As the years go by, there are frequent new developments in technology. These developments are typically for the better. They lead to further simplification of our daily lives, but more importantly make our jobs less complex in the world of business. These developments in technology along with human innovation, eventually disrupt the way business processes are conducted in organizations across the world. This is the same for various businesses in all types of industries. The current hot topics in the Financial Industry are – Robotics Process Automation (RPA) and Machine Learning. What exactly are they, and how do they affect the Financial industry?
Robotic Process Automation (RPA)
To find a true definition of RPA would be difficult. RPA can be described as how the application of software or technology can be programmed to capture, decipher, and manipulate data, to subsequently trigger responses from and connect to other digital systems to fully complete a business process.
For example, RPA is ideal for a piece of software to automatically enter huge volumes of invoices into an Enterprise Resource Planning (ERP) system. Once entered, then auto-matching occurs between these uploaded invoices against transactions from your Bank Statements, and subsequent updating of your General Ledger (GL). A process like this takes the boring manual entry out of the equation for the user. It also moves towards a system that is more like straight-through-processing (STP). It makes customers feel more confident in doing business with you, due to the speed and security of the transaction process. Employees in your Finance department are also happy, as it takes the repetitive manual entry out of the process. It also makes their daily lives less stressful.
The benefits of RPA are now huge for Finance departments. They equal to faster processing times, increased volumes of automated transactions, less manual work, and an enormous reduction in human error. All of this leads directly to the bottom line for a company, and it can have a huge impact in defining the success of a business. So far, RPA has helped to make current processes much faster and more efficient.
Combine RPA with machine learning and algorithms, and you have created a new world of possibilities to further increase automation in Finance departments. The combination of RPA and machine learning was once seen as the future, but it has very quickly become the present.
Machine learning is based on algorithms that can learn from data without having to rely on rules based programming. In this case, “learning” means loading numerous images, numeric values, or text that represent the pattern to be learned into an algorithm. As a result of this teaching, the algorithm is able to become increasingly better at identifying a particular pattern among the data set.
Machine learning has been around for a lot longer than we think, over 50 years in fact! Past examples of machine learning used in the Finance industry have been for: Fraud Detection, Loan Approvals, Portfolio Management, and Automated Trading Systems.
All of the decisions, outcomes and red flags from the above were based on the algorithms working with the data set available. Recognizing the suspicious patterns in the case of fraud detection, or identifying positive patterns to achieve an approval in the case of loan decisions. These are used every day in the background, and have led to loan decisions being achieved quicker, and fraud being detected immediately.
In Finance departments, machine learning can play a huge part also. For example, it has the ability to use the data set to predict when customers are going to pay, which in turn leads to better cash forecasting and improved company planning and growth.
Cashbook – RPA & Machine Learning
Where do Cashbook fit in to all of this you may ask? Cashbook are trusted experts in Cash Application Automation. We use both RPA and machine learning when it comes to designing our Cash Management software products. The main Cashbook products are for Accounts Receivable (AR) Automation, Accounts Payable (AP) Automation, and Bank Reconciliation Automation (Bank Rec). Cashbook also have useful tools for Customer Deductions, and a Data Extraction Lockbox tool designed to reduce banking fees for our customers in the USA.
Cashbook use both RPA and machine learning to automate and process key Cash Application procedures for our customers. The advances in RPA and machine learning allows for both repetitive and complex procedures to be automated to high levels, sometimes up to 95%. Thus, reducing both the capacity for human error and the amount of time spent on Cash Management in an organization. Cashbook uses RPA to automatically create files, which are then directly posted to the ERP system of our customer.
RPA and Machine Learning have shaped Cash Management Processes
By using RPA in Accounts Receivable, Cashbook can read files directly from the bank for invoice automation and exact auto-matching. Specific customer based algorithms allow for different procedures for each customer if required. Sometimes, our client’s customers send through email remittances. Cashbook can extract these remittances directly from email attachments or the body of the email by way of Data Extraction, and subsequently process them automatically. This automation of the AR process, reduces the amount of time spent processing bank statements. It also guarantees a reduction in DSO, and allows our customers to re-allocate resources to higher value activities in their company.
In Accounts Payable, once the user has paid the outstanding invoices in the ERP system, Cashbook then creates the necessary banking files by RPA to make the various payments. This automation of the AP process allows for a single payments platform which can be utilized across multiple work sites, banks, and all currencies. It can facilitate the creation of a single shared service center for a company that may have multiple locations across the world, and also in different continents.
By use of RPA in Bank Reconciliation, Cashbook can automatically upload bank statements received from the bank, this eliminates the need for manual entry. These bank statements are then auto-matched against cash transactions on the ERP system, and against current bank account transactions. This subsequently leads to direct postings by Cashbook to your GL.
With the Data Extraction Lockbox tool, Cashbook uses RPA to automatically receive the lockbox file directly from the bank overnight, this is done through the use of hot folders. Users then create a template for each specific customer remittance, and communicate to the software where the essential data is located on the remittance file. By highlighting the key columns, headings and creating footers on the remittance file, Cashbook creates the template which is then stored for future use. Each time that specific customer makes a payment, the software remembers to use that saved template for the payment. Cashbook’s Data Extraction technology can automatically identify and interpret numeric data and symbols directly from the remittance, it then auto-matches them against the outstanding transactions. This process can be automated to an extraordinarily high level, and greatly reduces expensive bank lockbox fees for users.
With the Deductions tool, Cashbook can quickly identify the various codes that retailers use when making deductions from your sale price. Identification of these codes can greatly benefit companies who process thousands of invoices per year. It would be near impossible for an employee to manually identify all of the deductions that occur. Even small deductions from one customer, can add up to a significant amount of value lost to the company over the course of a financial year. Through machine learning, the deductions tool can also identify and highlight customers that are showing a clear pattern of making the same deductions systematically. This tool shows the major cost savings that can be made, by embracing the developments made in technology for the finance industry.
Cashbook also has a Portals feature for its clients, this feature allows both clients and their customers to log in remotely and securely. Once logged in, they can proceed to download the required statements, remittance data, and key information. Portals also facilitate the extraction of data and the processing of data through Cashbook and directly to the ERP system.
Finally, it is usually CFO’s and Finance Managers who will have to shoulder the blame for any fiscal impact on their company. This fiscal impact may be down to having a higher than average rate of DSO, having archaic and unreliable financial processes in place, or having unhappy staff that make costly human errors due to the repetitive nature of manual processes. All of these variables lead to loss of company time, money and inevitably human resources. In the next couple of years, it will be very interesting to see how many companies move with the times and fully embrace Cash Application automation, or how many will stick to what they know and run the risk of getting left behind.
Martin Whelan, Chief Technology Officer, Cashbook.
Michael Twomey, Marketing Manager, Cashbook.