Machine learning in finance improves processes by automating tasks and reducing errors through the use of data algorithms that improve in accuracy as time goes on.
While we've put together 10 reasons why businesses implement machine learning in finance, the main reason is simple - it saves money.
Machine learning is a form of Artificial Intelligence (AI) that gives computers the ability to learn without out being obviously programmed. These computer systems learn from data, looking for repeat patterns or user behaviour: just like how your Netflix recommendations are made or how relevant stories and images appear on your Instagram feed.
The idea behind any AI is to mimic human behaviour and act and perform tasks as intelligently as possible. With machine learning, data is read by the computer software, remembered and then improved upon,
Machine learning in finance reduces the need for labour of many types, the most noticeable being data entry. Data entry is time consuming and a common area for mistakes due to the repetitive and monotonous nature of the work.
When these mistakes involve the businesses’ finances, they must be avoided at all costs. By using automatic tools, data is accurate, accessible for analysis or available to provide advice to customers.
The beauty of machine learning in finance is that the software learns from experience. To put it simply, it just needs to be fed with data and then it automatically adjusts its parameters to improve outcomes.
DocuWare has applied machine learning in finance long before it was a buzz word. DocuWare's Intelligent Indexing service is as useful today as it was when it was introduced over 10 years ago.
Machine learning can play a significant role in improving accounts payable (AP) processes by automating tasks, reducing errors, and increasing efficiency. Here are 10 ways in which machine learning will help your finance department.
Data extraction is automated as machine learning algorithms can take relevant data from invoices with no manual intervention. Important pieces of information such as invoice number, date, supplier details, and line-item information are seamlessly captured, stored and pushed into a finance system.
Machine learning models can cross-verify the extracted data with purchase orders and receipts, ensuring accuracy and reducing the risk of errors. This also speeds up processing times as no manual checks are required.
Machine learning in finance assists with predictive approval routing. If an invoice matches a purchase order, it can be automatically flagged and routed for approval. This reduces approval bottlenecks when an approver is on holiday, or if the approval request is lost amongst other emails.
Machine learning algorithms can also flag potentially fraudulent invoices by detecting anomalies and patterns indicative of fraud. This only increase the speed and efficiency of automated invoice processing.
Machine learning can help analyse historical supplier data, identifying trends in delivery times, pricing, and quality to make informed decisions about supplier relationships. The software can assess supplier risk by analysing financial data, news articles, and other sources to identify potential issues that might affect payments.
As a result of this data, businesses may decide to reward good suppliers, or make the decision to end a relationship with another if the evidence shows their performance is not up to scratch.
With invoice processing already being improved by AI, machine learning in finance can also assist by identifying opportunities for early payment discounts. Whether it's identifying repeated supplier invoices that were paid early or prioritising invoices that offer the best terms and flagging them to be paid first.
Cash flow needs can also be predicted by analysing historical payment patterns and pending invoices.
Machine learning can facilitate the three-way matching of invoices, purchase orders and delivery notes. Discrepancies can be automatically reconciled which reduces manual intervention further, freeing staff time to complete other tasks.
GL coding can be automated by the software, suggesting general ledger (GL) coding for invoices based on historical data and predefined rules.
Machine learning algorithms in document management software can classify documents based on their content, such as invoices, receipts, contracts, and bank statements, making them easier to organise and retrieve. Once the software is “trained” there is very little need for any manual data entry and incorrectly stored documents are a thing of the past.
Machine learning helps identify and maintain a detailed audit trail of all accounts payable activities, providing transparency and supporting compliance requirements. The software can can flag unusual or suspicious activities, helping auditors identify potential issues more effectively.
With machine learning in finance, insights into spending patterns can be highlighted alongside supplier performance and other accounts payable metrics that enables better decision making.
Machine learning powered chatbots can handle supplier enquiries and internal user questions, improving response times and reducing the burden on AP staff.
By implementing machine learning in finance and across the accounts payable process, organisations can streamline operations, reduce costs, improve accuracy, and gain valuable insights into their financial activities.
However, it's important to note that successful implementation requires data quality, proper training of software systems, and ongoing monitoring to ensure optimal performance.
Our document management solutions have had built in machine learning functionality for years, and assisting finance teams to improve their processes is what we do best.
If you're thinking about implementing machine learning in finance then please get in touch and book a time in our diary below.
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