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In the initial stages, it can extract relevant financial information from various data sources. It can then clean and process financial data by identifying errors, inconsistencies, or missing values and notifying finance staff of the areas needing attention. Effective cash flow management always ranks high on the priority list of CFOs and their teams, and AI is proving to be a valuable tool in cash flow optimization. Due to the large amounts of data required, most finance professionals need more than a day to how to calculate the provision for income taxes on an income statement build a consolidated view of their cash and liquidity. And even then, forecasts can include errors and be quickly rendered obsolete.

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The company has more than a dozen offices around the globe serving customers in industries like banking, insurance and higher education. The use of AI in finance creates potential risks for institutions, including biased or flawed AI model results, data breaches, cyber-attacks and fraud, which can cause financial losses and reputational damages eroding consumer trust. A major reason that AI is taking off now, and is accessible to such a broad base of companies, is because of today’s cloud-based AI platforms. Those two factors make it very hard to “buy AI” and run it in an organization’s own data center. Cloud computing platforms provide scalable infrastructure and resources for deploying and running AI applications, so companies pay for capabilities they need and enjoy updates without the need for patching and software updates. For companies that use cloud-based ERP systems, the incentive to use AI technology from the same cloud is substantial.

Giving finance staff increased understanding of AI will also be critical in ensuring the proper security, controls, and appropriate use of the technology. Companies can also use AI to automate approval workflows, flagging only the expenses that need the finance team’s review based on predetermined rules, promoting a “manage-by-exception” culture. AI-enabled expense assistants are also becoming more common, helping employees by automatically categorizing expenses, populating and filing the required documentation for each, and providing guidance around a company’s compliance policy. For employees, meeting expense policy rules by manually collecting receipts, filling out forms, and submitting expense reports is arduous and error prone. And finance teams can’t manually review every expense to ensure that all spend is compliant.

An organization, for instance, could use a centralized approach for risk, technology architecture, and partnership choices, while going with a more federated design for strategic decision making and execution. It’s the schools, the churches, the sports teams, and definitely the businesses. I’ve got a total soft spot for small businesses, particularly those started and owned by women and nonbinary people, where the founder is everything to the business—CEO, general counsel, CMO, CFO. There is so much to be done, and marketing tends to be one of the places that really can make or break that business.

How Is AI Transforming Finance? AI in Finance Use Cases

The widespread use of AI could introduce new sources and channels of systemic risk transmission (e.g. interconnectedness, herding behaviour, procyclicality, third party dependency). Financial institutions’ reliance on cloud services and third-party providers creates concentration risks, where a failure could impact financial stability. As the use of AI models and data grows, certain third-party providers may become critical, adding further risk. AI can help solve those problems by giving finance teams better insight into possible investment and cost saving opportunities, automating transactional work, generating needed data automatically, and enhancing data visualization.

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1. The use of AI, including Machine Learning (ML) and Generative AI (GenAI), is growing rapidly in finance, offering opportunities to boost efficiency and create value.
2. Financial firms are finding tremendous value in automation, and in particular robotic process automation.
3. NLP can even facilitate document management, automatically classifying documents based on predetermined criteria.
4. AI is being used in finance to automate manual tasks, such as inputting invoices, tracking receivables, and logging payment transactions so employees are free to focus on value-added strategic work.
5. Elevate your teams’ skills and reinvent how your business works with artificial intelligence.

AI is also being adopted in asset management and securities, including portfolio management, trading, and risk analysis. The dynamic landscape of gen AI in banking demands a strategic approach to operating models. Banks and other financial institutions should balance speed and innovation with risk, adapting their structures to harness the technology’s full potential. As financial-services companies navigate this journey, the strategies outlined in this article can serve as a guide to aligning their gen AI initiatives with strategic goals for maximum impact. Scaling isn’t easy, and institutions should make a push to bring gen AI solutions to market with the appropriate operating model before they can reap the nascent technology’s full benefits.

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Without central oversight, pilot use cases can get stuck in silos and scaling becomes much more difficult. Looking at the financial-services industry specifically, we have observed that financial institutions using a centrally led gen AI operating model are reaping the biggest rewards. As the technology matures, the pendulum will likely swing toward a more federated approach, but so far, centralization has brought the best results. For example, many previously manual and document-based processes at banks required handling and processing of customer identity documents. With software automation systems, customers can securely upload identity documents to a web-based location.

AI is a powerful way to accelerate expense management and remove some of its complexity. For instance, optical character recognition (OCR)—a form of AI that can scan handwritten, printed, or images of text, extract the relevant information, and digitize it—can help with receipt processing and expense entry. OCR will scan uploaded receipts and invoices to automatically populate expense report fields, such as merchant name, date, and total amount. A financial institution can draw insights from the details explored in this article, decide how much to centralize the various components of its gen AI operating model, and tailor its approach to its own structure and culture.

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