Finance Sector is leveraging ML and AI to provide real-time insights to drive efficiency and make informed decisions across the enterprise.
FREMONT, CA: One of the main application of artificial intelligence (AI) and machine language (ML) is to automate procedural tasks. Finance is leading the charge in leveraging ML and AI to provide real-time insights to drive efficiency and make informed decisions across the enterprise. Thus finance will be among the top business units to experience the impact of these technologies. AI will be the game-changer in this aspect. Here are the ways in which AI will contribute to the above:
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Clearing Invoice Payments
Treasury clerks or accounts receivable clerks struggle to clear invoice payments when customers cover the invoices in one payment, submit incorrect amounts or fail to include invoice numbers in their payments. In such cases, the employee either has to contact the customer for clarification or manually add up various invoices to match the payment amount. For short payments, the employee either has to ask for the remaining amount from the customer or request for approvals to accept the quick payment.
An intelligent AI-based system can assist by suggesting invoices that might match the amount paid by the customer and depending on established thresholds, automatically generate a delta invoice or clear the short payments.
Auditing Expense Claims
Expense claim auditing is another transactional finance task that can significantly gain from AI-driven automation. Conventionally, finance employees are required to ensure that receipts are genuine, compare claim amounts, and ensure that they are in line with company policy. Even though the claim process can be simplified, the auditing process is still manual.
However, AI and ML can support this process, perform an audit for all the claims, and forward only questionable claims to a manager for approval. Such systems can also read receipts to make sure that they are genuine.
The accounting team has to spend a significant amount of time to calculate bonus accruals. The team observes the headcount salaries and bonus plans and tries to forecast all KPIs in compensation plans. According to that, finance managers estimate the most accurate accrual, which is no more than a matter of luck.
With AI solutions, the calculation can be left to a machine that considers the entire available system data as well as predictive analysis to provide an unbiased accrual.
Mapping Risk Assessments
Finance teams are tasked with evaluating individual projects as per customer characteristics, such as industry, size, maturity, landscape, and so on while assessing commercial proposals for service projects. For the assessment, the finance employees often depend on managers who have experience in similar projects. Though it may work to an extent, still the decisions are limited by the individual's perspective.
ML can allow teams to access each implementation project the company has ever completed, over the world. With the data generated, teams can map the proposed project against all the endeavors from the past to arrive at a better-informed risk assessment.
Assessing Detailed Analysis
Most of the people in financial planning and analysis are faced with countless calls regarding the growth of the business or the revenue in the last quarter. Digital assistants can be a game-changer in this aspect. They can give decision-makers with instant access to information and provide the latest financial results.