AI vs. Traditional Financial Close: Why Manual Financial Close is Costing You More Than You Think

The financial close process is a critical component of any organization's financial operations, involving the preparation and finalization of financial statements. Traditionally, this process has been manual, time-consuming, and prone to errors. However, with the advent of Artificial Intelligence (AI), there has been a significant shift towards automating and streamlining financial close processes.

As of 2024, AI is increasingly being adopted across various financial functions, including financial close and consolidation, to enhance efficiency and accuracy. For instance, AI-powered tools can detect anomalies in financial data more efficiently than traditional methods, which often rely on manual checks and balances.

Last year, nearly 78% of financial institutions implemented Generative AI for at least one use case, with a significant portion focusing on risk and compliance enhancements. This trend highlights the growing reliance on AI to improve financial operations. Despite these advancements, many organizations still rely on manual processes for financial close, which can lead to increased costs, reduced efficiency, and heightened risk of errors.

The Cost of Manual Financial Close

A survey conducted by Gartner highlighted ambitious goals for finance executives, with 55% aiming to achieve a touchless financial close by 2025, and 86% intending to implement a faster, real-time close within the same timeframe. However, traditional financial close processes remain labor intensive and resource-heavy, involving tasks such as data entry, reconciliation, and compliance checks. These manual processes are not only time-consuming but are also prone to human error.

In contrast, the integration of automation and AI in financial close can significantly reduce closing times by several days, thereby freeing up resources for strategic initiatives. This reduction in time not only saves labor costs but also enables finance teams to focus on higher-value tasks, such as financial analysis and strategic planning. By transitioning towards more automated and real-time financial close processes, organizations can enhance efficiency, accuracy, and compliance, aligning with the broader industry trend towards streamlined financial operations.

In addition to time savings, manual processes can lead to significant financial losses due to errors. AI systems, on the other hand, use machine learning algorithms to detect and flag potential errors, minimizing financial risks. For example, AI can automatically match transactions and identify discrepancies, ensuring that financial statements are accurate and reliable. By reducing errors and enhancing accuracy, organizations can avoid costly corrections and audits that often follow manual financial close processes.

Enhancing Efficiency with AI

In 2024, the banking sector witnessed a notable shift toward the adoption of generative AI, with only 8% of banks developing it systematically, while a significant 78% adopted a tactical approach. This trend highlights the growing interest in leveraging generative AI to enhance financial operations. AI, in general, significantly enhances the efficiency of financial processes, including the financial close, by automating routine tasks. This automation allows finance teams to focus on more strategic activities rather than being bogged down in administrative work.

AI-driven tools are being used to streamline cross-border reporting, automate data integration, and improve compliance, especially for businesses operating globally. By handling these complexities seamlessly, AI enables organizations to emphasize strategic decision-making rather than operational challenges. As generative AI continues to evolve, it is poised to further transform the financial sector by providing personalized services, detecting fraud, and improving operational efficiency, thereby positioning banks to thrive in a rapidly evolving financial landscape.

Moreover, AI provides real-time insights into financial data, allowing organizations to make proactive decisions rather than wait until the month-end or quarter-end close to analyze financial performance. This capability is critical for businesses operating in dynamic industries, where agility and informed decision-making can make all the difference. As AI technology continues to evolve, its role in financial close and consolidation will only grow, with emerging technologies such as Natural Language Processing (NLP) and Robotic Process Automation (RPA) further enhancing automation capabilities.

Improving Accuracy and Compliance

One of the major challenges in manual financial close processes is maintaining compliance with ever-evolving financial regulations. AI helps businesses stay on top of these regulatory requirements by providing continuous monitoring and ensuring that financial activities align with industry standards such as IFRS, GAAP, or local laws. AI tools automatically cross-check ongoing financial transactions and identify discrepancies or potential non-compliance before they escalate into significant issues.

By 2025, over 70% of KYC onboarding is expected to be automated, leveraging technologies such as biometric identification, digital identity verification, and advanced data analytics to simplify compliance and enhance security. This shift toward automation aligns with the broader trend of AI adoption in financial firms, where AI is increasingly used across various functions, including credit underwriting, fraud detection, customer service, and treasury management.

The widespread adoption of AI underscores its ability to enhance accuracy and compliance across different financial operations. By leveraging AI, organizations can ensure that their financial processes, including financial close, are not only efficient but also compliant with regulatory standards. This compliance reduces the risk of fines and reputational damage associated with non-compliance, allowing businesses to operate more securely and effectively in a rapidly evolving regulatory landscape.

The Future of Financial Forecasting and Planning

There has been a notable shift in the financial sector's embrace of Generative AI, with 78% of banks adopting a tactical strategy to leverage its capabilities. GenAI facilitates real-time financial forecasting and projections by analyzing current financial data to predict future performance. Beyond its role in financial close, AI plays a pivotal role in financial forecasting and planning. By analyzing historical data and trends, AI algorithms can provide more accurate forecasts, enabling finance teams to make data-driven decisions.

AI's predictive capabilities are expected to redefine financial forecasting by integrating advanced data sources, including real-time market trends, customer behavior, and geopolitical events. This shift will enhance the precision and relevance of financial predictions, allowing businesses to navigate uncertainty with confidence and agility. As GenAI continues to transform financial operations, its ability to automate tasks and provide personalized insights will further streamline decision-making processes, positioning financial institutions for strategic success in a rapidly evolving market.

For instance, AI can predict revenue and expenses based on historical patterns, identify trends and outliers that may impact financial performance, and suggest the optimal allocation of resources based on predictive analytics. These insights help businesses plan more effectively, adjust to market conditions, and maintain a competitive edge. As AI continues to evolve, its role in financial forecasting will become even more critical, enabling organizations to make proactive decisions rather than reacting to past performance.

Reducing Financial Fraud with AI

Financial fraud poses significant risks to businesses, and traditional fraud detection methods often fail to identify emerging threats in real time. AI transforms the fraud detection process using advanced machine learning algorithms that continuously monitor transactions and detect unusual patterns indicative of fraud. Unlike manual systems, which rely on preset rules, AI learns from historical data to recognize evolving fraud tactics, improving detection accuracy over time. This capability is crucial in today's fast-paced financial environment, where speed and adaptability are key to preventing financial losses.

AI fraud detection systems could help save almost $1 billion of financial fraud each year by providing real-time insights and reducing false positives. With fewer false positives, there is a strong likelihood that investigations will be highly credible and speedier, allowing businesses to respond more effectively to potential fraud. This not only enhances the efficiency of fraud detection but also boosts the credibility of investigations, ensuring that financial institutions can safeguard their operations and maintain customer trust more effectively.

AI's ability to monitor transactions in real time ensures the quick detection of fraudulent activities, allowing businesses to take swift action to prevent financial losses. This proactive approach to fraud detection is particularly important in today's fast-paced financial environment, where the speed of response can significantly impact the extent of financial damage.

Conclusion: Embracing AI for a Future-Ready Financial Close

The integration of AI into the financial close process is set to redefine how financial operations are conducted, moving toward unprecedented levels of efficiency, accuracy, and innovation. Today, AI is not just an optional tool but an essential component for businesses seeking to thrive in a competitive financial landscape. By embracing AI-driven solutions, organizations can streamline their financial processes, enhance strategic decision-making, and gain a competitive edge.

In conclusion, while manual financial close processes may seem cost-effective in the short term, they ultimately cost organizations more than they think in terms of efficiency, accuracy, and compliance. As AI continues to evolve and become more integrated into financial operations, businesses that adopt AI-driven solutions will be better positioned to navigate the complexities of the financial close process and achieve long-term success.