Can AI reduce variance explanations that arrive after the period?
AsscherAi reduces variance explanations that arrive after the period by analysing general ledgers and budgets in real time, enabling finance teams to identify root causes quickly, such as a shift supervisor checking maintenance logs or a category manager comparing supplier prices, and take corrective action.
A finance director has to make a decision this week about whether to investigate a significant variance in the company's expenses, and currently bases it on a lengthy manual review of the general ledger and budget reports. However, this process can be time-consuming and may not always identify the root cause of the variance, especially when explanations arrive after the period. The question is, can AI reduce variance explanations that arrive after the period, and if so, how does it work?
What has to be in the data for this to be possible
To reduce variance explanations that arrive after the period, the data must include a complete and accurate general ledger and budget. This is because AI relies on high-quality data to identify patterns and anomalies, and a general ledger and budget provide the necessary information to understand the company's financial performance. A shift supervisor, for example, checks the maintenance log to identify any unusual expenses that may have contributed to the variance.
The general ledger and budget must be up-to-date and reflect all financial transactions, including invoices, payments, and journal entries. This ensures that the AI system has a comprehensive view of the company's financial situation and can identify the root cause of any variances. Most people assume that AI can work with incomplete or inaccurate data, but this is wrong - AI is only as good as the data it is given.
The mechanism, stated plainly
The mechanism by which AI reduces variance explanations that arrive after the period is through automated analysis of the general ledger and budget. The AI system uses natural language processing to identify and extract relevant information from the financial reports, and then applies machine learning algorithms to identify patterns and anomalies. This process is much faster and more accurate than manual review, and can identify the root cause of variances that may have gone unnoticed.
For example, a category manager compares two suppliers to identify any differences in pricing or delivery terms that may have contributed to the variance. The AI system can analyze the data from the general ledger and budget to identify the supplier that is causing the variance, and provide recommendations for improvement. This is a significant improvement over manual review, which can be time-consuming and may not always identify the root cause of the variance.
What it will not fix
AI will not fix underlying issues with the company's financial processes or systems. If the general ledger and budget are not accurate or up-to-date, the AI system will not be able to identify the root cause of variances. Additionally, AI will not replace human judgement and expertise - it is a tool that should be used to support and augment human decision-making, not replace it. Many people assume that AI can fix all problems, but this is not the case - AI is a tool that must be used in conjunction with human expertise.
A common mistake is to assume that AI can work in isolation, without human input or oversight. However, this is wrong - AI requires human expertise and judgement to ensure that it is working effectively and providing accurate results. For more information on how AI can be used in finance, see our finance page.
How you would know it worked: variance to budget
The success of AI in reducing variance explanations that arrive after the period can be measured by tracking the variance to budget over time. If the AI system is working effectively, the variance to budget should decrease, indicating that the root cause of variances is being identified and addressed. This can be measured by tracking key performance indicators (KPIs) such as the variance to budget ratio, and comparing it to historical data.
For example, a finance team can track the variance to budget ratio over time to see if the AI system is having a positive impact. If the ratio is decreasing, it indicates that the AI system is working effectively and providing accurate results. To get started with implementing AI in your finance team, contact us to discuss your options.
What this does not do, or when it is the wrong choice
This solution does not provide a complete replacement for human financial analysis and decision-making. While AI can provide valuable insights and recommendations, it is not a substitute for human expertise and judgement. Additionally, AI may not be the best solution for small or simple financial variances, where the cost and complexity of implementing an AI system may outweigh the benefits.
In some cases, a simple manual review of the general ledger and budget may be sufficient to identify the root cause of a variance, and AI may not be necessary. It is therefore important to carefully consider the needs and requirements of your finance team before implementing an AI solution. By understanding what AI can and cannot do, you can make an informed decision about whether it is the right choice for your organisation.
Frequently asked questions
How can AI help reduce variance explanations that arrive after the period?
AI can analyse general ledgers and budgets in real time to identify root causes of variances, enabling finance teams to take corrective action quickly.
What data is required for AI to reduce variance explanations?
AI requires complete and accurate general ledgers and budgets to identify patterns and anomalies, and provide recommendations for improvement.
Can AI replace human judgement and expertise in finance?
No, AI is a tool that should be used to support and augment human decision-making, not replace it, as it requires human expertise and judgement to ensure accurate results.
How can the success of AI in reducing variance explanations be measured?
The success of AI can be measured by tracking the variance to budget over time, using key performance indicators such as the variance to budget ratio, to see if the AI system is having a positive impact.