Corporate Finance in the Age of AI

Artificial intelligence (AI) is revolutionizing corporate finance, with real-time analytics and machine learning (ML) that enhance financial decision-making. AI helps finance teams improve key business processes of forecasting for budgeting and planning while AI- and ML-powered real-time analytics improve insight accuracy, expedite decision-making, and free finance teams from manual tasks so they can focus on generating business insights to fuel growth. For the consumer, AI advances enhance service efficiency, personalization, and security, creating smarter, safer, and more tailored financial services. Experts predict that AI’s ability to increase productivity could add between $200 billion and $340 billion (or 2.8-4.7 percent of total industry revenue) to the industry each year.

 

Analytics solution tools integrate with AI/ML models

Beyond their ability to improve operations, AI and adaptive ML bridge the gap for the transition from legacy systems to real-time reporting, improving agility and connectedness. This transition creates a smoother customer experience by proactively helping companies anticipate and respond to end-user needs.

Traditional analytics systems like SAP are being integrated with advanced financial and reporting tools such as Anaplan, Allocadia, and Tableau. These integrations enable finance departments to leverage in-house data assets, reducing latency and ensuring high-quality data. Live dashboarding tools offer real-time and customized insights, which improve business agility and decision-making. The process and performance requirements of high data quality and low data latency required for real-time analytics increase resource demands on IT systems. As such, it’s crucial for decision-makers to consider migration risks and change management when evaluating the labor savings of real-time analytics solutions.

Real-time analytics insights benefit operational teams by reducing response times. This allows them to manage issues before they escalate, directly benefiting the customer experience. Additional applications of AI include inventory management, marketing, fraud detection, and cybersecurity. Ultimately, these tools enable finance professionals to better manage risk, reduce error costs, respond to volatility, and improve the customer experience.

 

Customer and client benefits

AI and ML technologies substantially benefit financial consumers by enhancing service efficiency, personalization, and security. With these technologies, financial institutions can analyze consumer data in real time, providing personalized financial advice, tailored product recommendations, and proactive fraud detection. Additionally, AI-driven chatbots and virtual assistants improve customer service by offering instant support and accurate information around the clock.

Furthermore, AI algorithms can identify spending patterns and provide insights that help consumers make more informed financial decisions, manage budgets effectively, and achieve their financial goals. Enhanced security measures powered by AI ensure the protection of personal information, reducing the risk of fraud and cyberattacks. Overall, AI and ML empower financial consumers with smarter, safer, and more personalized financial services.

 

Challenges and ethical considerations

Despite AI’s many benefits, it’s imperative for finance professionals to be cautious of overreliance. By maintaining human oversight, organizations can mitigate risks from hasty decision-making and automated responses. The benefits can far outweigh the costs, however, because forecasting, decision response, and organizational connectedness remain key drivers for organizational returns. In 2024, one industry report found that companies achieving the most improvement in decision excellence outperformed peers by $4.3 billion in market cap for total shareholder returns (TSRs), and companies that improved decision-making the least underperformed against their peers by $5.7 billion in market cap for TSRs.

Real-time analytics stacks raise ethical considerations through increased IT resource demands and middle management automation or outsourcing. Data governance and AI protocols can perform labour-intensive data handling to free talent for insight generation and expertise. This labour savings is an automation of existing roles. Automating using real-time analytics stacks can result in smaller analytics teams, middle management layoffs, changes in data governance oversight, and organizational restructuring. There are unresolved legal and data ownership issues surrounding AI/ML along with the potential for increased exposure to corporate espionage and cyberattacks. That said, the benefits and opportunities in customer experience, error reduction, and increased responsiveness in budgeting and forecasting make it worth considering.

 

A new frontier of finance

AI/ML models and real-time analytics represent a new frontier in finance, offering unparalleled scalability and response times. To maximize the benefits of the technologies and establish a competitive advantage, it is essential for financial leaders to balance the demands of AI implementation with industry expertise. As finance professionals navigate an increasingly complex business landscape, governance reporting, privacy considerations, and data asset management requirements will be essential factors to consider. To harness its full potential, it is critical for financial leaders and consumers to stay informed and proactive about AI’s growing applications and capabilities.