AI in Finance: Applications, Benefits, Risks & Regulation (2026) - Virginia Institute of Finance Management

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February 23, 2026

Published: 2026
Approximate reading time: 7 minutes

Introduction

Artificial intelligence (AI) has moved from laboratories into the heart of the financial system. Since the launch of ChatGPT and other generative models, public interest and investment in AI have exploded. Searches for AIrelated terms and job postings have surged, and 64 % of businesses expect AI to increase productivity. Estimates suggest that generative AI could unlock USD 2.6–4.4 trillion in economic value annually, with banking among the largest beneficiaries. However, these benefits come with risks: concentrated suppliers of foundation models and widespread adoption may magnify operational and market vulnerabilities. Understanding how AI works, where it is being applied, and what challenges it introduces is critical for finance professionals.

Key Takeaways

AI adoption is accelerating. Generative AI and large language models mark a technological leap that allows financial institutions to automate text, code and other content, build predictive models and streamline operations. Spending on AI by financial firms now exceeds spending by the tech industry, and investments are expected to double between 2023 and 2027, topping USD 400 billion.
Common use cases include compliance, risk management and operational efficiency. AI is widely used for surveillance of communications, knowyourcustomer (KYC) and antifraud monitoring, regulatory intelligence, liquidity forecasting, credit scoring, cybersecurity and administrative automation. Most current implementations focus on internal processes and regulatory compliance rather than revenue generation.
Benefits are tangible. AI can reduce false positives in compliance surveillance, improve fraud detection and credit assessments, automate documentation and tax preparation, and personalize financial products—leading to cost savings, improved risk management and additional revenue.
Risks are multifaceted. AI models can hallucinate, embed historical bias and lack explainability, which jeopardizes decision quality. Heavy reliance on external AI suppliers raises operational and cybersecurity risks, and shared models may increase market correlations and systemic vulnerabilities. AIdriven credit scoring may unintentionally discriminate against certain groups.
Regulation is evolving. International bodies such as the Financial Stability Board (FSB) recognise AI’s benefits but warn of vulnerabilities like thirdparty dependencies, market correlations, cyber threats and model risk. Advocates call for agile, forwardlooking regulations that emphasise consumer protection, transparency and accountability. The FSB recommends addressing data gaps, assessing existing policy frameworks and enhancing supervisory capabilities.

What Is AI (and Generative AI)?

AI is an umbrella term covering technologies that enable machines to perform tasks requiring humanlike cognition. The European Central Bank (ECB) distinguishes two broad strands: datadriven machinelearning systems and rulebased systems built on deterministic if/else instructions. Machinelearning models encompass traditional statistical techniques and artificial neural networks. Neural networks attempt to mimic the human brain’s ability to learn from data; their capabilities have expanded dramatically thanks to declining computing costs and larger training datasets.

Generative AI—or foundation models—refers to highly complex neural networks trained on vast amounts of text, images, sound and numerical data in a largely selfsupervised manner. These models can not only classify and predict but also generate new content, such as writing humansounding text, summarising documents, writing code and even creating synthetic financial data. Foundation models form the basis of tools like ChatGPT and are rapidly being adapted for financial applications—from producing research reports to automating compliance documentation.

AI Use Cases in Finance

Compliance Surveillance and Financial Crime Monitoring

Brokerdealers and banks employ AIpowered surveillance tools to monitor communications across emails, social media and messaging channels. These systems move beyond simple keyword searches to riskbased models that recognise tone, slang or coded language, reducing false positives and freeing compliance staff to focus on highrisk alerts. For KYC and antimoneylaundering (AML) programmes, AI uses machine learning, natural language processing (NLP) and biometrics to detect money laundering, terrorist financing and market manipulation more accurately. Firms report that such tools dramatically reduce the volume of alerts and improve the precision of customer risk assessments.

Regulatory Intelligence and Governance

Regulatory requirements change constantly. AI tools can read, interpret and summarise new rules, enforcement actions and noaction letters, allowing financial firms to digitise regulatory intelligence and update their compliance programmes more efficiently. The FINRA report notes that some regulators are exploring machinereadable rulebooks to enable automated mapping of rules to internal processes. At a macro level, authorities themselves use AI to enhance supervisory analytics, though adoption remains cautious.

Liquidity, Cash and Credit Risk Management

Market participants use machinelearning models to analyse historical and current market data, predicting intraday liquidity needs, working capital requirements and securitieslending demand. AIbased creditscoring systems assess counterparty creditworthiness more quickly, incorporating nontraditional data sources such as social media signals. While these models speed up lending decisions and expand access to credit, they can amplify unfairness if the underlying data contain historical biases.

Risk Management and Fraud Detection

AI enhances market, liquidity and operational risk management by analysing large datasets to detect anomalies and emerging patterns. Banks deploy AI for fraud detection, using predictive models and pattern recognition to spot unusual transactions and reduce false positives. For example, J.P.Morgan reported that AIpowered payment validation reduced account validation rejection rates by 20 %. In risk modelling, AI can improve scenario analysis and stress testing, but regulators warn that reliance on opaque models may make it difficult to trace errors and could weaken risk management.

Cybersecurity and Insider Threats

Cyberattacks on financial institutions are increasing in frequency and sophistication. According to an industry survey, 69 % of organisations believe AI is necessary to respond to cyberattacks. Machinelearning systems can learn normal user behaviour patterns and flag deviations in real time, helping security teams detect phishing, insider threats and data exfiltration faster and at lower cost.

Administrative Automation

Financial firms are automating highvolume, repetitive tasks using AI technologies such as computer vision (CV) and NLP. Examples include processing faxed trade orders, depositing physical checks, searching and retrieving documents, and automating contract review. AI systems extract relevant clauses from legal agreements and prospectuses with higher accuracy than manual reviews, reducing processing times and operational errors.

Customer Service and Personalisation

Generative AI chatbots and digital assistants can handle routine customer inquiries, automate onboarding and tailor products to individual needs. Banks are using generative models to deliver personalised investment recommendations. Bank of America, for instance, leverages AI to suggest tailored investment strategies for customers. These tools help institutions meet growing expectations for seamless, ondemand service while freeing human advisers to focus on complex tasks. However, algorithmic bias in recommendation engines may lead to discriminatory outcomes.

Capital Markets and Investment Research

AI algorithms underpin algorithmic trading and portfolio optimisation, analysing market data at high frequency to execute trades. Generative models assist with investment research, producing summaries of earnings calls, regulatory filings and news. The FSB notes that most AI use cases in finance still focus on internal operations and compliance, with revenuegenerating applications—such as trading strategies—being less common. This suggests that while algorithmic trading and roboadvisory services are emerging, they remain a small part of AI’s overall footprint in financial services.

Benefits of AI in Finance

Greater efficiency and cost savings. AI automates labourintensive tasks—from compliance monitoring to document processing—reducing operational costs. A 2023 EY report highlights that AI can dramatically cut fraud rates and operational errors; J.P.Morgan’s AIpowered screening, for example, reduced validation rejections by 20 %. Automating routine tasks allows institutions to redeploy staff to highervalue activities and accelerate service delivery.
Improved risk management. Machinelearning models can assess creditworthiness more accurately than traditional scorecards by analysing a wide array of data. AI also enhances stress testing and scenario analysis, detecting anomalies that signal market or liquidity risks. Better fraud detection and AML surveillance reduces losses and compliance penalties.
Enhanced revenue generation. Personalised products and predictive marketing campaigns powered by AI can boost customer engagement and crossselling. AI tools reveal new business opportunities and optimise pricing strategies. Although revenuegenerating applications remain less common than operational uses, early adopters like Bank of America demonstrate the potential for AI to drive new income streams.
Better decisionmaking. AI’s ability to extract and synthesise information from multiple sources enables faster, datadriven decisions. Realtime analysis of news, market data and customer behaviour can improve trading strategies, credit decisions and risk assessments. The ECB notes that AI could significantly improve available information, leading to more precise decisionmaking.
Regulatory compliance and transparency. AI can automate monitoring of regulatory changes and ensure consistent application of rules across jurisdictions. This reduces the risk of noncompliance and eases the burden of regulatory reporting.

Challenges and Risks

Data quality and hallucination. Generative models sometimes produce plausible but incorrect outputs (hallucinations). If financial decisions are based on flawed AI predictions, the results can be costly. Poorquality or biased training data may perpetuate discrimination, especially in credit scoring and roboadvice.
Algorithmic bias and fairness. AI systems learn from historical data that may encode discriminatory patterns. In lending, AI credit models have been criticised for using demographic proxies that systematically disadvantage certain groups. In customer service and marketing, biased recommendation engines could lead to unfair pricing or exclusion.
Explainability and model risk. Many AI models—particularly deep neural networks—are opaque (“black boxes”). This makes it hard for firms to interpret outputs, identify root causes of errors and defend decisions to regulators or customers. The FSB flags model risk and governance as key vulnerabilities that could amplify systemic risk if not managed properly.
Operational and thirdparty risk. Financial firms increasingly rely on external providers of foundation models and cloud services. This concentration raises the risk that failures or cyberattacks at a single provider could disrupt multiple institutions. Overreliance on AI could also weaken manual processes, increasing fragility.
Market correlation and systemic risk. If many institutions use similar AI models and data, their decisions may converge, increasing market correlations and potential herding. Such synchronised behaviour could exacerbate volatility during stress events.
Cybersecurity threats and misuse. AI can both defend against and facilitate cyberattacks. Malicious actors could use generative models to craft convincing phishing emails or manipulate markets with deepfake news. Financial firms must balance AI’s defensive advantages against these new attack vectors.
Regulatory gaps and evolving standards. Regulators have begun to issue policy statements and propose rules on AI, but standards remain nascent. Better Markets highlights the need for affirmative regulatory standards, enhanced enforcement and more resources to keep pace with AI’s rapid development.

Regulatory Landscape and Best Practices

International bodies and national regulators are responding to AI’s rapid uptake with guidelines and rulemaking:

Financial Stability Board (FSB). The FSB’s 2024 report notes that most AI use cases focus on internal efficiency and compliance and highlights vulnerabilities such as thirdparty dependencies, market correlations, cyber threats and model risk. It recommends: (1) addressing data gaps to better monitor AI adoption; (2) assessing whether existing regulatory frameworks adequately cover AIrelated risks; and (3) enhancing supervisory capabilities to oversee AI applications.
European Union (EU). The ECB warns that widespread adoption of AI and concentration of model suppliers could increase operational risks and herding. The EU’s Artificial Intelligence Act (expected to enter into force soon) aims to classify AI systems according to risk and impose requirements on highrisk applications, including in finance.
United States. U.S. regulators are taking initial steps. The Securities and Exchange Commission has proposed rules on predictive analytics and generative AI; and agencies such as FINRA and the Federal Reserve have issued guidance on AI use in credit and trading. Advocacy groups argue that more agile, forwardlooking regulation is necessary to ensure consumer protection, ethics, transparency and accountability.

Best practices for financial institutions adopting AI include:

Establish robust governance and model risk management. Firms should implement clear policies for AI development, validation and oversight. Human oversight should remain central to ensure accountability and prevent overautomated decisionmaking.
Ensure data quality and fairness. Data should be assessed for bias and representativeness, with techniques to mitigate discriminatory effects. Regular audits can detect and correct drift and performance issues.
Prioritise explainability and transparency. Use interpretable models where possible and develop methods to explain complex models. Document model design, assumptions and limitations for regulators and stakeholders.
Strengthen cyber resilience. Integrate AI into cybersecurity programs to detect threats but also invest in training and controls to prevent misuse. Vet thirdparty providers carefully and diversify suppliers to reduce concentration risk.
Align with emerging regulations. Keep abreast of evolving standards and engage with regulators. Adapt compliance frameworks to integrate AI-specific requirements, such as those proposed in the EU AI Act and FSB guidance.

Conclusion

AI is transforming finance, from automating routine processes to enhancing risk management and enabling personalised services. The technology promises significant efficiency gains and new revenue opportunities, yet its risks—from data bias and model opacity to systemic vulnerabilities and cyber threats—cannot be ignored. Effective adoption requires balanced governance, transparent models, high-quality data and coordinated regulation. As generative AI continues to evolve, financial institutions must cultivate a culture of responsible innovation that harnesses AI’s benefits while safeguarding customers and the stability of the financial system.

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