The Role of Embedded AI in Finance: Improving Efficiency and Decision-Making
CFOs and finance teams have been operating in an increasingly complex environment in recent years. Factors such as supply chain uncertainty, recessionary fears, inflation, and the disruptive impact of new technologies have posed challenges for financial decision-making. However, the digitization of finance has helped these teams adapt and overcome these challenges by improving operational efficiencies and decision-making accuracy.
One of the most promising technologies for finance leaders is artificial intelligence (AI). AI has the potential to revolutionize financial processes by unlocking the power of data and providing predictive and analytical insights. The International Monetary Fund highlights various AI applications in finance, including machine learning algorithms to improve forecasting, natural language processing for chatbots and customer service, and anomaly detection for limiting insider trading and enhancing fraud detection.
According to McKinsey, the potential annual value of AI and analytics within the global banking sector could reach $1 trillion. Marketing and sales, risk management, and human resources are identified as the highest-value business functions for AI support. Finance leaders worldwide have also shown a strong interest in AI, recognizing its potential to enhance a broad range of finance processes.
While consumer-facing AI applications, such as chatbots, have garnered public interest, the true value of AI for businesses lies in embedded AI. Embedded AI involves purpose-built algorithms that run within standard business processes, such as invoicing and reporting. For finance teams, embedded AI offers enhanced analytical capabilities, process automation, and improved data processing.
For example, optical character recognition (OCR) technology can streamline invoice-to-cash processes by extracting key information from payments notes and matching them with open receivables in systems like SAP S/4HANA. This frees up finance professionals to focus on more strategic tasks. Accenture has also leveraged AI in reconciliation processes, achieving an automatic clearing hit rate of 54%. These efficiency gains allowed the company to redirect resources to higher-value activities.
Embedded AI can also play a vital role in enhancing business risk and compliance management. Given the heavy regulation of the finance industry, CFOs must prioritize statutory compliance. By applying AI and behavioral analytics to transactional data, finance teams can identify errors or fraudulent transactions that may expose the business to financial liability. AI-powered invoice payment forecasting can help predict payment timings from at-risk customers, leading to more optimized payment strategies and improved cash flow management.
Another area where embedded AI can make a significant impact is reporting accuracy. Manual errors can be avoided by automating the posting of new journal entries in record-to-report processes, leading to improved reporting accuracy and compliance.
In an uncertain global economic environment and a complex regulatory landscape, CFOs and their finance teams need to maintain high levels of efficiency and accuracy in their financial activities. Embedded AI, integrated into core business applications, can help achieve these goals by reducing inaccuracies, improving decision-making, and successfully navigating challenging conditions.
In conclusion, embedded AI is a powerful tool that can revolutionize financial processes. By leveraging AI technology within their operations, CFOs and finance teams can enhance efficiency, improve decision-making, and effectively steer their organizations through the complex and ever-changing business landscape.