As AI capabilities automate tasks, generate insights, and create content for finance teams, the future of finance looks more analysis-driven and strategic.

As AI evolves, so will its applications in finance. GenAI capabilities will increasingly be embedded into existing software systems used to manage financial processes, so teams can access such capabilities right in their existing workflows for accounts payable and receivable, budgeting and budget reconciliations, financial close, and more. Right now, there are several areas where AI is already being used to improve decision-making, efficiency, and the bottom line, including the following:

1. Financial forecasting and planning

AI is transforming the financial forecasting and planning process through predictive analytics. Predictive analytics is a type of data analytics used in businesses to identify trends, correlations, and causation. It uses data, statistical algorithms, and machine learning to forecast future outcomes based on the analysis of historical data and existing trends.

Using predictive analytics, finance teams can forecast future cash flows using historical company data, as well as data from the broader industry. While traditional financial forecasts must be manually adjusted when circumstances change, AI-driven forecasts can recalibrate based on new data, helping keep forecasts and plans relevant and accurate. GenAI can even automatically create contextual commentary to explain forecasts produced by predictive models and highlight key factors driving the prediction.

2. Regulatory compliance

With the increasing complexity of regulatory compliance around the globe, the cost and resource burden of regulatory reporting has soared in recent years. Organizations devote significant time and resources to meeting those requirements. AI can take on a portion of the workload by automating compliance monitoring, audit trail management, and regulatory report creation.

A particularly valuable technology in regulatory compliance is natural language processing (NLP). NLP is a branch of AI that lets computers comprehend and generate human language. NLP is capable of quickly parsing through large amounts of textual data, transforming raw text or speech into meaningful insights. It can analyze lengthy documents, contracts, policies, and other text sources to extract critical information, pertinent changes, and potential compliance risks. NLP can even facilitate document management, automatically classifying documents based on predetermined criteria.

3. Cash flow optimization

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 build a consolidated view of their cash and liquidity. And even then, forecasts can include errors and be quickly rendered obsolete.

Using predictive analytics and machine learning, companies can automatically compile data from all relevant sources—historical and current—to continuously predict future cash flows. With faster, more accurate cash flow forecasting, companies can make proactive moves to maintain healthy liquidity levels. For instance, if there is excess cash, they can take advantage of early payment discounts with suppliers or identify areas to reinvest in the business. When cash is tight, they can reassess loan positions or trigger foreign exchange transfers between subsidiaries. Finance teams also might use AI to optimize working capital by applying the right early payment incentives to select suppliers based on market conditions, payment history, and other factors.

4. Expense management

Expense management can quickly turn into a source of frustration. 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 spending is compliant. 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.

The role of AI in expense management doesn’t end there. 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.

5. Task automation

Perhaps one of the most common, and arguably one of the most impactful, capabilities of AI is task automation. AI can help automate numerous manual, time-consuming finance processes that used to inundate the finance team, including the following:

  • Data entry: Extracts and inputs relevant information from statements, invoices, receipts, and other financial documents into a company’s system.

Invoice capture and processing: Scans, reads, and digitizes paper invoices.

  • Matching and verification: Automatically compares and matches invoices against corresponding purchase orders and delivery receipts.
  • Payment execution: Facilitates the payment process, including scheduling payments to optimize cash flow, selecting the preferred payment method, and executing the payment automatically or with minimal human intervention.
  • Reconciliation: Carries out an automated reconciliation process that matches payment transactions with bank statements to verify that amounts and beneficiaries align.
  • Account code combination defaulting: Predicts and automatically defaults segment values of the account code combination on no-PO accounts payable invoice lines.
  • Financial close processes: Intelligent process automation (IPA) automates the complex orchestration of the financial close and monitors task status across multiple systems, recommending new rules that guide ongoing automation, automatically kicking off close processes as soon as dependent tasks are completed and updating the close calendar.

Advanced automation of high volume, repetitive, and mundane manual tasks presents numerous benefits, including time and cost savings, decreased errors, and higher employee satisfaction as finance staff get to focus on more strategic, value-added tasks.

6. Financial reporting and analysis

AI can help automate and enhance multiple aspects of the financial reporting and analysis process. 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.

AI can then use the data to help generate financial statements, such as income statements, balance sheets, and cash flow statements, transforming the data into reports that highlight key performance indicators (KPIs), trends, and observations. It can also help with regulatory reporting. GenAI can fill out the needed forms with data provided by the finance team for the staff to review and confirm.

GenAI can be used to produce narrative reports, providing context into the numbers by combining financial statements and data with an explanation of each. GenAI can even help prepare first drafts of 10-Qs and 10-Ks, including footnotes and management discussion and analysis (MD&A).

Enchant Apps has a team of Oracle Cloud experts across all modules. If you are interested in learning more or have a project where you need Oracle cloud expertise, feel free to contact us!

How AI Is Transforming Finance

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