AI in financial services: The rise of intelligent finance

Intelligent finance represents the next evolution of open finance, now powered by artificial intelligence (AI). This shift continues to move the industry along its path from digitization of banking to greater data portability and connectivity, culminating in contextual financial intelligence.

The urgency for this transformation is driven by significant market momentum. Global financial services AI spending is projected to reach nearly $100 billion by 2027, signaling large-scale industry investment that goes far beyond simple pilot programs. 

Additionally, the AI market in personal finance alone is projected to grow to approximately $3.7 billion by 2033, reinforcing the long-term growth trajectory for AI-enabled consumer finance. These trends collectively signal that AI in finance is becoming foundational infrastructure, not merely an experimental layer. 

The move towards intelligent finance

The financial services industry has undergone three distinct phases of innovation:

• Phase 1 — Digitization: This foundational phase involves migrating paper-based systems to online banking, implementing early automation, and establishing digital record-keeping. For companies, this drives efficiencies and cost savings. For consumers, this means convenience. Checking balances no longer requires a branch visit. Bill pay moves online. Money transfers become faster. Finance becomes accessible from a computer or a phone.

• Phase 2 — Open Banking & Finance: Characterized by APIs that enable secure, user-permissioned sharing of financial data. This phase is a crucial shift from siloed data toward interoperable ecosystems. Open finance unlocks a wave of fintech innovation for companies. For consumers, it creates more choice, allowing them to connect their bank accounts to budgeting apps, lending platforms, investment tools, and payment services—and take control of how they manage their money.

• Phase 3 — Intelligent Finance: This new phase is driven by AI systems that are designed to interpret financial patterns over time. The focus shifts from merely understanding “what happened” to discerning “what it means,” and determining “what to do next,” embedding financial intelligence within core operational workflows. This is expected to drive another wave of innovation, enabling companies to build more personalized and responsive financial experiences that adapt to consumers’ actual financial behavior.

A recent example of intelligent finance in action is Perplexity’s new Portfolio tool. Portfolio enables users to connect their investment accounts via Plaid to get real-time, personalized insights into their investment holdings and have questions about their portfolio answered in seconds. It’s an interactive financial management experience that perfectly marks the shift to intelligent finance.

Why AI in financial services is accelerating now

AI in financial services is rapidly accelerating due to maturing AI technologies, as well as heavy market investment and fundamental shifts in consumer expectations.

Market investment and institutional adoption of artificial intelligence in finance

AI is no longer confined to ancillary functions like chatbots. It is being embedded across core financial activities—lending, compliance, fraud detection, underwriting, and general operations—delivering measurable performance improvements. 

Institutional readiness is high: 43–46% of financial services organizations are already using generative AI, including Large Language Models (LLMs), which signals a rapid normalization of the technology. Furthermore, leading industry analysis suggests that AI integration could improve banking efficiency ratios by up to 15% when deployed across middle- and back-office functions.

Consumer readiness and expectations around intelligent finance

Consumers are actively engaging with AI-powered tools. Consumer readiness has already ramped up to significant levels, 57% of consumers state they expect fintech apps to use AI, and 78% are open to receiving AI-based personal financial guidance. Already, major players in AI-powered personal finance have emerged, including CashApp’s Moneybot, Sofi’s Cash Coach, and Cleo.

Five real-world use cases for intelligent finance

Intelligent finance drives smarter outcomes across critical financial workflows, including:

1. Personal financial management (PFM): Merchant identification and normalization, along with transaction categorization, are essential to powering accurate spending insights, subscription tracking, and budgeting capabilities. Improved categorization and personalization through AI models will make the PFM user experience more adaptive, reliable, and action-oriented. 

2. Advanced fraud detection: As fraud tactics grow more sophisticated, static rules are proving inadequate. Companies can detect coordinated and emerging fraud patterns by pairing AI tooling with device signals across financial networks to spot and stop fraudulent activity. Fraud intelligence systems, such as Plaid Protect, can be widely deployed across financial institutions to identify anomalous behavior in real time. 

3. Payment risk & authorization: Machine learning (ML) models can be trained to analyze behavioral patterns, transaction history, and contextual signals to reduce fraud exposure while simultaneously improving approval rates. Plaid Signal, for example, uses ML models built on account data and insights from the Plaid Network to accurately predict ACH risk and reduce returns. 

4. Income & employment verification: AI models can also be trained to distinguish between recurring payroll and non-recurring peer-to-peer transfers, dramatically improving the insights lenders use to assess financial health beyond what traditional heuristic rules can achieve.

5. Agentic finance: The adoption of generative AI suggests a growing readiness for financial systems that are capable of not only analyzing data but also taking action with a user’s consent. As these AI agents begin executing complex workflows, including making purchases on a user’s behalf, infrastructure requirements focused on governance and oversight remain critically important.

→ Learn more about how Plaid supports companies building AI experiences, including agentic commerce.

Five key elements needed for intelligent finance to work

The five use cases above don’t work without certain key elements, including secure, verified financial data and models fine-tuned for financial purposes. 

1. Real financial data at scale: Longitudinal financial data across different types of accounts and institutions enables intelligent finance to work at scale. To successfully surface meaningful behavioral patterns, the breadth and depth of this data must be robust, trusted, and accurate. 

2. Models purpose-built for a financial context: General-purpose LLMs are not optimized for structured financial reasoning. Financial AI models should be trained on contextual financial activity and refined using outcome-based feedback.

3. Production-grade infrastructure: AI in financial services must meet regulatory, security, and performance standards. As spending approaches ~$100B industry-wide, investment will be increasingly directed toward operational AI infrastructure. 

4. Data quality & stewardship: Clean merchant identity resolution and transaction normalization are required to reduce noise and increase signal in financial data. High-quality data materially improves model performance.

5. Trust layer & responsible deployment: Transparency and explainability are critical for long-term adoption, consumer trust, and the responsible use of AI in financial services.

Intelligent finance vs. traditional automation

For years, financial institutions have relied on automation to streamline processes, reduce costs, and improve speed. Rules-based systems helped digitize workflows, flag obvious risks, and handle repetitive tasks at scale. But as financial data grows more complex and fraud tactics become more sophisticated, efficiency alone is no longer enough.

Intelligent finance represents the next step. Instead of simply automating tasks, it applies AI in financial services to understand patterns, context, and behavior over time. The difference is not just faster processing, but smarter insights and decision-making.

The future of artificial intelligence and finance

Intelligent finance marks a shift to financial systems designed to recognize patterns and context as financial behavior evolves. As AI becomes embedded in core financial systems, the advantage will go to institutions that can turn data into contextual, adaptive decision-making that drives value for consumers and businesses they serve.

AI spending trends point to embedded intelligence becoming core infrastructure. Institutional adoption is starting to move beyond experimentation to enterprise deployment. And sustained growth in personal finance AI shows that consumers are increasingly interacting with financial intelligence as part of everyday life.

The next generation of financial services won’t be defined by who can automate processes the fastest, but by who can deploy financial intelligence responsibly, at scale, in a way that delivers value and centers on trust.

Learn more about Plaid’s role in the evolution of intelligent finance.