• Written by Sonali Subhadarsini, Product Manager

On a typical Monday morning, the finance manager reviews the previous week's spending, checking for any unexpected variances from the budget, while the category manager compares supplier prices to ensure the organisation is getting the best deal, and the shift supervisor checks the maintenance log to see if any urgent repairs are needed, all of which rely on the organisation's financial data being accurate and up-to-date, which is where enterprise AI governance for finance comes in, to ensure that the financial data is handled correctly and securely, particularly when using a system like AsscherAi to query the organisation's own data in natural language and get answers in real time.

Enforcing permission at retrieval

One of the key aspects of enterprise AI governance for finance is enforcing permission at retrieval, rather than relying on the model to instruct who can see what data, which can be problematic when the system is used by people who did not build it, as they may not fully understand the intricacies of the model and the data it is querying, and may inadvertently expose sensitive information, or worse, modify the data in some way, which is why it is crucial to have controls in place that check the user's permissions at the point of retrieval, to ensure that only authorised personnel can access the financial data they need to do their jobs.

This approach also helps to prevent data breaches, as even if an unauthorised user manages to access the system, they will not be able to retrieve any sensitive information without the proper permissions, which provides an additional layer of security and peace of mind for the organisation, and is particularly important in finance, where the data is often sensitive and confidential, and where the consequences of a data breach could be severe, which is why it is essential to have robust controls in place, such as those provided by AsscherAi, to ensure that the financial data is handled correctly and securely.

Auditability

Auditability is another critical aspect of enterprise AI governance for finance, as it provides a clear record of what was asked, what was returned, and on what data, which is essential for tracking and verifying the accuracy of the financial data, and for identifying any potential issues or discrepancies, and is particularly important when using a system like AsscherAi, which can provide real-time answers to complex financial questions, but may also introduce new risks and challenges, such as the potential for errors or biases in the data, or the risk of unauthorised access or modification, which is why it is crucial to have a clear and transparent audit trail, to ensure that any issues can be quickly identified and addressed.

This audit trail can also be used to monitor user activity, and to identify any potential security risks or threats, such as unauthorised access or malicious activity, which can be particularly problematic in finance, where the data is often sensitive and confidential, and where the consequences of a security breach could be severe, which is why it is essential to have robust controls in place, such as those provided by AsscherAi, to ensure that the financial data is handled correctly and securely, and to provide a clear and transparent audit trail, to ensure that any issues can be quickly identified and addressed, and to provide peace of mind for the organisation, and for more information on how AsscherAi can help with this, please visit our finance page.

Finance data dependencies

The effectiveness of enterprise AI governance for finance depends on the quality and accuracy of the underlying financial data, which includes the general ledger, budget, open commitments and purchase orders, receivables and terms, and other financial data, which must be up-to-date and accurate, to ensure that the system can provide reliable and accurate answers to financial questions, and to prevent any potential errors or discrepancies, which could have serious consequences, such as incorrect financial reporting, or unauthorised transactions, which is why it is crucial to have robust controls in place, to ensure that the financial data is handled correctly and securely.

This data is used to inform a wide range of financial decisions, from budgeting and forecasting, to risk management and compliance, and is critical to the smooth operation of the organisation, which is why it is essential to have a clear understanding of the data dependencies, and to ensure that the data is accurate and up-to-date, to prevent any potential issues or discrepancies, and to provide peace of mind for the organisation, and for more information on how AsscherAi can help with this, please contact us to discuss your specific needs and requirements.

Changes to variance explanations and budgets

Enterprise AI governance for finance can also change the way that variance explanations are handled, as it provides real-time answers to complex financial questions, which can help to identify and explain any variances from the budget, and to provide a clear and transparent understanding of the financial performance of the organisation, which is particularly useful for finance managers, who need to be able to quickly and easily identify and explain any variances, and to take corrective action to get the organisation back on track, and to prevent any potential issues or discrepancies.

This can also help to prevent budgets that look healthy on the surface, but are actually overcommitted, which can be particularly problematic, as it can lead to financial difficulties and constraints, which can have serious consequences, such as reduced profitability, or even bankruptcy, which is why it is crucial to have robust controls in place, to ensure that the financial data is handled correctly and securely, and to provide a clear and transparent understanding of the financial performance of the organisation, which is where AsscherAi can help, by providing real-time answers to complex financial questions, and by helping to identify and explain any variances from the budget.

Measurement and evaluation

Enterprise AI governance for finance can be measured and evaluated using a range of key performance indicators, such as variance to budget, commitment coverage, and days sales outstanding, which provide a clear and transparent understanding of the financial performance of the organisation, and help to identify any potential issues or discrepancies, which can be particularly useful for finance managers, who need to be able to quickly and easily identify and explain any variances, and to take corrective action to get the organisation back on track, and to prevent any potential issues or discrepancies.

This can also help to provide a clear and transparent understanding of the effectiveness of the enterprise AI governance for finance, and to identify any areas for improvement, which can be particularly useful for organisations that are looking to improve their financial performance, and to reduce their risk and exposure, and to provide peace of mind for the organisation, and for more information on how AsscherAi can help with this, please visit our website, where you can find more information on our products and services, and how they can help your organisation to achieve its financial goals.

Limitations and exceptions

While enterprise AI governance for finance can be a powerful tool for improving financial performance and reducing risk, it is not a panacea, and there are certain limitations and exceptions that need to be considered, such as the quality and accuracy of the underlying financial data, and the potential for errors or biases in the system, which can be particularly problematic, as they can lead to incorrect financial reporting, or unauthorised transactions, which is why it is crucial to have robust controls in place, to ensure that the financial data is handled correctly and securely, and to provide a clear and transparent understanding of the financial performance of the organisation.