This entry was submitted in our ‘One to Watch’ category and our judges decided it is our Judges’ Choice this year. This solution uses a ‘chatbot’ so Microsoft’s Worldwide Credit Services team now allows sales and operations users to automatically check the credit status of a customer’s account before placing orders, and to track status of orders until they are collected.
Photo of Jayna Bundy and José Luis Martí, Microsoft.
Founded in 1975, Microsoft is the worldwide leader in software, services and solutions that help people and businesses realise their full potential.
Can ‘Edward the chatbot’ revolutionise the finance and treasury function?
The “order to cash” process involves thousands of people at Microsoft and several different teams worldwide.
The process starts with sales, moving to credit services in treasury for a credit check or credit granting, then going back to sales who create the order, and then to operations who process the order, and finally going back to credit services for collection.
Orders can have issues for many reasons, some in operations (eg missing a contract signature) and some in credit services (eg a credit event causing the customer to be placed on credit hold). To ensure timely revenue, sales frequently ask about the status of orders. In addition, even before selling, sales may have questions about the credit and collections process, such as “what’s the policy for extensions?” or “what happens if my customer doesn’t pay on time?”
There are many reports and databases that provide visibility into portions of the “order to cash” process. Even with all those reports and databases, sales still ask many questions directly to members of the credit team.
“Winning an Adam Smith Award is extremely prestigious at Microsoft. Not only does it provide us with external validation for our work, but it also makes it easier for us to reach and help people outside of our company with our solution.”
Lucas A. Meyer, Credit Analysis Manager explains, “The volume of questions increases at the end of fiscal periods, especially at quarter and fiscal-year ends, where the demand for ad-hoc status requests and FAQs becomes a significant part of the workload of some participants in this process, like collectors, operations managers, collections managers and credit managers.”
Although each question asked is simple, it’s usually time-consuming to answer, requiring opening and filtering multiple reports with different taxonomies.
Microsoft developed a chatbot. Worldwide Credit Services now allows sales and operations users to automatically check the credit status of a customer’s account before placing orders, and to track the status of orders until they are collected. The chatbot can also recognise more than 30 FAQs and return data from multiple systems.
This was done internally within Microsoft using Microsoft Azure extensively, as well as LUIS, the Language Understanding Intelligent Service, a service of Cortana Intelligence Suite. LUIS translates questions from natural language into a common language that a computer can understand.
Best practice and innovation
Chatbots are particularly useful to connect data from multiple different systems: users now have to connect with only one modern and simple interface that requires little training.
Chatbots are especially useful in processes that span multiple areas. Everybody can talk to the same chatbot and have access to one set of information.
Chatbots can gather and keep context. Although they don’t do this as well as humans, they do much better than reporting systems.
Meyer recalls, “Chatbots are easier to implement than we thought. Our relatively complex chatbot was done in less than two months by one resource. The chatbot is incredibly popular. We didn’t expect as much. Whenever we show the chatbot, we get more and more users, and several requests to demonstrate it to other groups and customers.”
Meyer concludes, “There’s a lot more that will happen with chatbots in finance and treasury and we will be at the forefront of that wave.”
The chatbot is expected to answer 12,000 questions per month in approximately 3,000 interactions, saving the equivalent of 1,800 hours of work per month. In its first two months, without being programmed to answer the full set of questions expected, the chatbot answered more than 2,000 questions.
The automation of this work allows efficiency gains of 3.8% in credit services.
Key learning points
It’s not hard to create a chatbot.
Chatbots excel in answering simple questions or starting simple processes.
Chatbots excel in being a single user interface for multiple systems, and they require little user training.
Lucas A. Meyer
Credit Analysis Manager
Lucas A. Meyer is a Computer Scientist with a MSc in finance from the University of Washington, and the Credit Analytics Manager for Microsoft Treasury, where he does BI architecture, forecasting, machine learning, chatbots and other new technology projects.