Generative AI in finance

Finance leaders face an increasingly demanding role—CFOs must navigate a complex landscape, whether negotiating the intricacies of digital transformation or adapting to changing consumer spending habits.

At the same time, the financial services industry is undergoing a profound transformation as generative artificial intelligence (gen AI) reshapes how institutions operate and manage potential risk. Using advanced language models and machine learning algorithms, gen AI is increasingly automating and streamlining a wide range of finance processes—including financial analysis, asset management, reporting, procurement and accounts payable.

Finance processes are among the most critical functions within an organization, leaving little room for error. Generative AI, along with targeted automation tools, can reduce errors and boost efficiency in the financial sector. By synthesizing information from multiple sources, these technologies help institutions gain meaningful competitive advantages.

For example, according to research from IBM, organizations that have effectively implemented AI in finance experienced the following benefits:

• 33% faster budget cycle time
• 43% reduction in uncollectible balances
• 25% lower cost per invoice paid

But to harness the power of generative AI, CFOs and finance teams must carefully consider a range of variables, with a focus on risk assessment and data governance. Successfully integrating AI into finance operations requires a strategic and well-planned approach. 

“To successfully integrate AI into finance operations, it’s essential to take a strategic and well-planned approach,” says Monica Proothi, Global Finance Transformation Leader at IBM. “AI and gen AI initiatives can truly be as successful as the underlying data permits.” 

Generative AI refers to artificial intelligence systems that create new content. In finance, these systems typically use large language models (LLMs) and other machine learning algorithms to understand financial data, regulations and market dynamics. These AIs then generate human-like responses, analysis, recommendations or insights.

The effectiveness of AI models in financial applications depends heavily on sophisticated data-management processes that transform raw information into AI-ready datasets. This means that financial institutions often integrate data from multiple sources. This raw data then undergoes extensive cleaning and standardization to address inconsistencies or duplicates. 

Generative AI use cases differ depending on a particular organization’s needs, but some of the most common deployments include: 

Generative AI systems power sophisticated chatbots and virtual assistants that handle complex customer inquiries or guide users through processes like loan applications. AI assistants maintain context across conversations and provide personalized advice based on individual customer profiles, freeing up human finance agents for more creative work. 

Financial institutions deal with enormous volumes of documents daily, from loan applications to regulatory filing. Generative AI technology extracts key information and summarizes lengthy documents. Gen AI tools also have the capacity to generate standardized reports from unstructured data—an especially valuable feature for compliance reporting and contract analysis. 

Some finance professionals use gen AI to synthesize vast amounts of data from multiple sources to generate financial market analysis. The technology can analyze a wide variety of information from multiple sources, including earning calls and financial statements, to produce comprehensive investment documents and risks assessments. Using forecasting and modeling tools, finance teams glean deeper insights to guide investment strategies, among other. 

Gen AI analyzes patterns in transaction data and generates fraud scenarios, providing an early-warning sign for suspicious activity. 

Gen AI assists with regulatory compliance by analyzing records and ensuring documentation meets regulatory standards. It can also generate reports and update records to stay current with changing requirements. 

Financial technology teams use generative AI to write and optimize code for trading algorithms, risk management systems and data processing pipelines—ultimately increasing security across an organization. 

Generative AI’s capabilities transform financial workflows across an organization, creating new efficiencies that fundamentally change how work gets done. At IBM, we’ve identified four core finance areas where generative AI shows the most promise:

• Order to cash cycle
• Financial planning and analysis
• Record to report process
• Procure to pay workflow

Over the course of the customer transaction lifecycle, generative AI-driven tools personalize customer interactions and streamline operations

AI can generate intelligent order recommendations based on customer history and market conditions. It also creates personalized sales documents or contracts tailored to specific customer needs. 

Generative AI has the capacity to automatically generate accurate invoices, send personalized billing communications, and send payment reminders. 

Generative AI can monitor payment patterns and generate insights based on historical data, as well as create automated follow-up communications tailored to particular customers. 

AI tools can generate personalized collection strategies and automate the development of payment plans. 

With generative AI, finance teams analyze deduction patterns and create recommendations for process improvements to reduce discrepancies—as well as automating key adjustment processes as approved by human employees. 

Generative AI enhances forecasting accuracy and provides deeper insights into business performance. Using these tools, finance teams collate large and occasionally disparate pieces of information to provide a 360-view of market dynamics and internal practices.

For example, the fintech startup Edger Finance launched a series of AI-assisted processes that simplified the creation of CEO summaries from business’ quarterly reports. These tools also allowed investors to interact with report data through a question-and-answer flow chart. Also, these programs provided more personalized investment data to clients and employees.

With generative AI, finance departments generate comprehensive financial plans integrating multiple scenarios and produce analyses that help leaders understand potential outcomes under different conditions. 

Generative AI automates budget creation based on pricing strategies and other historical data, creating detailed budget documentation. 

AI-powered dashboards and management reports provide leadership with outputs displaying a comprehensive view of financial performance. Generative AI also automatically generates insights into trends and anomalies, equipping enterprises with real-time, actionable information.

Generative AI creates sophisticated financial models that incorporate multiple variables and scenarios, generates forecast documentation and produces analyses to help management evaluate financial strategies. These models provide leadership with critical information during key decision-making processes. 

Generative AI streamlines the record-to-report process by automating routine tasks, enhancing accuracy and providing deeper analytical insights into financial data. For example, by using AI and Robotic Process Automation, IBM Finance created a streamlined process for validating inputs against the ledger and generating journal data for review. The new process saved an estimated USD 600,000 in costs and reduced cycle time by 90%.

AI generates standard journal entries based on business transactions, freeing up finance teams to focus on more sophisticated work. 

Generative AI automates account reconciliations by matching transactions across systems.

AI automatically identifies and matches intercompany transactions across entities, creating detailed reports for regulatory compliance.

AI generates financial statements and regulatory reports and produces customized documentation for different stakeholder groups and regulatory requirements.

Generative AI optimizes the procure-to-pay process by improving vendor selection, automating approvals and enhancing payment processing. Recently, the B2B data vendor Dun & Bradstreet introduced an AI-powered pilot that included an assistant designed to accelerate supplier risk analysis through conversational language. This innovation helped reduce the time required for procurement tasks by 10–20%.

Generative AI analyzes business requirements and generates optimal sourcing strategies, automating vendor selection based on specific criteria. It also creates requisition requests, contracts and vendor agreements tailored to specific business needs. 

AI automatically processes vendor invoices and matches them to purchase orders and receipts, as well as generating payment recommendations and approval workflows based on business rules. 

Generative AI dramatically reduces the time required for document creation, data analysis and other routine tasks. What once took hours or days can often be completed in minutes, allowing human professionals to focus on higher-value strategic activities.

AI assistants and other generative AI powered tools enable 24x7 customer support. Customers receive immediate, accurate responses to complex queries, leading to higher engagement rates.

Generative AI automates routine tasks and minimizes the need for manual intervention across many financial processes. As a result, financial institutions can significantly reduce operational costs. The technology particularly excels at scaling services without proportional increases in staffing.

Generative AI synthesizes vast amounts of information quickly, providing decision-makers with comprehensive analysis and multiple perspectives on complex financial situations. This capability leads to more informed and timely decisions across all areas of finance.

Financial institutions can now provide highly personalized services to millions of customers simultaneously. Generative AI creates customized communications, recommendations and services tailored to individual users but are delivered efficiently at scale.

Implementing generative AI in financial services requires a structured, phased approach that balances innovation with risk management. The following steps provide a roadmap for organizations looking to harness the power of generative AI while maintaining operational stability and regulatory compliance.

Developing a comprehensive strategic plan forms the foundation of successful generative AI implementation. This plan should align AI initiatives with broader business objectives, identifying specific areas where generative AI creates the most value while considering organizational readiness and resource requirements.

The strategic planning process also often involves conducting a thorough assessment of current capabilities and infrastructure, particularly around data readiness. “Enterprises often undertake various data initiatives to support their AI strategy,” says Proothi, “ranging from process mining to data governance.”

Data preparation represents one of the most crucial and time-intensive aspects of generative AI implementation. Financial organizations must audit their existing data assets, identifying sources, quality issues and integration challenges that might impact AI performance.

The data preparation process also involves establishing robust data governance frameworks that help ensure information quality and security. Successful organizations create standardized data collection and processing procedures with clear documentation and ownership rules, as well as shoring up cybersecurity practices to protect sensitive information. 

“After the right data initiatives are in place, building the right structure is essential to successfully integrate gen AI into finance operations,” says Proothi. This approach can be achieved by defining a clear business case articulating benefits and risks, securing necessary funding and establishing measurable metrics to track ROI.”

Clear goal definition helps ensure that generative AI implementations deliver measurable business value and align with organizational priorities. Goals should be specific, measurable, achievable and relevant. They should also provide clear metrics for success, creating specific rubrics to guide implementation decisions and resource allocation. 

Identifying and automating labor-intensive, repetitive tasks provides immediate value and builds organizational confidence in generative AI capabilities. These initial process automation implementations serve as proof-of-concept projects that demonstrate AI’s value while minimizing risk and complexity.

“Automate labor-intensive tasks by identifying and targeting tasks that are ripe for gen AI automation,” says Proothi. “Start with risk-mitigation use cases and encourage employee adoption aligned with real-world responsibilities.” 

Successful generative AI implementation requires ongoing refinement and optimization based on performance data, user feedback and changing business requirements. Organizations should establish regular review cycles that evaluate AI performance, identify improvement opportunities and adjust implementation strategies based on lessons learned.

Successful businesses also progressively integrate more complex gen AI workflows and tools into their processes. “It’s important to use gen AI to fine-tune FinOps by implementing cost estimation and tracking frameworks and by simulating financial data and scenarios,” Proothi notes. “These capabilities also help improve the accuracy of financial models, enhance risk management and support strategic decision-making.”