Postponed is not canceled: AI in Finance & Accounting
So it's no wonder that more and more managers and finance professionals are turning to AI to leave companies with traditional accounting processes behind.
However, very few companies today are in a position to implement AI themselves and develop and train highly complex learning models. Help comes from the software industry. By integrating AI into their platforms, innovative providers of financial solutions mean that the user has nothing to do with development and maintenance. They can simply take advantage of the benefits provided by an AI-supported finance and accounting platform.
AI and the four-pillar success model in finance and accounting (F&A)
The possibilities of AI in F&A are diverse and also fluid across the various disciplines. In order to be able to assess the added value when introducing AI in finance, an AI initiative can be divided into four success factors:
- TransformTransforming data and information into insights to drive business growth
- AutomateAutomate repetitive tasks to have more time for the important things
- Strengthen: Strengthening risk management and compliance
- Collaborate: Better cooperation in the finance team
With this clustering, companies can ensure that both operational and strategic goals are achieved in their finance department.
From theory to practice with AI
AI is a topic that is omnipresent. However, when it comes to concrete implementation, many find it difficult. It is important that the effects of AI are assigned to specific processes and optimization potential. The advantages of AI can be clearly illustrated using the example of two important areas in F&A.
Traditional intercompany processes are complex and labor-intensive. From initial invoicing to final payment, the likelihood of errors is high and increases with the growing volume of intercompany transactions. Experts know the problems: costs appearing in the wrong place, overdue and unpaid balances and lengthy payment disputes. Correcting these errors costs time and money and has a negative impact on productivity.
An AI-supported financial platform, such as BlackLine, is able to analyze a company's transaction data. It predicts where problems might occur, for example, and where there could be a risk to the financial close processes and data accuracy - even before the transactions are actually posted. It identifies high-risk transactions, explains them and shows the accounting teams where immediate corrections can be made. The highlight: as the volume increases, the solution learns and applies the findings to the processes. The benefit: thanks to an error reduction of up to 97 percent, companies can manage their processes faster and more precisely and concentrate on the essentials.
Another area is manual postings. For most companies, manual entries represent a significant risk. Financial platforms with built-in AI solve these challenges by integrating with ERP systems and other financial close management solutions to centralize and automate the entire journal entry process - including journal creation, validation, review and preparation for posting. Tasks are completed in a fraction of the time with AI by centralizing the process from preparation to posting with workflow automation and standardization.
These examples show that AI can already be used as a valuable assistant in finance today and generates many positive effects because it relieves finance teams of time-consuming, risky manual work. The decisive factor here is the cross-platform AI capabilities that provide the finance team, controlling and management with an overall picture of the financial situation in order to create valid financial statements and make the right management decisions on a solid basis to position the company for the future.
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