In mere months, Artificial intelligence (AI) has transitioned from a mere topic of (often negative) discussion to something considered an unignorable or indispensable solution reshaping various business sectors throughout the world.
While the innovative Generative AI (GenAI) variant of AI holds promise in assisting with important tasks of creating content and generating data, some more established forms of AI and machine learning are already actively finding use cases in areas related to financial control, risk, and compliance. They are empowering financial controllers to strengthen controls, optimize period-end processes, detect fraud, and streamline operations—all without elevating the risk profile of the organization.
Machine Learning (ML) engines meticulously study trends in data over time to profile them and identify normalities, abnormalities, or risks. This helps execute the control system and compare them to the compliance framework. For example, consider a scenario where a company has 20 journal entries auto-approved at the month’s close and five manually approved by a controller above $100,000. AI algorithms leverage these patterns to scrutinize alignment (low risk) and deviations (high risk) at the subsequent month’s close, strategically flagging them by the risk profile of the company.
Finance executives, especially finance controllers, are witnessing a dynamic transformation in their roles as the landscape of finance, itself, rapidly evolves. Controllers equipped with predictive capabilities, advanced analytics, and ML can elevate their role from providing back-office support to business partnering and real financial controls. Modern controllers are expected to take ownership of the company’s accounts and drive strategic performance. This paradigm shift is further underscored by the explosion in the volume, complexity, and variety of data available within an organization.
A growing number of top analysts see 2023 as a marker in time when AI transformed from theoretical possibility to a massive-yet-tangible opportunity. A recent report authored by Don Johnson and Alex Treuber of Ernst & Young LLP’s Financial Crimes Compliance practice even likened the ongoing sea change to the “explosion of interest” that the advent of cryptocurrencies caused a little more than a decade ago.
“ Recent developments in deep learning and computational reasoning have sparked widespread discussion of AI’s potential applications across the working world. As new AI technologies begin to enter the financial services industry, compliance professionals will be forced to rethink operational models and traditional approaches to risk management. ” –Ernst & Young
Controllership areas that are driven by AI and Advanced Analytics
A myriad of use cases exist where AI, including data science and ML techniques, can empower finance controllers to drive improvement in financial control and compliance in an organization. Beyond controllers’ standard functions such as bookkeeping, financial statements, these technologies assist in audits, budgeting, cash flow management, and statutory and regulatory compliances of taxes and corporate laws. The technology tools help financial controllers ensure that the organization complies with all relevant financial regulations, tax laws, and accounting standards.
Plug revenue leakages
Businesses lose money through revenue leakages due to various reasons, such as process inefficiencies, poor customer experience, invoice disputes, invalid deductions by customers with a relatively high volume and low value, auto-approved write-offs, etc. AI can serve as a strategic ally for financial controllers in pinpointing the root cause of such leakages and gain insights into actions that can prevent such cases.
Identify high-risk customers and take corrective action
Navigating numerous transactions across large customers poses challenges in understanding customer behavior and financial stability. This can lead to late payments or bad debts from receivables. AI tools embedded with advanced classification algorithms play a pivotal role in detecting customers moving into risk zones. AI/ML techniques empower finance controllers to proactively identify such customers but also reduce exposure to them over time.
Inventory management is one of the most challenging tasks in any manufacturing and retail organization. Inventories have different categories such as finished goods, semi-finished goods, raw material, etc., and within each category, there could be different types viz. slow-moving, fast-moving, etc. The use of AI/ML can help manage inventory by revealing insightful information about stock-keeping units (SKUs) and their associated variables such as minimum order quantity, lead times, replenishment frequency, and safety stocks. Predictive capabilities and advanced classification algorithms can help keep inventory issues such as supply mismanagement, deadstock, and wastage under strict control.
Efficient working capital management
One of the most significant benefits of AI/ML lies in optimizing cash conversion cycles. By fine-tuning receivables, inventory, and payables management, finance controllers achieve heightened performance in cash conversion and accounts receivables.
Intelligent root cause analysis
Whether through predictive analysis, scenario modeling, or descriptive root cause examination, AI/ML empowers financial controllers to discern the factors influencing product popularity. This insight aids in strategic decision-making.
Internal controls, finance risk management
AI emerges as a catalyst for integrating and enhancing various business aspects, including finance and internal controls. By evaluating and optimizing controls, AI offers valuable insights into control effectiveness, identifying weaknesses and optimizing costs.
Smooth month-end close process
Finance teams dread month-end close, with the daunting challenge of working with inaccurate, disparate, or missing data. If the finance team has to collate and combine data from different Excel sheets and manually reconcile accounts, along with multiple cards, expense management, and ERP systems, it can take days and even weeks for month-closing. AI helps overcome these challenges by significantly expediting month-end reconciliation processes.
Staying current with regulatory changes
Remaining abreast of regulatory changes is paramount for controllers and businesses. AI and ML enable controllers to ensure meticulous compliance, preventing fines, penalties, and reputational damage through timely internal audits.
Streamline regulatory compliance
AI coupled with ML and Data Analytics ensures that financial controllers have visibility and updates on changes in regulatory, taxation frameworks, and accounting standards. Automated workflows prevent oversights, ensuring compliance deadlines are met with precision and efficiency.
Similar to the arch of cybersecurity and data privacy laws and regulations, AI is cementing itself as a permanent player in organizational risk, legal, and compliance frameworks. This requires a combined focus on global, federal, state, and industry-specific levels. Research from the Gartner Finance practice found that 80% of CFOs surveyed in 2022 expected to increase spending on AI in the coming two years, aiming to reach an autonomous state within six years and improve financial controls and compliance.
Companies contemplating a shift to AI tools must conduct a comprehensive cost-benefit analysis. The selection of AI tools, such as Emagia’s GiaGPT, should prioritize data quality and security. The nature of data used for AI model training directly impacts accuracy and fairness.
What are Financial Controls?
Financial controls encompass procedures, policies, and means by which an organization monitors and controls the direction, allocation, and usage of its financial resources. Financial controls are critical for resource management and operational efficiency in any organization, enhancing business performance, growth, and compliance with statutory laws and regulations.
What is Compliance in Organizations?
Compliance necessitates organizational adherence to applicable rules and laws, including both country-specific laws, regulatory authorities’ requirements, and internal company policies and directives. A range of tools and processes is designed to ensure that non-compliance or violations can be detected, prevented, or resolved proactively before leading to serious consequences, such as criminal prosecution, fines, or severe damage to a company’s reputation.
What is Artificial Intelligence?
Artificial intelligence applications are tools that operate without human bias. Humans cannot match the accuracy and consistency of AI tools. Improved precision and speed help organizations perform time-consuming activities, including regulatory compliance smoothly, minimizing the cost of operation and the risk of regulatory violation and the associated penalties. AI’s ability to handle large volumes of data efficiently enables organizations to manage operations traditionally time-consuming for human workers.