7 fascinating use cases of AI in finance

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If there’s one technology paying dividends for the financial sector, it’s artificial intelligence. AI has given the world of banking and finance new ways to meet the customer demands of smarter, safer and more convenient days sales of inventory dsi ways to access, spend, save and invest money. Employees who perceive AI as a co-worker that helps them with their work feel more engaged and aren’t threatened by a technology some perceive as an adversary.

  • Automatically generated based on your actual spending, 22seven’s personalized budget gives you a clear picture of your monthly expenditure, helping you manage your finances more effectively.
  • AccountsIQ enables seamless connectivity with applications like Autoentry, Lightyear, Salesforce, and various electronic banking systems.
  • With its ability to process vast amounts of data and quickly produce novel content, generative AI holds a promise for progressive disruptions we cannot yet anticipate.
  • For example, with Yokoy, detecting duplicate payments is fully automated and is a matter of seconds, no human input being required.
  • One way it uses AI is through a compliance hub that uses C3 AI to help capital markets firms fight financial crime.

Companies that take their time incorporating AI also run the risk of becoming less attractive to the next generation of finance professionals. 83% of millennials and 79% of Generation Z respondents said they would trust a robot over their organization’s finance team. Millennial employees are nearly four times more likely than Baby Boomers to want to work for a company using AI to manage finance. Assess existing talent, identify skill gaps, provide training opportunities, and recruit individuals who are equipped to handle future use cases as they emerge. Ensure that finance personnel understand how generative AI can complement their work and unlock their potential by automating routine tasks, accelerating business insights, and improving operational efficiency.

Applications of AI in Financial Services

Notable features include eliminating spreadsheets, consolidating redundant planning systems, reducing costs and risks, improving decision accuracy and outcomes through predictive analytics, and “what-if” scenario analysis. Users can consolidate their bank and credit card accounts within the app, offering a comprehensive view of their financial landscape. This holistic financial perspective, combined with Snoop’s capability to monitor bill payments, ensures users are not overpaying, and highlights potential saving opportunities through special offers and exclusive deals. For accounting teams, the platform enhances accuracy by automating lease and revenue workflows.

The experience of finance suggests that AI will transform some industries (sometimes very quickly) and that it will especially benefit larger players. In short, it means that companies will likely invest heavily in unlocking and understanding the data they have and seek to acquire more to make smart business decisions. However, it’s not just the quantity of data that matters, it’s the quality of the analysis that counts. Investments in consumer behavioral analysis are set to rise, and there is a renewed focus on gaining a deeper understanding of the current market.

  • This reduces the need for manual data entry and eliminates human errors, making the invoice processing workflow more time- and cost-efficient.
  • Amid a bright future, the impact of generative AI in finance may transform how leaders analyze data, manage risk, and optimize their operations.
  • When an invoice is uploaded into the tool, the AI model analyzes line items submitted by that particular supplier, and looks for associations between keywords and selected line items.
  • This places finance behind other administrative functions (i.e., HR, legal, real estate, IT and procurement).
  • Prebuilt AI solutions enable you to streamline your implementation with a ready-to-go solution for more common business problems.
  • OCR is a technology that is designed to recognize and convert text from scanned documents or images into machine-readable text.

Looking ahead, the integration of generative AI will transform core processes, reinvent business partnering, and mitigate risks. Generative AI will eventually collaborate with traditional AI forecasting tools to create reports, explain variances, and provide recommendations, thereby elevating the finance function’s ability to generate forward-looking insights. The enhancements will empower finance professionals to make more informed strategic decisions, leading to improved operational efficiency and effectiveness. One way it uses AI is through a compliance hub that uses C3 AI to help capital markets firms fight financial crime. Announced in 2021, the machine learning-based platform aggregates and analyzes client data across disparate systems to enhance AML and KYC processes.

What are the risks of not implementing AI in finance?

This automation allows for real-time report production, cost reduction, and minimization of compliance risk. Finally, companies are deploying AI-guided digital assistants that make it easier to find information and get work done, no matter where you are. For example, finance organizations can leverage digital assistants to notify teams when expenses are out of compliance or to automatically submit expense reports for faster reimbursement. Today’s digital assistants are context-aware, conversational, and available on almost any device. For a preview, look to the finance industry which has been incorporating data and algorithms for a long time, and which is always a canary in the coal mine for new technology.


Artificial intelligence (AI) adoption by financial institutions (FIs) will be boosted by technology improvement, improved user acceptability, and altering regulatory frameworks. Banks using AI can greatly improve the client experience by providing 24/7 access to accounts and financial advisory services. According to Forbes, “70% of all financial services organisations are already utilising machine learning to forecast cash flow occurrences, fine-tune credit ratings, and detect fraud.” It promises to provide unrivaled forecasting accuracy, real-time collaboration, and an effortless user experience. Furthermore, Planful offers role-based security and controls to manage complex processes while ensuring the scalability to accommodate growth. Some of the key features offered by Datarails include data consolidation from multiple sources, automated financial reporting & monthly close, budgeting, forecasting, scenario modeling, and in-depth analysis.

Additionally, the platform analyzes the identity of existing customers through biometric authentication and monitoring transactions. Here are a few examples of companies providing AI-based cybersecurity solutions for major financial institutions. Socure created ID+ Platform, an identity verification system that uses machine learning and AI to analyze an applicant’s online, offline and social data, which helps clients meet strict KYC conditions.

The 14 Best AI Tools for Finance

USD offers an innovative, online AI master’s degree program, the Master of Science in Applied Artificial Intelligence, which is designed to prepare graduates for success in this important fast-growing field. This program includes a significant emphasis on real-world applications, ethics, privacy, moral responsibility and social good in designing AI-enabled systems. In fact, 78% of millennials say they won’t go to a bank if there’s an alternative. AI, machine learning and trained algorithms can be used to accurately estimate the creditworthiness of clients by analyzing credit history and income growth of a client, but also considering market conditions.

At the level of the individual analyst, the value proposition includes fewer repetitive tasks and keyboard strokes and more time for business collaboration. The pioneering approach optimizes intricate financial strategies and decision-making processes, enhancing efficiency, accuracy, and adaptability in the dynamic world of finance. As the “tip of the spear” in generative AI, finance can build the strategy that fully considers all the opportunities, risks, and tradeoffs from adopting generative AI for finance. Every day, huge quantities of digital transactions take place as users move money, pay bills, deposit checks and trade stocks online. The need to ramp up cybersecurity and fraud detection efforts is now a necessity for any bank or financial institution, and AI plays a key role in improving the security of online finance. Zest AI is an AI-powered underwriting platform that helps companies assess borrowers with little to no credit information or history.

This positions artificial intelligence as more of a co-worker than other technologies. But despite AI’s capabilities, finance has unique responsibilities — such as validating the integrity of financial statements — that can’t be delegated to an algorithm. Successful finance teams design processes so that people and machines are each tasked with the actions they perform best. These organizations recognize that AI performs some narrowly defined tasks better than people, but it cannot do everything better. In many cases, tasks that people perceive as simple are nearly impossible for a machine to replicate. If you’d like to see how our AI-powered spend management platform can help you automate processes and save time and costs, while gaining end-to-end visibility and control over your business spending, you can book a demo below.

Lowering the number of false positives and human errors

Finally, another general area where artificial intelligence can be used is data analysis and forecasting. Instead of relying on outdated methods, finance teams can use AI and machine learning algorithms to analyze historical data and make predictions about future trends with much more ease. In fact, a recent study found that AI algorithms outperformed traditional rule-based systems by up to 20% in detecting fraudulent credit card transactions. Additionally, AI-based fraud detection can process vast amounts of data in real-time, enabling financial institutions to detect suspicious activities with speed and accuracy.

According to a Gartner study, 80% of CFOs surveyed in 2022 expected to spend more on AI in the coming two years.2 With that investment, however, around two-thirds think their function will reach an autonomous state within six years. 22seven is a finance tracking and budgeting app designed to simplify your financial life. It serves as a one-stop solution to help you keep track of your money by aggregating all your accounts and transactions in one place, linking to over 120 financial institutions. Nanonets also provides a system for validating the data extracted from documents, which ensures the accuracy of data and enables the AI to continually improve its performance with increased usage. As a learning AI, Nanonets continuously improves its accuracy with each document processed. This tool stands out with its ability to handle uncategorized transactions and coding errors, providing increased efficiency and reducing stress.

Yet, despite these changes, many finance tools remain stuck in the past, with a poor user experience and interface. Finance AI technology can be used to automate approval flows for both expenses and invoices, based on pre-set rules, such as suppliers, categories, or spending limits. This ensures that payments and reimbursements are approved quickly and efficiently. NLP  or natural language processing is the branch of AI that gives computers the ability to understand text and spoken words in much the same way human beings can. When processing invoices, artificial intelligence can be used for different purposes, some of them similar to those described in the section above.

With millennials and Gen Zers quickly becoming banks’ largest addressable consumer group in the US, FIs are being pushed to increase their IT and AI budgets to meet higher digital standards. These younger consumers prefer digital banking channels, with a massive 78% of millennials never going to a branch if they can help it. Between growing consumer demand for digital offerings, and the threat of tech-savvy startups, FIs are rapidly adopting digital services—by 2021, global banks’ IT budgets will surge to $297 billion. This advanced machine learning technology offers quick and low-cost content creation. However, it’s crucial to acknowledge hurdles such as security, reliability, safeguarding intellectual property, and understanding outcomes. Armed with appropriate strategies, generative AI can elevate your institution’s reputation for finance and AI.