While consumer-facing applications of machine learning (ML) have gotten most of the attention (think Netflix movie recommendations, Amazon product selection and efficient driving routes in Waze), the back office deserves more Artificial Intelligence (AI) focus.
Enterprise level systems that run the business – think finance, accounting, operations, human resource and procurement – tend to be large, complex and process centric. But, they also use and produce large amounts of both structured and unstructured data that can be handled in new ways to to save time and money. ML combined with solution-specific software can dramatically improve the speed, accuracy and effectiveness of back-office operations and help organizations re-imagine how back-office work gets done.
Here are five specific applications of ML that can be used to improve back-office operations:
Account Reconciliation (Finance)
Account reconciliations are painful, nasty and error prone. They are also critical to every business in order to have the proper controls in place to close the books accurately and on-time. Many companies do this manually (which really means using Excel + Macros + Pivot Tables + Visual Basic) or have invested in RPA, which doesn’t get you very far, or in a Boolean rules-based system, which is expensive to set up and not super accurate.
Challenges in Account Reconciliation
An ML approach is ideal for account reconciliations, specifically matching reconciliations, because you have ground-truth data—previous successful matched transactions, and consistent fields in subsequent reconciliations. The challenge has been that for large and complex data sets, the combinatorial problem of matching is really hard. New companies like Sigma IQ have focused on this problem and solved it with a combination of machine learning and point-solution software as a hosted platform. Automating financial reconciliations (like Bank Accounts, POS, Gateway, Intercompany, etc.) and operational reconciliations (inventory specifically) will change the experience of account reconciliation from a manual chore to a check-box type item.
Invoice Processing/Accounts payable (Accounting)
Every business deals with invoices at some level, and as Natural Language Processing (NLP) and ML advance, these improvements will roll down from the enterprise level to small businesses.
Aberdeen Group indicates that well-implemented accounts payable systems can reduce time and costs by 75%, decrease error rates by 85% and improve process efficiency by 300%, so it makes sense to pursue, right?
Using ML to Augment Accounting
Companies like AODocs are extending their NLP and ML capabilities to take some of the pain out of invoice management by automatically capturing information from invoices and triggering the appropriate workflow. These types of solutions can greatly reduce or eliminate manual data entry, increase accuracy, and match invoice to purchase order.
Employee attrition detection (HR)
There are many applications of AI in the HR function, including applicant tracking and recruiting (resume scanning and skills analysis), attracting talent before hiring, individual skills management/performance development (primarily via regular assessment analysis), and enterprise resource management.
Using ML to Track Employee Satisfaction
One interesting use case from an ML-NLP perspective is employee attrition. Hiring is expensive, and retaining employees and keeping them happy is imperative to sustainable growth. Identifying attrition risk requires source data—like a consistently applied employee survey that uses unstructured data analysis for the open field comments. Overlaying this data with factors such as tenure, time since last pay raise/promotion, sick days used, scores on performance reviews, skill set competitiveness with market, and generally available employment market data can help assess probability of satisfaction.
Predicting repairs and upkeep for machinery (operations)
The influx of sensors into all types of equipment including trucks, oil rigs, assembly lines, and trains means an explosion of data on usage, wear, and tear of such equipment. Pairing this data with historical records on when certain types of equipment need certain pre-emptive maintenance means that expensive machinery can be scheduled for downtime and repair not just based on number of hours used or number of miles driven, but what actual usage is.
Predix is a General Electric company that powers industrial apps to process the historic performance data of equipment. Its sensors and signals can be used to discern a variety of operational outcomes such as when machinery might fail so that you can plan for—or even prevent—major malfunctions and downtime.
Predictive Analytics for Stock in Transit (procurement)
For companies that spend a lot of money on hard goods that need to be moved for either input into manufacturing or delivery to a retail shelf, stock in transit is a major source of opportunity for applying ML models to predict when goods will arrive at a destination.
Item tracking has improved dramatically with sensors, but it is only a point-in-time solution that doesn’t predict when the goods will arrive or when they should arrive. Weather, traffic, type of transport, risk probabilities, and historical performance are all part of the data that can help operations nail the flow of goods for optimal process timing.
Stock in Transit Solutions
SAP S/4HANA has an entire module dedicated to making trade-off predictions between different options for stock in transit solutions to meet the customer order objectives.
Ample Opportunity for Machine Learning to Help the Back Office Put Out Fires
These are just five of the hundreds of use cases ML paired with solution-specific software can be applied to in order to improve the way the back office does work. Whether it is cutting down on manual tasks, improving accuracy, reducing costs, or helping teams change their critical processes wholesale, machine learning can augment nearly every back-office process.