improving security to enhancing decision-making. Compared to other industries, finance firms are ahead in several key areas:
- Data Analysis: 71% of finance firms use AI for advanced analytics, compared to 62% across all industries.
- Decision-Making: 55% of financial organizations leverage AI-driven insights to guide business strategies, outpacing the 45% all-industry average.
- Customer Experience: 58% use AI to personalize customer interactions, compared to 49% across the all-industry baseline.
- Operational Efficiency: 68% apply AI for automation and workforce productivity, exceeding the 61% industry-wide rate.
- Product Innovation: 50% of financial firms integrate AI into product development, compared to 41% across all industries.
From AI-powered fraud detection to predictive analytics, financial institutions are rapidly shifting from traditional methods to AI-driven solutions. But with this shift comes increasing pressure to ensure AI is deployed responsibly.
Navigating AI Adoption Challenges in the Finance Sector
Despite its clear advantages, AI implementation isn’t without challenges. In the survey report, those in the finance sector report several key hurdles:
- Technical Complexity: 40% cite technical challenges involved in AI implementation, compared to 35% across all industries.
- Leadership Expectations: 31% report executive pressure for rapid ROI on AI investments, a rate higher than many other industries
- Data Security Concerns: 51% identify data exposure as their top AI-related risk. This makes sense given the overall sensitivity of financial data.
For CIOs and IT leaders, the challenge isn’t just adopting AI—it’s integrating AI in a way that aligns with security policies, regulatory frameworks, and operational needs. Without the right controls in place, AI adoption in the financial services industry can lead to compliance risks and unintended vulnerabilities.
AI Governance: Balancing Innovation and Risk
AI governance is becoming a priority as finance firms work to align AI adoption with security and compliance requirements. According to the AI Readiness Report, IT leaders in the finance sector are already taking steps to manage AI risks:
- 70% of finance firms have AI risk management plans in place, compared to 63% across all industries.
- 62% support government regulation of AI in data privacy and security, above the 55% all-industry average.
- 55% advocate for ethical AI guidelines, compared to 50% across industries.
- 57% believe AI governance should be the responsibility of the companies deploying AI, while only 26% think it should be led by government entities. In general, finance firms want to be responsible and in control of self-governing AI usage.
While financial firms recognize the need for regulation in areas like data privacy and ethics, they also want the flexibility to develop AI governance frameworks that align with their specific risks and business goals.
The Future of AI in Finance: A 5-Step Checklist
AI is no longer just an emerging technology—it has become a foundational part of the finance sector. To fully harness its benefits while managing risks, IT leaders should focus on implementing the following:
- Defining clear AI use cases – Prioritize applications like fraud detection, compliance automation, and customer analytics.
- Strengthening AI governance – Implement internal policies that align AI adoption with security and compliance requirements.
- Investing in data infrastructure – Ensure AI models are built on high-quality, well-managed data.
- Enhancing cybersecurity defenses – Use AI to detect fraud and mitigate cyber threats in real time.
- Upskilling employees – Provide AI literacy training to help teams adopt and manage AI responsibly.
Taking a strategic, well-governed approach to AI will bring about a long-term competitive advantage—enhancing efficiency, strengthening security, and delivering better customer experiences. Those that fail to address governance and security risks, however, may find themselves vulnerable to compliance failures and repetitional damage.
The future of AI in financial services isn’t just about adoption—it’s about adoption done right. With strong governance, clear use cases, and responsible implementation, finance IT leaders can harness AI’s potential while mitigating risks.
Want more insights? Download Presidio’s AI Readiness Report for expert commentary, industry benchmarks, and strategies to drive secure, responsible AI adoption in your organization.
