AI is a mission-critical technology in the finance sector, with applications across a wide range of use cases and subdomains, including investment banking, private equity, and small business lending. 

Finance workers can now design autonomous AI agents to create content, automate complex workflows, and conduct advanced analytics, among other capabilities. And with the emergence of no-code AI builder tools, non-technical finance teams can now create AI agents without coding. 

In the following blog, we’ll explain what AI in finance is, including an industry overview, use cases, AI agents currently employed, and no-code solutions for non-technical teams. 

What is AI in Finance?

AI in finance is the use of AI-driven technologies, such as machine learning, natural language processing, and predictive analytics, to automate, enhance, and optimize financial services and decision-making. These technologies process large volumes of structured and unstructured financial data to identify patterns, make predictions, detect anomalies, and continuously improve outcomes.

In finance, AI powers applications like algorithmic trading, fraud detection, credit scoring, risk modeling, customer service chatbots, and personalized financial recommendations. This helps financial institutions reduce costs, improve accuracy, manage risks, and deliver personalized services to customers.

From trading systems, to credit ratings, to customer service, AI powers the most critical operations in the finance sector. AI is the fundamental technology behind everything from split-second stock transactions to risk models that continuously improve through feedback loops.

 Why AI Matters to Finance Teams Today

AI matters to finance teams for a number of reasons. The finance sector employs investors, analysts, lawyers, and many other highly-paid knowledge workers. But these expensive workers spend too much time on manual, uncomplicated tasks, such as analyzing documents, data entry, and designing slide decks.

AI enables finance teams to automate these time-consuming and duplicative tasks, so they can focus on high value work. Instead of spending hours creating a strip profile, investment bankers can automatically convert LLM output into a fully designed slide deck in a few minutes with an AI platform.

Generative AI also empowers finance teams to automate more sophisticated tasks, including generating IC memos, summarizing legal documents, and performing compliance checks on emails. AI agents can execute these tasks autonomously, reducing the time high-salaried knowledge workers spend on them. 

AI systems also enable finance teams to use real-time, predictive insights to make decisions. Finance teams can plan budgeting, forecasting, risk exposure, and more with live data as opposed to static reports. 

AI can simulate complex variables and outcomes, enhancing scenario modeling and what-if analysis. And with AI, finance teams can also improve and automate regulatory compliance, eliminating the risk of noncompliant behavior and financial penalties. 

Benefits of Implementing AI in Companies

The benefits of implementing AI in finance companies is multitudinous, and depends on factors such as use case, team, and company size. Some key benefits of implementing AI in finance companies include:

• Greater accuracy - AI reduces manual errors that often occur in spreadsheets, data entry, and reconciliations. AI agents can validate transactions by cross-checking multiple data sources in real time. 

• Enhanced decision making - AI enables real-time data processing and continuous reporting. Instead of waiting weeks for a quarterly close, finance teams can access up-to-the-minute insights into revenue, spend, and cash position.

• Expanded automation - AI agents automate mundane tasks that previously required human input. This includes invoice processing, expense report categorization, bank reconciliation, and journal entry generation.

• Improved forecasting - For financial modeling, AI moves beyond historical trend analysis, incorporating a wide array of variables. This leads to more granular and adaptable forecasts.

• Smarter risk management - AI identifies risks early, and continuously monitors for exposure. Additionally, AI can help monitor market, operational, and liquidity risks by highlighting early warning signs in the data.

• Streamlined compliance - AI agents streamline compliance for finance teams, allowing them to automatically confirm that emails, marketing materials, disclosures, and much more, comply with regulations. 

These are some of the key benefits of harnessing AI in finance companies, although others are emerging as new use cases arise. 

How Has AI Been Used in Finance in 2025?

Historically, the finance sector has remained ahead of the curve in terms of AI adoption. In the 20th century, the industry began algorithmic trading. By the 1990s, AI experienced wider adoption in the finance sector, with usage in statistical arbitrage strategies and fraud detection systems. 

For the finance sector, machine learning emerged in the 2000s. This enhanced predictive modeling, improving risk assessment, credit scoring, and cash flow analysis, among other segments of finance. ML unlocked greater speed, accuracy, and automation for financial institutions.

With the birth of generative AI, new possibilities have emerged for finance companies. Whereas traditional AI enabled finance teams to make predictions and enhance decision making, generative AI allows them to create content, personalize experiences, and simulate scenarios with greater depth. 

Today, AI is used widely across many areas of finance, including the industry use cases listed below: 

• Fraud Detection - Banks and other financial institutions often utilize machine learning algorithms to detect fraud, including suspicious activity such as large withdrawals. By analyzing massive volumes of data, banks can train their AI models to detect anomalous behavior across millions of transactions.

• Loan Underwriting - Lenders leverage AI agents in their underwriting workflows to assess creditworthiness. This includes transforming unstructured data into structured inputs for credit models, analyzing borrower documents, and extracting information from documents. 

• Portfolio Management - AI is at the core of AI-driven portfolio management. AI algorithms harness large volumes of structured and unstructured data to identify key risk factors, patterns, and anomalies. AI also powers dynamic asset allocation.  

• Risk Management - AI models analyze historical and real-time data to predict risks before they happen. Risk managers can use AI to test thousands of hypothetical market scenarios to prepare for future eventualities. 

• Predictive Analytics - AI systems allow finance teams to forecast outcomes based on past data with higher accuracy. AI trains models to spot patterns and make predictions about the future. This includes regression models, classification models, and deep learning. 

• Customer Service - Banks and fintechs deploy AI agents as support bots to answer customer inquiries. Personalization through AI allows customers to access customized financial advice. 

With the emergence of AI agents, or AI entities that can act without human intervention, finance teams can now outsource complex jobs that require intelligence and autonomous action. AI agents can take on jobs that once required human decision making, such as the following multi-step tasks: 

• Automated financial research and summarization

• Client communication and reporting

• Financial document drafting and review

• Conversational AI

• Regulatory and compliance support 

These are just some of the ways AI is impacting the finance sector in 2025, although other advancements are also emerging.

Examples of Stack AI Usage in Finance

At Stack AI, we’ve worked with some of the world’s leading financial institutions to deploy AI agents, including at banks, PE firms, fintechs, and more. As a no-code AI builder platform, Stack AI empowers non-technical finance teams to create AI agents, without requiring them to code. 

With Stack AI, finance teams design AI agents using a drag-and-drop workflow builder. Teams can leverage LLMs of their choice — including OpenAI, Anthropic, and Perplexity — to power their agents. 

Finance teams can access the AI agent in their web browser, and share it amongst their team. Each agent adheres to enterprise security standards, such as SOC 2 and GDPR, and is ready for deployment in highly secure financial institutions. 

Let’s review some of the top AI agents for finance teams, based on our work with leading financial firms.

Investment Memo Generator

Investment memos provide in-depth analysis of an investment opportunity. VCs and PE firms use these memos to communicate the structure, risks, and potential return on investment for a particular asset. However, generating these memos is a manual and time-consuming process. Analysts must perform document analysis, synthesize sources, and write the memo in a highly regimented format.

With Stack AI, finance teams can instantly launch an AI agent that writes investment memos using a pre-built template. The investment memo generator AI agent allows analysts to draft investment memos based on documents and web sources. 

The AI agent can transform hundreds of pages of unstructured financial documents and live data from the web into a pre-written IC memo. The memo follows industry best-practices, including formatting. This saves analysts hours of work and enables them to focus on high-value work instead. 

Buy vs. Sell Side Agent

In M&A deals, sellers share an information memorandum (IM) with buyers. The IM contains critical details about the company that’s for sale, including business model, market dynamics, and growth strategy. Buyers also prepare their own IM on the company, to compare to the sell-side version. But juxtaposing the two documents is painstaking, highly manual, and prone to error. 

Finance teams, particularly investment bankers, use Stack AI to build buy vs. sell side AI agents. This agent enables bankers to upload two sets of documents — those from the buy side, and those from the sell side. The agent automatically compares the document sets, and provides a summary of the findings. 

On the backend, the documents are fed into an LLM controlled by a pre-written, specialized prompt optimized for comparing complex financial documents. This allows bankers to compare buy and sell side documents in seconds, freeing them up to do more important work on the M&A deal. 

Contract Analyzer

Most finance teams frequently deal with legal contracts, for everything from debt financing, to mergers, to cap table management. Parsing these documents is difficult for finance team members without legal training. And for in-house lawyers, the pile of contracts never stops growing, creating bottlenecks for a wide variety of projects.

That’s why we’ve seen many finance teams we work with develop AI agents that analyze contracts automatically. This enables anyone on the team to upload a legal contract, and receive back an analysis in seconds. Teams can customize the points they want the analysis to focus on. 

They can also upload “gold standard” contracts and compare them against the contract in question. This enables non-expert team members to analyze contracts on their own, and allows lawyers to get through their pile of contracts faster. Stack-AI offers a pre-built template for a contract analyzer. 

10Q/10K Document Extraction

A 10-Q filing offers an unaudited view into a company’s financial performance every three months. A 10-K form is filed yearly and contains more detailed information about a company’s financial performance, business operations, and risks for a company. They are indispensable for financial analysts, but sifting through them to find the right data is time-intensive and error prone. 

With Stack AI, financial analysts can build AI agents that extract key information from 10-Q/10-K forms. This includes information such as risk and uncertainties, debts and financing, and performance. 

The findings of the AI agent are presented in an easy-to-read report for financial analysts. This allows analysts to review 10-Q and 10-K forms much faster, and deliver more relevant insights to stakeholders. 

Data Room Agent

Data rooms are an essential part of the due diligence process. They serve as the secure hub for all the data investors need to make decisions, including audited financial statements, shareholder agreements, finance forecasts, and much more. However, the process of analyzing and synthesizing these documents into defensible findings is arduous and requires multifaceted approaches.

With Stack AI, finance teams can build an AI agent to analyze all of the documents in a data room simultaneously. Using Stack AI’s batch processing interface, teams can upload hundreds of documents from a data room all at once. The agent generates analysis for each individual document. 

Teams can then use Chat with Table to ask questions across all the documents, and synthesize comprehensive analysis. 

The question above yields a synthesis of all the financials in the table. 

This AI agent allows you to analyze and synthesize all data room documents in minutes, rather than hours or days. 

Challenges and Ethical Considerations of AI in Finance

AI in finance raises its own unique challenges and ethical concerns, with potential costs and serious ramifications. These include: 

• Data quality and access - The quality of an AI model depends on the quality of the data it's trained on. Incomplete or inaccurate data can lead to bad predictions and cost financial institutions money.

• Black box risk - Complex models are opaque and it’s hard to determine how they arrived at their conclusions. This is difficult for financial professionals, who are required to explain their reasoning and steps taken. This can lead to errors and policy violations. 

• Security & privacy - AI systems in finance handle highly sensitive personal and business data. Cyberattacks, data breaches, or model manipulation could have serious consequences for customers and markets.

• Regulatory compliance - The finance sector is burdened with significant regulation, including fair lending (e.g., FCRA, ECOA), trading transparency (e.g., SEC, FINRA rules), GDPR/CCPA data privacy laws. Financial firms must ensure that AI systems follow these rules. 

Companies must be aware of the potential challenges of adopting AI in their organizations, and work to mitigate them. 

The Future of Finance Will be Powered by AI

The future of AI in finance will be defined by deeper automation, hyper-personalization, and real-time intelligence at scale. As AI models become more powerful and context-aware, they will move beyond narrow tasks such as approving loans to managing complex, interconnected decisions. AI will power not just chatbots, but also strategic financial planning, capital allocation, and regulatory compliance.

Autonomous finance will become more prevalent. This includes real-time portfolio rebalancing based on market signals, automated budgeting tools, and treasury systems that dynamically manage corporate liquidity. Generative AI will also play a growing role in creating reports, forecasts, and financial products, freeing up human experts to focus on high-value tasks rather than manual analysis.

The  future of AI in finance will require careful attention to ethical considerations, transparency, and governance. As AI systems take on more responsibility in decision-making, ensuring that they operate fairly will be critical. Financial institutions will need to implement rigorous oversight, explainability, and compliance frameworks to maintain trust with customers, regulators, and the public. 

As this new era of AI emerges in finance, no-code AI builder tools will become more essential for non-technical finance teams. To tackle emerging use cases, they will need builder tools to approach this new environment with flexibility. No-code tools enable non-technical users to build AI agents that can analyze data rooms, perform loan underwriting, write IC memos, and many other complex jobs in finance. 

Book a demo with Stack AI to start building your top finance use cases for 2025 and beyond: