Industry 4.0 may very well be in the early stages, but in our digital world a ‘stage’ unfolds a bit faster than before, so financial institutions and general industrial counterparts had best be defining a future strategy with executable tactics to compete and survive. In a recent publication titled Artificial Intelligence in Corporate Banking (February 2018), we point out how machine learning can be (and is already being) utilized in wholesale banking product and service cases. The article in Global Banking & Finance review discusses the value of data in the overall improvement of corporate financial intelligence and operations.
‘As enterprises become more automated and intelligent, finance is expected to become the knowledge hub of the organization, not only reporting but also simulating outcomes and predicting results. To achieve this, finance leadership should be spending a lot more time in business value creation and driving business strategy rather than providing inputs based on historical numbers.’
When speaking of payments, many tend to focus on one side of the equation, meaning payments initiation and the onslaught of faster schemes, APIs and eventually (perhaps) cryptos entering the mix in a scalable way. The authors in this piece point us towards the less glamorous part of the payments equation, receivables, and how machine learning is being used to reduce costs associated with repetitive tasks, most often associated with manual processes for matching inbound payments with invoices and remittance data, among other things.
‘To best illustrate how machine learning can support finance in tasks such as payment clearing, let’s take a look at energy management company Alpiq. Its finance team has been using a traditional rule-based approach for the payment clearing process, making maintaining rules a challenge as they are constantly changing. By moving to a single integrated environment that learns from accountants’ behavior and leverages both historical data and existing AR workflows they will be able to amplify the accounts receivable process by matching incoming payments to open invoices. With the help of intelligently extracted information from payment advice documents and historical data records of successfully matched payments and invoices, they will be able to reduce manual efforts, error-prone and repetitive tasks.’
Machine learning allows for self-adapting of logical matching algorithms, using transactional data, to increase digital payments clearing and reduce manual staff intervention. Cost savings is a clear goal, however, attendant analytical intelligence and interoperable systems create the longer range success metrics to deliver a more efficient financial organization.
‘This is where using machine-learning-enabled solutions in payment clearing can be advantageous. The technology seamlessly adapts to changing conditions, as it is constantly learning from accountants’ actions, capturing much richer detail of customer and country-specific behavior, without the expense of manually defining detailed rules…. Instead of manually reviewing months of spreadsheets, self-learning algorithms can find patterns and solutions in data to make decisions easily, and with confidence. Shared service teams no longer need to spend time updating payment rules and regulations. This freedom enables them to process higher transaction volumes, focus on strategic tasks, and scale with the business by delivering insights and informing decision-making on demand.’
Overview by Steve Murphy, Director, Commercial and Enterprise Payments Advisory Service at Mercator Advisory Group