In this episode, Pre-Sales Solutions Consultant and credit risk management expert, Don Mills discusses how understanding risk early in the customer’s lifecycle can drive actionable insights downstream. Businesses that think about an integrated credit risk management and collections strategy and share interconnected data are having a significant impact on DSO and the customer experience.
In this episode, Pre-Sales Solutions Consultant and credit risk management expert, Don Mills discusses how understanding risk early in the customer’s lifecycle can drive actionable insights downstream. Businesses that think about an integrated credit risk management and collections strategy and share interconnected data are having a significant impact on DSO and the customer experience.
Welcome to Finance to Futurist, a Sidetrade podcast series on how innovation data and AI are disrupting order-to-cash.
Natalie Silverman:Hi, this is Natalie Silverman for Sidetrade. Welcome to Finance to Futurist. On today's episode, we're discussing how understanding risk early in the customer's lifecycle can drive actionable insights downstream. Businesses that think about an integrated credit risk management and collection strategy are having a significant impact on DSO and the customer experience. Please welcome Sidetrade's, pre-sales solutions consultant, and credit risk management expert, Don Mills. Good morning, Don and thanks for sitting down for Finance to Futurist.
Don Mills:Well, thank you for having me, Natalie, appreciate being here.
Natalie Silverman:Yeah, it's great to have you on. We always like to have new guests here. And maybe the first question I'll ask you is, if you wouldn't mind introducing yourself and your background and your role at Sidetrade?
Don Mills:Sure. My name is Don Mills, I have been in order-to-cash software space for a little over 15 years. And prior to that I was with several Fortune 100 companies for about another 15 years. So yes, I'm old. Dealing with, you know, operational, financial and risk management across organizations. So and right now with my side trade role, I am a Solutions Consultant. And so I work with the sales teams and the clients in order to provide them the best solution possible to help solve the needs they have as an organization.
Natalie Silverman:Well, we're very happy that you're here. And with all of your knowledge, we wanted to talk a little bit today on a different topic that we haven't covered yet on the podcast, and that is credit risk management. Let's talk a little bit about I think, the beginning of the cycle, right, we we talk a lot about e-Invoicing, and this idea of time-to-invoice and how that beginning of the cycle really has a profound impact on the end of the cycle, and DSO. But I wanted to talk a little bit about credit risk management, and how that again, can also have a great impact on collections and how they go hand in hand.
Don Mills:Sure. So credit is the introduction to the customer. That's where I'm first learning about this customer that wants to do business with me and ultimately become a partner and you know, buy product or services from me is part of that relationship building. You know, as far as from a credit perspective, you know, I'm vetting that customer, I'm, you know, are they a good risk level to do business with us. And so basically, what my job is, as that credit analyst is to alleviate some of the risk or work I may have to do downstream in a collections department, you know, because this customer was not a good payer, or I didn't evaluate them correctly, or maybe I did evaluate them correctly. So what I'm looking to do is when I have that relationship with that potential customer determining that risk, it lets the collector know, downstream, you know, if there are issues, you know, what type of risk level does this customer have? And that can work into my risk strategies downstream. And vice versa. I can also see, you know, from a collections perspective, how are they paying me, and that could impact things on the credit side as well. So there really is from a data sharing perspective and a data interchange that there is information that is helpful from both the credit side and the collection side. And they really work hand in hand to make our organization successful.
Natalie Silverman:Yeah, I think that makes a lot of sense. And, you know, as you said, it really does set the tone for this customer within an organization. And like you said, if you're going to be a good partner, from a sales perspective, even seems that the customer journey, it really does start with that credit decisioning. And also that relationship management piece.
Don Mills:Oh, absolutely. I'm introducing them to the organization, right at that point in time, I'm determining based on the information I may have, whether that's, you know, credit bureau information from that customer, if I've got a credit application from that customer, or any other type of data that I might be using in order to evaluate that customer within our organization. I'm the one setting the tone for how that customer is hopefully going to transact within our organization going forward. And again, I'm hoping that my decision criteria that I can avert from having to have a heavy handed collection side to go, you know, be constantly looking after this customer because that's, that's another thing I'm doing is, you know, is that relationship with that customer that I'm building from determining what their credit worthiness is, it really does set the tone for future interactions that I may have with him in my organization.
Natalie Silverman:Sure. And I want to go back a little bit to something you said because you mentioned credit bureaus, and again, I don't want to put down credit bureaus, credit bureaus are good partners of ours as well. But I'd love to talk about the data piece and how how data has actually evolved in this credit decisioning process. And the trend seems to be moving from moment in time data to real time data. So maybe can you talk a little bit more about how data has become more actionable?
Don Mills:Sure, absolutely. So traditionally, from a credit department, a lot of the credit policy revolves around being able to pull data in from third party bureaus. You know, if I've got a new relationship with a customer, I don't maybe don't know a whole lot about them. So I'm dependent on things like financial statements, or third party bureau information to provide me enough background about this customer in order to make an educated decision. Now, the problem I have, or the problem that that data has is that that data is always dated, it's, you know, with financials, at best, I'm going to see those monthly, most likely not until quarterly. And by that time, it could be too late with Bureau information, typically, that updates on a monthly basis, unless there's just a huge issue, like a bankruptcy or something like that, maybe provide me some more leeway as far as knowing sooner. But the reality of it is, is that data is a snapshot in the past. And it's usually anywhere from you know, at least a few weeks, to several months in the past. Now, with what Sidetrade offers is Sidetrade offers information from the data lake. And what that data lake information is, is that's a pool of information about these customers, their payment behaviors, how it's changing, and that payment behavior is dictated on a daily basis. So that's as bad as real time as you can get without sitting in a chair, you know, entering the general ledger information for those customers is I've got this data lake providing me how is this payment behavior changing? How are they paying other vendors, because that's also important to me, for instance, if a customer is paying me on time, and it's looking good, and you know, all my metrics look good. But then I see this information from the data lake saying, you know, hey, wait a minute, they're starting to pay all these other vendors really slowly, their average days to pay keeps increasing, you know, at some point in time, that's going to hit you, at some point in time, you're going to be the one that they're paying slowly. So that information in the data lake and that the AI can use by analyzing that information in the data lake is paramount to success in the credit department. Because I can use that information for alerts, I can use that information to potentially you know, go review and account and take action on that customer before it's too late. So really gives me a proactive stance that I don't have available today. To me as a credit manager within a you know, that particular discipline, I just don't have that without that data lake and that data lake information being updated in a near real time fashion.
Natalie Silverman:I think you hit the nail on the head, it's it is about being more dynamic and proactive. And that scorecard data and that risk management piece of it, it becomes more actionable. And I think it helps get firms and finance leaders into what we call moving from tactical to strategic. Right. So this actually segues pretty nicely into my next question, because as we're talking about that transformation, let's say from tactical to strategic order-to-cash. Is transformation a buzzword a little bit, but hey, we're seeing it as one of the highest priorities for 2023 for a lot of finance leaders. So you know, again, taking the context of credit risk management, how you know, our finance leaders using that in a way to transform from tactical to strategic.
Don Mills:Absolutely. So that's, in my estimation, this is where the rubber is going to meet the road, both here and going forward is this real time integration to date is, you know, data's king, I need to know the most relevant information that I can as quickly as I can, because things change so dramatically, so quickly, both technically and within our economy or the economies of the world. So for customers to be able to have access to this type of data, such as you know, the data lake or even more importantly, that AI that lives on top of that data that's analyzing that information, providing me real time insight into customer behavior, payment behavior is are those things changing? Isn't it changing just for me, or is it changing for everyone I think is critical for anyone that's in a managerial or executive role to understand how that impacts their business and their bottom line because as far as I can sell everything I want to sell I may have the greatest revenue in the world but if I can't bring those dollars in the door, it's really all for naught. This type of information provides an arms those users or those folks in that organization to be able to go out and actually divine a plan to bring those dollars in the door based off this information that they're getting from sources such as side trades data lake and Aimie living on top which is you know, the AI that Sidetrade is using to evaluate that information is just something that is a differentiator in credit decisioning process, it impacts the credit policy a great deal and I hope fully we'll see this continue to grow as people believe more in the AI and more in the data lake component of being able to use that information to really drive their business and drive their success.
Natalie Silverman:Yeah, that's a great point. And you know, one question I'd love to ask you on the back end of that is some of these concepts are very abstract, right? I mean, let's talk about ChatGPT. That's the biggest buzzword, I'd say in every industry. And, you know, we're looking at robotics and machine learning and AI and ideas like collaborative intelligence, right, which essentially, is crowdsourcing data, but how do you again, take these abstract concepts, and we just saw Microsoft announced their integration with their Microsoft Copilot that's really bringing ChatGPT into an everyday function like using Microsoft 365. So I'm trying to, you know, see, how do we take abstract concepts? Like you just mentioned, data lake collaborative intelligence, AI? And how do we apply that back to some of these day to day credit activities?
Don Mills:This is probably I think, one of the most straightforward aspects of the AI. And this collaborative intelligence is being provided through the AI and the data lake is resources are not growing at the same rate, that typically businesses are growing, they're highly inefficient. They're trying to manage things that they're never going to get to. It's just a reality of the smaller organizations up to you know, Fortune 100 companies struggle with the same things. And going forward, most companies are looking for a way to manage exceptions, they're looking for a way for something to point out to them, what customers do I need to focus on? What things do I need to go look at without having to dig through all of the analytics and the data themselves, that's what AI is for AI is to go through and do that heavy lifting on the analysis and serve up or tee up to those credit professionals, here's what you need to really focus on based on the rules that are important to me. So these are the rules that I have, you know, my high dollar transactions or those more medium sized transactions, whatever, it might be important to me that I want to keep an eye on the AI. And in our case, Aimie is sitting in the background, being the super credit analyst that's evaluating that entire portfolio of customers I have and determining which ones really need our attention. No, typically, it's not the big companies that are Exxons and Walmarts of the world, you need to be keeping your eye on but it's really hard for those credit departments to keep an eye on these customers that are more mid size, and even smaller, they just don't ever get to them. And the same story can be told on the collection side, it's very difficult to be a collector in today's world working off of spreadsheets, or even working off of dated workflows that are, here's how long I'm past due, here's an action to take. AI allows us to free up those resources to allow them to really use their skill set in order to just work on the things that are the exceptions and not focus their time or energies on all of the rubber stamp activities that they would be doing anyway, you know, if Walmart wants credit for me, I'm probably not going to tell them no, you know, in most instances, so just taking the time out of an analyst having to go deal with those types of scenarios and focusing just on the customers who really need my attention. That is the direction these companies are trying to go that they need to go that they want to go because it impacts their bottom line. And it's a real difference between, you know, being in the black or being in the red, the end of the day and your financials.
Natalie Silverman:Great point. And thank you for that. Because again, I think sometimes people get so lost with some of these concepts. And you have to bring it back to how does it affect my day to day, like you said, how does it affect the bottom line working capital, you know, inflation and market drivers that we can't control necessarily are always in the background. But being able to leverage some of these tools and technology and new ways of thinking, I think are great ways to combat some of these drivers, again, that are external that we can't always control. Last question for you, Don, let's talk a little bit about platform solutions versus point solutions. Because we all know we have a challenge. Sometimes that's right in front of us. It might be something that's short term, potentially. But at the end of the day, if we take a step back, and we talk about again, how the beginning of the cycle affects the end, why do you think it's important that finance leaders think a bit more strategically about a platform solution vs. one point solution,
Don Mills:Platform solutions are always going to have an advantage. And that advantage is going to be around the sharing of data, it's going to allow them to have a consistency across that order to cache space that most of them don't have today. It's all broken out functionality between well whether it's handling my collections on Excel spreadsheets, or in another system versus you know, how I'm managing my credit versus how I'm applying my cash, all of those Things could be separate disparate units. The reality of it is, is there are CIOs, technical leaders out in those companies who are trying to consolidate the software, it's an expensive proposition to have these different software's, especially those that are living on premise, because it takes resources to take to not only have that hardware within the organization, but also to manage that hardware within the organization for that software to exist to be able to do those jobs with side trade with, you know, a completely cloud-based offering not only as a cloud based, so it takes those resources that can be again reassigned or reused elsewhere in your organization. But it also allows, again, for the consolidation of all of that different functionality to happen in one place, you know, whether it's credit risk collection activities, cash application, all of those major order to cash functionalities can happen in one platform using information that's shareable between those different platforms as well. So now, as a user, I can have my credit decisioning, in the risk level that I might be associating to that customer at work over in my collections platform, because I may want to, you know, the AI may use that information to say, Oh, this is a higher risk customer, I want to be more aggressive with this customer, maybe I want to have a more contact with this customer than I do with a lower risk customer or even a key account. I can also use that information from the collection side as we're getting data in from the invoices and the payments and seeing the payment behavior of the customer changing or seeing past do percentages on a customer increasing that can impact over on the credit side and provide them alerts or situations where they may want to go look at that account. And then once you layer in the AI component to that the AI lives over all of that and can allow a complete sweep of that information, basically almost like a holistic approach to that information and be able to use pieces from all the systems in order to enrich each one of the other platforms. So it's, it's really, you know, this continuous pattern of flow, it goes back between the different modules or the different disciplines and allows them to all basically use that same information, be on the same page with all the other groups and not have any disparities in how one group may view the customer versus another because they're using identical information.
Natalie Silverman:Thanks, Don for your insights into the future of finance. For Sidetrade, this is Natalie Silverman.
Conclusion:This has been another episode of Finance to Futurist, a Sidetrade podcast series. Make sure you catch every episode by subscribing to our podcast on Sidetrade.com or through your podcast platform of choice. Thanks so much for tuning in. This podcast is brought to you by Sidetrade, and is for general information purposes only. All rights reserved.