20 Best AI Tools for Finance

In the rapidly evolving world of finance, staying ahead means leveraging the power of advanced technology. One such technological marvel making waves is the Financial LLM, a specialized AI model designed to understand and process complex financial data. Consider being able to analyze vast amounts of financial information in seconds, enabling you to make informed decisions faster than ever before. This is not a distant future scenario but a current reality with the best AI tools for finance. In this guide, we will explore how these tools can help you write and research faster with AI, transforming the way you work with financial data.

As we unravel the capabilities of these AI tools, you'll discover how they can enhance your productivity, accuracy, and decision-making in the financial sector. This is just the tip of the iceberg when it comes to the benefits of integrating AI into your financial workflow. Next, we introduce Otio's AI research and writing partner, a powerful solution designed to help you achieve your objectives with ease.

Table Of Contents

• Use Cases of AI in Finance

• Benefits of Using AI Tools in Finance

• 20 Best AI Tools for Finance

• 7 Tips to Use AI in Finance

• Supercharge Your Researching Ability With Otio — Try Otio for Free Today

Use Cases of AI in Finance

1. Speech Recognition and Transcription

In the financial sector, clear communication is critical. Speech recognition technology enables the conversion of spoken language into written text, facilitating the accurate and efficient documentation of customer interactions. This capability is handy in contact centers, where sales calls and customer service conversations can be transcribed for further analysis. By understanding the nuances of customer communication, financial institutions can enhance their service delivery and tailor experiences to meet client needs.

2. Sentiment Analysis for Financial Insights

The ability to gauge the sentiment behind text is invaluable in finance. Natural language processing (NLP) enables the analysis of emotional tone in various textual sources, including investment research, social media chatter, and customer feedback. Identifying whether the prevailing sentiment is positive, negative, or neutral helps financial professionals make informed decisions and respond appropriately to market dynamics or client concerns.

3. Anomaly Detection for Security and Compliance

Financial organizations handle vast amounts of data daily, making it challenging to identify irregularities manually. Anomaly detection algorithms can identify unusual patterns that may indicate fraudulent activity, financial crime, or cyber threats. These systems enhance security by alerting personnel to potential risks, enabling swift intervention to protect assets and maintain regulatory compliance.

4. Personalized Recommendations for Financial Products

Every customer has unique financial goals and risk tolerances. AI-driven recommendation engines analyze individual preferences, behaviors, and peer interactions to deliver customized suggestions for banking services, investment portfolios, and insurance options. This personalization enhances client satisfaction and enables institutions to differentiate themselves in a competitive market.

5. Multilingual Translation for Global Reach

As financial services become increasingly globalized, the need to communicate effectively across languages grows. Machine translation technology allows for the rapid conversion of content, such as financial news and application interfaces, into multiple languages. This capability ensures that customers worldwide can access accurate information and engage with their financial providers in their preferred language.

6. Document Processing for Operational Efficiency

Handling large volumes of paperwork is a common challenge in the finance industry. AI-powered document processing tools can extract relevant data from both structured forms and unstructured text, thereby streamlining workflows for tasks such as loan approval and investment analysis. By automating these processes, organizations reduce errors, save time, and free up staff to focus on higher-value activities.

7. Image Recognition for Visual Data Analysis

Financial institutions can leverage image recognition technology to interpret visual data, such as photos and videos. This is particularly useful in the insurance sector, where AI can assess property damage to expedite claims processing. Additionally, image analysis assists with identity verification during customer onboarding, ensuring compliance with Know Your Customer (KYC) regulations.

8. Conversational AI for Enhanced Customer Service

Deploying AI-powered chatbots and virtual assistants enables financial organizations to provide round-the-clock support with human-like interactions. These conversational agents can handle routine inquiries, guide users through complex processes, and even offer financial advice. By improving accessibility and reducing wait times, chatbots enhance the overall customer experience.

9. Data Science and Analytics for Smarter Decisions

Equipping the potential of big data is essential for modern finance. Advanced analytics and machine learning tools allow institutions to uncover hidden patterns, predict trends, and make data-driven decisions. These capabilities support a wide range of functions, from optimizing marketing campaigns to managing investment risk.

10. Predictive Modeling for Future-Proofing Finance

Predictive models use historical data to forecast future outcomes with a high degree of accuracy. Financial organizations utilize these insights to anticipate customer needs, detect fraud, assess creditworthiness, and perform other related tasks. By proactively addressing potential issues, institutions can improve operational resilience and maintain a competitive edge.

11. Cybersecurity Automation for Proactive Defense

Protecting sensitive financial data requires constant vigilance. AI-driven cybersecurity solutions monitor network activity in real-time, identifying threats before they can cause harm. Automated response systems then take appropriate action to neutralize risks, minimizing the impact of cyberattacks and ensuring business continuity.

12. Generative AI for Creative Problem Solving

The latest advancements in AI enable machines to generate original content, whether it be text, images, or code. Financial professionals can equip this technology to create innovative solutions, enhance communication, and streamline workflows. As generative AI continues to evolve, its applications in finance will expand, unlocking new opportunities for growth and efficiency.

Benefits of Using AI Tools in Finance

1. Efficient workflows with automation 

In any customer experience (CX) environment, efficiency is key. The less time agents spend on repetitive tasks, the more time they have to focus on complex cases that require human intervention. Automation can help reduce redundancies and streamline processes, resulting in faster resolution times and enhanced customer satisfaction.

For example, AI can analyze customer service tickets to identify common queries that are suitable for automation, such as email verification or password resets. CX leaders can then use this information to create new workflows that automate these tasks, freeing up agents to focus on more pressing issues. AI can also assist with quality assurance by flagging metrics such as negative customer sentiment, which can help organizations enhance their agent interactions. 

2. Consistent, high-quality customer support 

Providing excellent customer support is crucial for any business, but it can be especially challenging for financial services companies that serve a diverse customer base. AI can help ensure consistency in customer service by drawing on past interactions to gather context and predict customer needs. It can then utilize conversational AI to personalize responses naturally, catering to customers in multiple languages as needed. This not only improves the customer experience but also reduces the risk of errors and ensures that all customers receive high-quality support. 

3. Intelligent data processing and analysis 

Data is a valuable asset for any organization, but managing it can be overwhelming, especially when it comes to customer interactions. AI can analyze 100% of customer interactions to identify patterns and trends related to customer satisfaction, effort scores, and other metrics. It can then synthesize this information into actionable insights that internal teams can use to make informed decisions and improve operations. This eliminates the need for manual data analysis, allowing teams to focus on optimizing performance rather than sifting through vast amounts of information. 

4. Round-the-clock availability 

Customers expect to be able to reach support whenever they need it, regardless of the time of day. This is particularly important for financial services companies that handle sensitive and urgent matters, such as fraudulent transactions. AI agents can provide 24/7 support by responding to inquiries, providing relevant information, completing transactions, and escalating cases to human agents when necessary. This ensures that customers receive timely assistance and helps reduce the workload for human agents. 

5. High-strength data security 

Data security is a top priority for financial services companies, and AI can play a critical role in protecting customer information. AI uses encryption and machine learning algorithms to authenticate users and detect fraudulent activity. It can also assist customers who have lost their cards or forgotten their login information by providing empathetic support and guiding them through the necessary steps to resolve their issues. This not only enhances the customer experience but also helps build trust and confidence in the financial institution.

Related Reading

• How to Improve Finance Processes
• Financial Reporting Challenges
• Financial Forecasting Techniques
• Financial Data Extraction
• Financial Statement Analysis Techniques
• How to Forecast Budget
• Market Research Challenges
• How to Do a Cost Analysis
• AI Financial Modeling

20 Best AI Tools for Finance

1. Otio: Your AI Research Partner Facing content overload? 

Otio creates one AI-native workspace for researchers that helps collect data from diverse sources like bookmarks, tweets, books, and YouTube videos. It extracts key takeaways with AI-generated notes and source-grounded Q&A chat. Moreover, it facilitates the drafting of outputs and writing of research papers more efficiently. 

Key Features 

• AI-generated notes on all bookmarks 

• Chat with individual links or entire knowledge bases 

• AI-assisted writing 

• Web scraping capabilities 

2. Zest AI: Smarter Lending 

Zest AI enables lenders to make more innovative, more inclusive, and efficient decisions with the aid of AI. It assesses risks, optimizes lending processes, and improves loan approvals while managing risks. 

Key Features 

• Risk reduction with maintained approval rates

• Increased approvals without added risk 

• AI integration in lending for confident decisions 

3. Workiva: AI-Powered Financial Reporting 

Workiva is a cloud-based platform enriched with generative AI to draft narratives and automate controls for regulatory, SOX, ESG, audit, and financial reporting. 

Key Features 

• GenAI assistant for document context 

• Auto-generation of SOX testing and controls documentation 

• Automation of ESG disclosures 

4. Anaplan: Connected Planning 

Anaplan offers a Connected Planning platform that integrates planning across organizations. It enables scenario modeling and zero-based budgeting for better financial management. 

Key Features 

• Connected Planning 

• Scenario Modeling 

• Zero-Based Budgeting 

5. AlphaSense: AI for Market Intelligence 

AlphaSense scans financial documents, earnings reports, and market news using AI to identify trends and investment opportunities. 

Key Features 

• Sentiment analysis of financial reports 

• Accelerated investment decision-making 

• Market trend prediction 

6. MindBridge: Anomaly Detection 

MindBridge uses AI to analyze transactions, spot fraud, errors, and inefficiencies. It highlights high-risk journal entries and monitors ongoing financial activity for issues. 

Key Features 

• Real-time AI scoring 

• Anomaly explanation 

• Dashboard visualization 

7. Workday Adaptive Planning: AI-Powered EPM 

Workday Adaptive Planning enhances budgeting, planning, financial analysis, and modeling with the power of AI and machine learning. 

Key Features 

• Flexible Budgeting 

• Scenario Planning 

• Workforce Planning 

8. Spindle AI: Predictive Analytics 

Spindle AI utilizes machine learning to forecast sales trends, market behavior, and business performance, enabling data-driven financial decision-making. 

Key Features 

• Sales trend prediction 

• Market behavior analytics 

• Business performance forecasting 

9. Datarails: Excel-Based FP&A 

Datarails automates data consolidation, forecasting, budgeting, and reporting in Excel with AI-powered Q&A chat capabilities. 

Key Features 

• Centralized budget management, 

• Auto-refresh budgets and forecasts 

• What-if scenario analysis 

10. Expensify: Expense Management 

Expensify simplifies expense tracking with receipt scanning, automated invoice creation, and corporate card integration. 

Key Features 

• Spend Management 

• Receipt Tracking 

• Expensify Card 

11. Quantitative AI: Risk Management 

Quantitative AI streamlines risk management with AI-powered tools for governance, risk, and compliance (GRC). 

Key Features 

• AI-driven risk assessment 

• Regulatory compliance enhancement 

• Risk visibility improvement 

12. Cube: Collaborative FP&A 

Cube connects spreadsheets with ERPs for continuous planning, scenario modeling, and natural-language queries. 

Key Features 

• Rolling forecasts

• Natural language queries 

• Excel and Google Sheets integration 

13. SAP Concur: Expense Automation 

SAP Concur automates expenditure procedures, enabling expense submissions and travel expense tracking from any location. 

Key Features 

• Remote expense submission 

• Automated 

• AP processes 

• Travel expense capture 

14. Zapliance: Cash Recovery 

Zapliance automates cash recovery and accounts receivable processes to reduce payment delays and improve financial efficiency. 

Key Features 

• AI-powered automation 

• Payment delay reduction 

• Financial process optimization 

15. Ramp: Expense Automation 

Ramp offers an AI-driven expense, bill-pay, and corporate card solution for spend capture, policy enforcement, and reconciliation. 

Key Features 

• Auto-capture receipts 

• Policy enforcement 

• Real-time spend intelligence 

16. Kensho: Data Management 

Kensho provides AI tools for audio transcription, entity identification, document analysis, and data extraction. 

Key Features 

• Speech to Text 

• Entity Identification 

• Data Mapping 

17. Tipalti: Invoice Automation 

Tipalti automates invoice processing, supplier payments, and financial reconciliation for improved accuracy and compliance. 

Key Features 

• Global payment automation 

• Invoice approval automation 

• Compliance assurance 

18. Brex: Corporate Card 

Brex offers a modern corporate card platform with AI tools for spending insights, budget compliance, and virtual card management. 

Key Features 

• Virtual card issuance 

• AI anomaly detection 

• Budget enforcement 

19. Ayasdi: AI Platform 

Ayasdi combines machine learning, statistical algorithms, and topological data analysis to solve complex data problems. 

Key Features 

• Advanced AI tools 

• Custom application frameworks 

• Pattern discovery 

20. Botkeeper: AI Bookkeeping 

Botkeeper automates transaction categorization, expense management, and financial statement generation for accurate bookkeeping. 

Key Features 

• AI-driven automation 

• Financial record accuracy

• Time-saving

7 Tips to Use AI in Finance

1. Use Otio

Knowledge workers, researchers, and students today face content overload and are left to manage it using fragmented, complex, and manual tools. Too many of them settle for stitching together complicated bookmarking, read-it-later, and note-taking apps to get through their workflows. Now that anyone can create content with the click of a button, this problem is only likely to worsen. Otio solves this problem by providing one AI-native workspace for researchers. It helps them: 

1. Collect: a wide range of data sources, from bookmarks, tweets, and extensive books to YouTube videos. 

2. Extract key takeaways: with detailed AI-generated notes and source-grounded Q&A chat. 

3. Create: draft outputs using the sources you’ve collected. 

Otio helps you transition from reading list to first draft more quickly. Additionally, Otio enables you to write research papers and essays more efficiently. Here are our top features that researchers love: 

• AI-generated notes on all bookmarks (YouTube videos, PDFs, articles, etc.), 

• Otio enables you to chat with individual links or entire knowledge bases, just like you chat with ChatGPT, as well as AI-assisted writing. 

• Our tool features web scraping capabilities, enabling you to access a broad range of data sources beyond traditional academic papers and search engines. 

This feature enables researchers to collect diverse information from sources like bookmarks, tweets, books, and YouTube videos, streamlining the process of curating and analyzing data for research purposes. Let Otio be your AI research and writing partner — try Otio for free today!

2. Take Small First Steps, Then Scale Up. 

Jumping straight into complex AI implementations can feel overwhelming. Instead, begin your AI journey with focused, manageable projects. Take expense report automation, for example. It’s a straightforward process that can deliver immediate value while helping you build confidence with AI tools. These small wins do more than just improve efficiency — they help build trust in AI solutions across your organization. As you grow more comfortable with AI-driven processes, you can gradually tackle more complex applications. 

3. Strengthen Your AI Prompting Skills 

Working with AI requires a unique communication approach. Think of AI models as brilliant but inexperienced interns — they’re competent but need clear, structured guidance to deliver their best work. When crafting prompts for your AI tools, provide specific and detailed instructions to ensure optimal results. Learn how to use techniques like Chain-of-Thought (CoT) prompting.

The CoT approach breaks down large requests into manageable steps, guiding AI tools like ChatGPT through a step-by-step process. CoT prompting works particularly well for complex analytical tasks, such as comparative or trend analysis. And always review the outputs carefully. The quality of your AI-generated insights directly depends on how well you communicate your needs to the system. 

4. Balance AI with Human Oversight: The Role of Human Oversight in AI-Driven Decisions Source

CFI’s Introduction to AI in Finance course. While AI brings powerful capabilities to financial operations, it works best as a complement to human expertise rather than a replacement for it. Keep humans in the loop for critical decisions and ensure you can clearly and effectively explain every step of your AI-driven processes. This balance is crucial. You should maintain final decision-making authority while leveraging AI to enhance analysis and streamline routine tasks. Understanding and being able to explain how your AI solutions work builds confidence and maintains accountability throughout your organization. 

5. Build Fair and Unbiased AI Systems 

AI systems can inadvertently perpetuate biases present in their training data. Regular testing of your AI models for potential biases is crucial, particularly in finance, where decisions can have a profound impact on people’s lives. Use diverse datasets when training your models, and continuously monitor outputs for signs of bias. This vigilance is crucial in areas such as lending and hiring, where fairness and inclusivity are of paramount importance. 

6. Embracing Transparency with Explainable AI 

In finance, the ability to explain how decisions are made is a best practice and often a regulatory requirement. Invest in AI tools that provide clear insights into their decision-making processes. Explainable AI helps build trust with stakeholders and supports compliance efforts. This transparency becomes particularly valuable in high-stakes scenarios, such as credit scoring, where understanding the factors behind decisions is crucial. 

7. Stay on Top of AI Trends 

As AI technology rapidly advances, stay up-to-date on the latest developments in language models and automation. Make it a priority to stay informed about these advancements. Following AI trends enables you to identify new opportunities to enhance your financial operations and maintain a competitive edge. Subscribe to reputable AI news sources, attend industry conferences, and actively engage with AI communities to stay current with your knowledge. 

Related Reading

• Financial Analysis Tools
• How to Improve Forecast Accuracy
• How to Write an Analysis Report
• Automated Reporting Tools
• Finance Automation Tools
• Finance Workflow
• Budget Variance Analysis
• AI to Analyze Calls
• Robotic Accounting Automation
• Private Equity Analysis Tools

Supercharge Your Researching Ability With Otio — Try Otio for Free Today

Knowledge workers, researchers, and students today suffer from content overload and are left to deal with it using fragmented, complex, and manual tooling. Too many of them settle for stitching together complicated bookmarking, read-it-later, and note-taking apps to get through their workflows. Now that anyone can create content with the click of a button, this problem is only likely to worsen. Otio solves this problem by providing one AI-native workspace for researchers. It helps them: 

1. Collect: a wide range of data sources, from bookmarks, tweets, and extensive books to YouTube videos. 

2. Extract key takeaways: with detailed AI-generated notes and source-grounded Q&A chat. 

3. Create: draft outputs using the sources you’ve collected. 

Otio helps you transition from reading list to first draft more quickly. Additionally, Otio enables you to write research papers and essays more efficiently. Here are our top features that researchers love: 

• AI-generated notes on all bookmarks (YouTube videos, PDFs, articles, etc.), 

• Otio enables you to chat with individual links or entire knowledge bases, just like you chat with ChatGPT, as well as AI-assisted writing. 

• Our tool features web scraping capabilities, enabling you to access a broad range of data sources beyond traditional academic papers and search engines. 

This feature enables researchers to collect diverse information from sources like bookmarks, tweets, books, and YouTube videos, streamlining the process of curating and analyzing data for research purposes. Let Otio be your AI research and writing partner — try Otio for free today!

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