The environment of Credit to Cash (C2C) has evolved and is accelerated as a platform towards achieving continuous automation and deeper analytical insights through innovation. Moving from basic blocking and tackling to a highly automated, analytics-driven platform brings considerable efficiency and accuracy to the enterprise. The most advanced and well-funded enterprise has led the charge with a model of highly effective technology applications inclusive of Robotic Process Automation (RPA) and cognitive Artificial Intelligence (AI) learning, but the access to these highly intuitive and productive solutions is quickly coming into reach for all. Maximizing productivity, quality of performance and driving greater value persists. The realization of disruptive technologies brings the few highly skilled domain experts to an automated and proactive credit and collections delivery, which transforms and optimizes C2C.
The use of effective platform automation has been proven to reduce manual processes by an average of 30%, lowering overall cost of credit to cash by 35% when coupled with a Business Process Outsourcing (BPO) delivery. If you have not yet moved away from legacy practices or improved your technology platform, this is no longer a novel way of attacking productivity but a competitive must have. Most enterprises have attacked this segment of their business either as a working capital initiative or as a finance and accounting transformation, yet the question remains – How do you achieve the next level of automation to win efficiency and effectiveness in a competitive global business environment?
Let’s start with some of the options: With continuous improvement objectives, collection platforms need to increase efficiency and lower total costs of ownership. The challenge is – Which model is right for your enterprise: ERP Tools, Domain Specific Application (the wrapper) or obtaining the advantage through outsourcing?
• Build Model: In-house IT builds and manages for long-term return on investment
• ERP: Add-on modules and boxed credit and collection software applications
• Wrapper: Purchase a credit and collection tool and customization from a software vendor
• Outsource: Use the credit and collection platform and processes provided by an outsourcing vendor
The in-house option which exists has mostly been discarded except for very specific in-house applications. The build model, while we have seen some excellent results of such builds, most outsourcing relationships deliver these innovations both in technology, as well as with a complete end to end set of integrated processes. The question in this aspect is how much it ultimately costs to start and build from ground zero vs.adopting a mature and experienced implementation solution.
Let’s narrow our focus to understanding the three options of ERP add-on, Wrapper, and Outsource. Assuming you have already taken one of these steps as the enterprise continues to obtain best in class metrics, what would be your choice for next level efficiency?
ERP: Today most enterprise clients complete a major installation of ERP which fit some parts of the business but doesn’t necessarily fit the focus of credit to cash. In satisfying the needs of finance, accounting, compliance and regulatory, some gaps are naturally going to exist. While ERP can’t be all things to all people, module add-ons have been the solutions. With this comes a high price of customization, licensing and support. You still may be in a world which is highly dependent upon the platform to release automation functionality or do some in-house tinkering to find a solution. This is also the least cost-effective manner of reaching your ROI targets.
Wrapper: Readily available solutions and a market which has been created due to the lack of focus on the ERP environment. These tools feed off data in the ERP and augment the lack of solutions as a wrap around the ERP. Due to an industry and process specific solution, the chances with a Wrapper are greatly improved vs.the ERP model.
Outsourcing: The outsource solution makes the most sense in various ways because the providers need to drive value for your business. Tools, technology and domain expertise which has been proven under a multitenant environment accelerates transformation and aligns with the outcomes the enterprise is seeking. The evolution of Order to Cash BPO has quietly separated from the large Finance and Accounting (F&A) BPO engagements and taken on a very pragmatic transition to domain expert providers for these reasons.
Businesses will continue to look for improved cycle times to achieve cash flow or find ways to create flexible risk mitigation strategies. So, where do you obtain next generation efficiencies and gain RPA today? Have you seen this automation in action? Most might answer no, yet that doesn’t mean that the technology doesn’t exist. Auto cash applications, deduction matching, auto credit line algorithms and intelligent workflow exist and are alive and well in more places than you think. These are the basics given the scope of order to cash, so what really accelerates the next level of automation?
Taking into consideration that an important aspect of successful credit and collections practices centers on customer contact, I believe building relationships is a key part of a credit and collections strategy. Utilizing or automating with robo dialers to resolve deduction issues does not necessarily result in resolutions nor is a recommended practice to improve the customer experience. But what happens when you truly understand the behaviors of your customers and can predict their next move? A great example of this comes from enabling call analytics and AI to analyze the customers most frequent behaviors. If communication is being made by phone, email, mail, remittance and other, AI ingests this information and can take proactive steps to furnish the customer with resolution focused efforts before they ask. Simple tasks such as supplying statements and invoices or resolving cash application matching can be set off in triggers just by knowing the customer’s behavior. Repeated calls, requests, and documentation gaps are now solved in a proactive and complete manner, closing the gap and better satisfying the customer experience.
Taking the proactive approach, the capabilities deployed around customer risk management go even further. With the ability to search, locate and aggregate mass amounts of data, predictive credit analytics really comes to life. For small and large businesses, credit analysis was once an area of pulling credit reports, 10K’s, and phone call references. Today with completely automated modeling and deeper insights available to review your total credit exposure, sales may have a better tool at their disposal to maximize sales and outcomes. More importantly, without labor, the automated and insightful nature of RPA brings a more robust set of forecasting and customer targeted strategies to the credit and collection manager. CFO’s embrace this model as highly effective and a means of continuous cash forecasting against potential defaults. Seeing the behaviors of a customer who may possibly default goes from months of monitoring to minutes based on wider trends analysis.
An exciting era of advanced automation in this segment of finance is evolving, as the push for continuous improvement persists and is demanded by both the buyer and supplier. Continuous improvement is the mechanism which drives innovation beyond the immediate challenge towards long perpetual shifts in how we do business. Advanced learning applications make this process quick to the eye and accelerate the reaction time, in some cases saving the credit grantor from serious defaults in both stable and fluctuating markets.
The digitalization of processes in C2C such as with credit applications, invoicing, connecting payments and mass data referencing for reconciliation are no longer fragmented in the enterprise today. Tomorrow, such technical and cultural shifts in business processes will be the standard, allowing for the next generation of automation to emerge and bridging the gap in effective RPA and AI transformation.