Finance resisted AI longer compared to other sectors due to concerns over data quality, compliance, and control. But as pressures mount to deliver faster insights and cut costs, AI is quickly becoming a core part of how finance operates.
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AI in Finance: Key Points
Where AI Is Making an Impact in Finance
Financial teams are using AI to detect fraud, score credit, forecast performance, and manage risk faster and more accurately than before.
Let’s explore where and how financial institutions are applying AI today and the trends that will shape AI in finance over the next few years.
5 Use Cases of AI in Finance
Finance has adopted artificial intelligence (AI), but mostly for internal operations. A 2024 EY study showed that 48% of AI use cases in finance were mostly focused on back-office functions, with only 21% reaching customer-facing roles. A sign that most teams were still playing it safe.
But adoption is picking up.
According to McKinsey, general business usage of AI jumped to 78%. In financial operations specifically, KPMG projected that adoption would reach 83% in the next three years, suggesting the sector is starting to move past its cautious phase.
Where is that shift happening?
Here are key areas where finance teams are already seeing impact, and how the technology is being applied in practice:
- Risk management and compliance
- Fraud detection and cybersecurity
- Credit scoring and lending
- Customer experience and personalization
- Wealth management and advisory
1. Risk Management and Compliance
Risk and compliance demand fast decisions, deep oversight, and more documentation than other departments. But many finance teams still rely on manual processes and siloed systems, increasing the risk of missed red flags, regulatory penalties, and inefficiencies.
AI is closing that gap.
JPMorgan’s COiN platform, for instance, uses natural language processing to scan thousands of complex contracts and extract clauses like NDAs and termination terms — work that once took 360,000 hours annually.
Now, it happens in seconds.
The system helps cut manual review times dramatically while reducing legal risk. It’s part of a broader AI strategy that has delivered up to $1.5 billion in value across use cases like credit and operations.
On the compliance side, companies using Watson — IBM’s AI engine — to track regulations, internal policies, and business activity are seeing similar gains. A Forrester study found that organizations using IBM’s OpenPages platform cut risk management effort by 50%, saving $1.9 million in labor over three years.
2. Fraud Detection and Cybersecurity
Modern financial institutions process billions of transactions annually, but fraudsters are evolving just as fast.
AI is now the core defense. It scans transaction data in real time, flags anomalies, and learns from new patterns at a scale and speed humans can’t match.
Mastercard, for example, uses an AI system to scan up to 160 billion transactions a year. It assigns real-time risk scores and flags potential fraud in just 50 milliseconds — a change that’s significantly improved detection rates.
HSBC uses AI to monitor 1.35 billion transactions monthly, giving teams a faster, more comprehensive view of suspicious activity and enabling quicker fraud response.
3. Credit Scoring and Lending
Traditional credit scoring systems rely on narrow metrics like FICO scores and past borrowing history, often excluding creditworthy borrowers who don’t fit that standard profile.
Goodwin Law found that adding alternative credit data, such as rent, telecom, and utility payments, could help lenders evaluate an additional 19 million more consumers currently underserved by traditional models.
AI helps lenders tap into that market.
Upstart, for example, partners with banks and credit unions to approve loans using AI models that factor in education, employment history, and behavioral data. Its platform approves 44.28% more borrowers than traditional models, often at lower APRs.
Pagaya Technologies does similar work behind the scenes, partnering with fintech companies like Klarna to underwrite “second look” loans. It uses AI to reassess risk and turn previously unscorable applicants into viable customers, helping lenders expand access while managing risk.
4. Customer Experience and Personalization
AI is transforming how financial institutions engage customers, making services faster, smarter, and more personal at scale.
One standout example is Erica, Bank of America's virtual financial assistant. Since 2018, it has handled more than 2 billion interactions, assisting over 42 million clients with everyday financial needs.
Erica provides proactive insights, flags duplicate subscriptions, tracks deposits, and helps users understand spending patterns. More than 98% of users get what they need in under 44 seconds.
Its adoption aligns with performance gains: In Q2 2023, Bank of America reported a 19% rise in net income — hitting $7.41 billion. While many factors are at play, AI-powered tools like Erica are helping the bank streamline operations and deepen client satisfaction.
5. Wealth Management and Advisory
In 2025, wealth management firms are increasingly adopting AI tools to automate routine tasks such as document handling and data analysis. This shift enables wealth managers to dedicate more time to high-touch client interactions and strategic planning, enhancing service quality and productivity.
JPMorgan Chase exemplifies this trend through its implementation of the "Coach AI" tool.
This AI-powered assistant enables private client advisers to access relevant research and content up to 95% faster, facilitating quicker and more personalized client interactions.
As a result, the bank reported a 20% increase in gross sales within its asset and wealth management division between 2023 and 2024.
Furthermore, JPMorgan anticipates that this technology will allow advisers to expand their client portfolios by 50% over the next three to five years.
4 Challenges Slowing Down AI in Finance
While AI offers tremendous benefits, finance leaders must navigate several challenges to implement AI successfully and responsibly:
- Data privacy and security
- Regulatory and ethical compliance
- Integration with legacy systems
- Skills gap and change management
1. Data Privacy and Security
AI in finance depends on large volumes of sensitive data, making strong data protection essential.
Companies must comply with regulations like the EU’s General Data Protection Regulation (GDPR) and California Consumer Privacy Act (CCPA). Failing to do so can be costly. GDPR fines alone reached €2.1 billion in 2023.
AI systems are also vulnerable to adversarial attacks — malicious inputs that exploit model weaknesses to cause faulty predictions. These attacks can lead to serious financial risks if they’re not addressed.
How To Solve This Challenge
Key practices include enforcing cybersecurity measures, restricting access to AI models, and auditing data use regularly.
These steps help organizations comply with privacy laws and reduce vulnerabilities, ensuring that AI systems in finance remain both secure and trustworthy.
2. Regulatory and Ethical Compliance
Financial institutions face strict regulations when using AI, with growing pressure to ensure fairness, transparency, and accountability.
For instance, in the US, the Consumer Financial Protection Bureau (CFPB) requires lenders to clearly explain why credit is denied — even when AI or algorithms are involved — to prevent discrimination and promote transparency.
In Europe, the proposed AI Act would label many financial AI tools as “high-risk,” requiring extra checks to avoid bias and ensure decisions are explainable.
How To Solve This Challenge
To stay compliant, companies must use diverse data, apply bias detection tools, and test AI systems regularly for fairness. Because some AI models operate as “black boxes,” explainability is crucial for meeting regulatory standards and maintaining trust.
3. Integration With Legacy Systems
Many financial institutions still rely on decades-old core systems and siloed databases. These legacy setups weren’t built to support AI, making integration a major hurdle.
Outdated infrastructure often lacks the real-time data flow and computing power AI needs. Data is typically scattered across silos — loans in one system, deposits in another — requiring extensive cleaning and consolidation before AI can be effective.
This complexity is a leading reason why AI projects in finance stall or fail.
How To Solve This Challenge
To address this, financial businesses should be adopting a phased approach:
- Start small in a controlled sandbox to prove AI’s value.
- Invest in modern data architecture like cloud platforms or data lakes.
- Use application programming interfaces (APIs) or middleware to connect legacy systems without full overhauls.
- Collaborate with fintechs that specialize in AI integration for finance.
These strategies help reduce disruption while laying the foundation for scalable AI deployment. But success depends on executive buy-in — CFOs and CIOs must treat infrastructure upgrades as a core part of their AI roadmap, not an afterthought.
4. Skills Gap and Change Management
AI implementation in finance is as much about people as it is about technology.
There's a growing need for professionals who understand both finance and data science, but this hybrid talent is in short supply. CFOs frequently cite the skills gap as a key barrier to effective AI adoption.
Upskilling current teams is just as challenging. Many financial analysts worry AI might replace them, creating resistance and disengagement that can undermine success.
How To Solve This Challenge
To overcome this challenge, leadership must frame AI as a tool to augment human capabilities, not replace them. That may mean redesigning roles to focus on higher-level, strategic tasks as automation handles the routine work.
Key actions include:
- Training staff to interpret and act on AI insights.
- Building data literacy among roles like accountants and risk managers.
- Running pilot programs and hands-on workshops to foster trust.
- Encouraging experimentation, starting with small AI projects that scale based on results.
Ultimately, cultural acceptance is crucial. If end-users don’t trust or adopt AI tools, the technology won’t deliver a ROI. The most successful companies foster a mindset of continuous learning and innovation, where AI becomes a trusted partner, not a threat.
How AI Is Shaping the Future of Finance
As AI adoption in finance matures, the focus is shifting from experimentation to innovation at scale. Emerging trends are reshaping how institutions generate insights, automate operations, and navigate evolving regulatory landscapes.
The next wave of AI in finance will be defined not just by smarter tools but by smarter strategies:
- Generative AI for financial insights
- Autonomous finance with self-driving financial operations
- AI-first operating models in finance
Generative AI for Financial Insights
Finance teams are increasingly turning to generative AI tools like ChatGPT and BloombergGPT to move beyond static reports. These models generate real-time summaries, detect patterns, and produce narrative insights from dynamic datasets — accelerating decisions and improving cross-team collaboration.
According to Gartner, by 2026, 90% of finance functions will adopt at least one AI-enabled technology solution, underscoring the sector’s shift toward AI-powered operations.
Early adopters are embedding these tools into business intelligence (BI) platforms and enterprise resource planning (ERP) systems to automate commentary on KPIs, generate executive-level briefings, and simulate outcomes across different financial scenarios.
As AI technology improves, it’s also changing how financial analysts work, turning them into professionals who use AI every day to make smarter decisions.
Autonomous Finance: Self-Driving Financial Operations
Autonomous finance is moving from theory to reality, with AI systems now capable of managing operations with minimal human input. These tools can analyze data, decide what to do, and respond immediately, not just perform routine tasks.
A prime example is 9M, a New York-based fintech behind NovaMind™ 3.0. This AI engine autonomously manages investment decisions across asset classes, offering a glimpse into the future of fully self-driving financial systems.
These advances promise faster execution, improved accuracy, and leaner workflows. But they also demand clear oversight protocols to ensure accountability and maintain trust as automation scales.
AI-First Operating Models in Finance
As AI moves from pilot programs to enterprise-wide adoption, finance teams are rethinking how they’re structured and how decisions are made. This shift is giving rise to AI-first operating models — organizational frameworks that integrate AI into the core of financial operations.
Rather than treating AI as an add-on, these models are designed to make AI the default across forecasting, planning, reporting, and compliance.
Key features of AI-first finance models include:
- Embedded AI roles within financial planning and analysis (FP&A), treasury, and compliance teams;
- Centralized data infrastructure that supports real-time decision-making;
- Collaborative tooling where finance staff interact with AI copilots in their day-to-day workflows;
- Continuous feedback loops, where user input helps refine AI outputs over time
Companies adopting this model are also seeing shifts in leadership priorities. CFOs are becoming stewards of data strategy, and financial analysts are evolving into AI supervisors — validating outputs, identifying edge cases, and ensuring ethical application.
AI-first doesn’t mean AI-only. The goal is to create finance teams that are faster, smarter, and more resilient; powered by machines, but guided by human judgment.
AI in Finance: Final Notes
Finance leaders are under pressure to deliver faster decisions, tighter controls, and better returns. AI is a practical way to get there, but only when it’s tied to real business priorities.
That means choosing use cases with measurable impact, investing in the right infrastructure, and making sure teams know how to use the output. No posturing, just focused execution with accountability at every step.
The firms treating AI as a core business function, not a technology experiment, are already pulling ahead. If you're ready to accelerate that shift, the right AI agency can help you move with clarity and speed.
AI in Finance: FAQs
1. What roles should CFOs prioritize when building internal AI capabilities?
Start with roles that connect finance expertise with technical execution, such as data analysts with finance backgrounds, AI product managers, and AI governance leads. These bridge gaps between models, decisions, and accountability.
2. How do we evaluate AI vendors or agencies in finance?
Look for domain experience, clear success metrics, integration support, and compliance readiness. Ask for case studies in regulated industries and assess how explainable their models are.
3. Can AI help with board reporting or investor updates?
Yes. Generative AI is now being used to summarize performance data, flag anomalies, and auto-generate commentary for board decks and investor memos, reducing manual prep and improving insight clarity.
