What is artificial intelligence (AI) in finance?

Unlike traditional software, AI systems mimic human intelligence and reasoning, and can learn over time, continuously improving as they process new information. The resulting advancements of fintech allow financial institutions to increase efficiency, reduce risk and deliver more personalized services. It powers applications like credit scoring, fraud detection, algorithmic trading, portfolio management, regulatory compliance and customer service.

By identifying patterns and making real-time predictions, AI helps institutions streamline operations and respond more effectively to market and customer demands.

The finance industry relies on data-intensive processes and real-time decision-making. AI tools can process large volumes of data quickly and accurately, making it possible to address challenges. Key areas where AI can help include:

Operational efficiency: AI-powered automation reduces manual workloads, streamlines processes and minimizes errors.

Risk management: AI models help assess potential risks more accurately and detect fraudulent activities in real time.

Customer experience: AI enables personalized interactions, such as tailored financial advice and responsive customer service through chatbots and virtual assistants.

Regulatory compliance: AI simplifies compliance by automating monitoring and reporting processes, which helps institutions navigate complex regulations.

Competitive advantage: Institutions that use AI can optimize costs, innovate faster and offer improved services, which might give them a better position in competitive markets.

As intelligent technologies evolve, AI is becoming a key driver of innovation and growth in the financial sector. These artificial intelligence advancements enable smarter, faster and more scalable solutions that redefine traditional banking and investment models.

AI is being applied across many areas of the financial ecosystem. Here are some of the most common use cases for AI in finance:

AI-driven algorithms are a part of today’s financial markets, enabling high-frequency trading (HFT) and complex investment strategies. These systems can perform more complex data analytics and analyze vast datasets, including historical market data, news sentiment and real-time price movements, to execute trades at speeds and scales beyond human capability.

• Example: Trading platforms use reinforcement learning to simulate market scenarios and adapt dynamically to changing conditions.
• Impact: Algorithmic trading might enhance profitability and assist in risk management for traders and institutions.

From automating expense management to simplifying compliance monitoring, AI-powered tools enable organizations to handle growing transaction volumes while maintaining accuracy and consistency. These advancements improve productivity and support the scalability of financial operations.  

• Example: IBM’s watsonx Orchestrate® automates journal entries, cutting cycle times by over 90% and saving USD 600,000 annually.
• Impact: Workflow automation might lead to reduced operational costs and free human employees for higher-value activities.

Traditional credit scoring methods often rely on limited datasets, such as income and credit history. AI expands the scope of credit decisions by incorporating alternative data sources, such as utility payments, social media activity and geolocation patterns. This approach can improve access to credit for individuals who lack traditional credit histories.

• Example: Credit unions that implemented one AI model reportedly experienced a 40% increase in credit approvals for women and people of color.¹
• Impact: AI-driven credit scoring models might be able to reduce bias, increase loan approval rates and lower default risks.

AI-powered chatbots and virtual assistants handle routine customer inquiries, providing instant responses and freeing up human agents to focus on more complex issues. Natural language processing (NLP) enables these conversational AI systems to understand and respond to customer needs effectively.

• Example: In one case study, Bank of America’s Erica chatbot has handled over 2 billion customer interactions, assisting with tasks like balance inquiries and bill payments.²
• Impact: Chatbots might make it easier to offer 24/7 support and deliver faster responses to routine inquiries.

AI fraud detection efforts use deep learning algorithms and predictive analytics to track transaction patterns in real time to identify anomalies that might indicate questionable activity. Machine learning models can adapt to new fraud tactics, improving detection accuracy while reducing false positives.

• Example: A credit card company might use AI to spot unusual spending behavior, such as a sudden large purchase in a foreign country, and move to prevent unauthorized transactions.
• Impact: 90% of financial institutions are using AI to expedite fraud investigations and detect new tactics in real-time.³

AI is transforming insurance operations by automating underwriting and claims workflows by using tools like natural language processing and image recognition to analyze documents, photos and unstructured data. It enables faster risk assessment, personalized policy pricing and quicker claims settlements.

• Example: AI-powered systems can process damage photos and text-based claim forms in seconds, speeding up approvals for common issues like windshield repairs.
• Impact: Insurers see efficiency gains, faster settlements and stronger fraud detection with AI-powered automation. 

AI tools are used for advanced data analysis that can identify market trends and optimize financial and investment portfolios. These systems can provide actionable insights for both individual investors and institutional asset managers.

• Example: As of 2025, 91% of asset managers are using or plan to use AI for portfolio construction and research—up sharply from 55% in 2023.⁴
• Impact: AI expands access to sophisticated investment strategies, making them available to a broader range of investors.

Predictive analytics uses AI models to identify patterns in historical data and predict future outcomes. This capability is valuable for financial institutions seeking to anticipate risks and spot new opportunities. It also supports tasks such as cash flow management, where AI forecasts liquidity needs.

• Example: AI systems analyze global economic indicators to forecast market trends, enabling businesses to make proactive decisions about investments or resource allocation.
• Impact: Financial institutions that use predictive models might improve strategic planning and operational efficiency by leveraging insights into cash flow trends, price movements and market dynamics.

The financial services industry operates under stringent regulatory requirements. AI systems help with compliance by automating the monitoring of transactions and detecting suspicious activities. They also help institutions stay up to date on evolving regulations.

• Example: AI tools scan legal texts and adjust internal policies to ensure adherence to new regulations.
• Impact: Financial institutions that use AI for antimoney laundering efforts might be able to respond faster to issues and improve the accuracy of their monitoring systems over time.

AI provides several advantages for financial institutions, including:

Improved risk management: AI systems offer deeper insights into credit risk, fraud detection and market volatility.

Enhanced efficiency: Automation reduces manual effort and streamlines workflows.

Personalized experiences: AI enables financial institutions to tailor products and services to individual customer needs.

Scalability: AI systems can handle growing transaction volumes without compromising speed or accuracy.

Expanded financial inclusion: AI makes it possible to assess creditworthiness by using nontraditional data sources, increasing access to financial services and making credit decisions fair.

While AI has significant potential, it also presents challenges that financial institutions must address. These challenges include:

• Bias in algorithms: AI models trained on biased datasets can include algorithmic bias that might produce discriminatory outcomes, particularly in areas like credit scoring.
• Lack of explainability: Complex AI systems often function as "black boxes," making it difficult to explain their decisions to regulators and stakeholders.
• Cybersecurity risks: AI systems are vulnerable to cyberattacks, including adversarial attacks and data breaches.
• Data privacy concerns: Managing sensitive financial data raises questions about security and compliance.
• Regulatory uncertainty: The absence of standardized global regulations complicates the implementation of AI systems.

To mitigate these risks, institutions are adopting governance frameworks, prioritizing ethical AI practices and ensuring transparency in AI-driven processes.

A diverse set of stakeholders implement, operate, regulate and use AI technologies in the financial sector. These participants include:

Financial institutions: Banks, insurers and investment firms adopting AI to enhance operations and customer experiences.

Technology leadership: Chief information officers (CIOs) and chief technology officers (CTOs) make key decisions regarding AI implementation, usage and security.

Executive leadership: Top executives and the board of directors make strategic decisions regarding the implementation and use of AI initiatives and their proper management.

Technology providers: Companies developing AI tools, platforms and infrastructure.

Regulators: Authorities ensuring AI systems comply with laws and do not pose systemic risks.

Customers: End-users benefiting from personalized services and improved accessibility.

The use of AI in finance requires monitoring to ensure proper use and to minimize potential risk. Proactive governance can drive responsible, ethical and transparent AI usage, which is critical as financial institutions handle vast amounts of sensitive data. Key components of effective AI governance include:

Ethical guidelines: Establishing principles for fairness, transparency and accountability.

Regulatory frameworks: Governments and organizations like the EU and OECD are developing standards for AI governance.

Risk management: Financial institutions must implement robust controls to mitigate risks associated with AI adoption.

Explainable AI (XAI): Ensuring AI systems are interpretable and their decisions can be justified.

Many AI applications are already well established in the financial services field. However, advancements and emerging technologies are poised to shape the future of finance and the finance industry’s digital transformation in new ways.

Generative AI has gained attention for its ability to create realistic content. Going forward, its future applications in finance might include more sophisticated use cases, such as scenario modeling for risk management, synthetic data generation for training machine learning models and advanced fraud simulations. These gen AI capabilities could enhance decision-making by providing financial institutions with more insights into potential challenges and opportunities.

Large language models (LLMs) are useful for tasks like customer service and document analysis, but the next generation of AI systems—large reasoning models (LRMs)—might take this potential further. LRMs are designed to perform complex analytical reasoning, which helps them simulate intricate financial scenarios, optimize portfolios and assess credit risk with more precision. It could help financial institutions address challenges that require deeper contextual understanding and strategic planning.

AI agents capable of managing entire workflows autonomously are expected to become more sophisticated. These agents could handle complex processes such as expense management, compliance monitoring and cash flow forecasting without requiring human intervention. By integrating natural language processing, decision-making algorithms and contextual awareness, autonomous AI agents can significantly reduce operational bottlenecks and improve efficiency across financial organizations.

Amid concerns about data privacy and cybersecurity, decentralized AI systems might be a potential solution. These systems process data locally rather than relying on centralized servers, reducing the risk of breaches and ensuring compliance with stricter data protection regulations. In the future, decentralized AI could enable financial institutions to implement secure, privacy-preserving solutions for tasks such as fraud detection and identity verification.

While real-time fraud detection is already a critical application of AI, future efforts are going to focus on scaling these systems to handle increasingly complex and high-volume transaction environments.

Growth in distributed AI architectures and edge computing might allow fraud detection systems to process data closer to its source, reducing latency and improving response times. Also, these systems are likely to incorporate more diverse data sources, such as biometric authentication and behavioral analytics, to enhance accuracy.

Embedded finance—the integration of financial services into nonfinancial platforms—is becoming more widespread. AI is expected to play a role in making these experiences more personalized. Future AI systems might leverage real-time user data to offer tailored financial products, such as customized lending options or investment recommendations, directly within e-commerce platforms, social media apps or other digital ecosystems. This trend could affect how consumers interact with financial services.

As quantum computing and quantum technologies mature, they could revolutionize areas such as portfolio optimization, risk modeling and cryptographic security. Financial institutions might use, for example, quantum algorithms to solve optimization problems that currently are computationally impossible, leading to more efficient resource allocation and improved forecasting accuracy.

Hybrid cloud architectures are becoming essential for scaling AI solutions. By integrating on-premises and cloud-based systems, financial institutions can achieve greater flexibility and scalability. In the future, hybrid cloud environments might support the deployment of AI models across diverse business functions, from compliance to customer service.

Environmental, social and governance (ESG) considerations are becoming increasingly important in finance, and AI is expected to play a larger role in advancing sustainability initiatives. Future AI systems might provide more granular analyses of ESG metrics, helping institutions align their investments with environmental goals. Also, AI might assist in tracking carbon footprints, evaluating climate-related risks and identifying opportunities for green financing.

AI is already used to expand access to financial services in underserved markets. In the future, AI-powered platforms might expand to include alternative data sources—such as mobile phone usage patterns or agricultural yields—to build financial profiles for unbanked populations. It could enable more people to access credit, savings accounts and insurance products, contributing to greater economic inclusion worldwide.