CFOs rightly refer to cash as the oxygen for business. However, despite leveraging various working capital improvement initiatives, organizations continue to rely on working capital loans According to a survey report by Hackett, debt as a percentage of revenue has increased steadily over the last few years from 35% of revenue for the period 2008-2013 to 51% today, a 10-year high.
Where is this cash stuck? Mostly, in receivables as DSO increased by 4%. For the fourth year in a row, DSO degraded as organizations pushed payment term extensions on their supply base.
Finance leaders worldwide have to therefore pay more attention to credit and collections to define better credit policies and drive teams towards proactive collections to improve working capital.
This e-book has distilled insights from our study of credit and A/R transformation projects at 300+ organizations on how Fortune 1000 companies and SMBs reduced DSO by 15%-20% by implementing these eleven strategies.
Broadly, the strategies are categorized into the following buckets:
- How to reduce the credit risk of delinquent accounts?
- How to build a more proactive collections process?
- How to eliminate routine transactional tasks in credit and collections?
- How to improve overall process efficiencies across A/R functions to refocus resources on credit and collections?
Customer Onboarding and Credit Approval Process
Customer onboarding is the first and one of the most important steps as wrongly estimated/arbitrary credit limits lead to bad-debt write-offs and high DSO. Most organizations ignore this step and take the word of sales representatives or customers to decide on credit limits.
1.1. The Credit Application Process
According to a survey by NACM 33% organizations are pressed for time to perform due diligence on prospective customers. This is because companies typically assign credit limits based on credit agency data, financials, credit insurance, and bank guarantees. All of the manual processes, including paper-based credit applications, gathering data from third-party agencies such as D&B and Experian, and credit reviews, make it increasingly difficult for credit analysts to find time and onboard new customers.
Figure 1: Current Credit Application Process
1.2. Top Challenges in the Credit Application Process
The top bottlenecks for organizations in the credit application process are:
- The volume of paper that they need to process and maintain each month
- Incomplete and inaccurate financial documents that they receive from customers
- Manual data entry into spreadsheets and ERP systems leading to errors and wasted time
- Downloading credit reports from leading third-party agencies including Experian and D&B
- Calculating credit scores in excel spreadsheets
- Obtaining approvals on emails
Figure 2: The Online Credit Application Process
Moving credit applications online, integrating them with credit agencies, and securing them with digital signature technology helps eliminate manual work and incomplete credit applications. An online application replaces a paper intensive credit management process with an electronic one to enable better credit portfolio and risk management and to quickly onboard new customers. Credit score and credit limits are calculated automatically while workflows are assigned to relevant credit managers to review and approve the assigned credit limits.
Figure 3: Credit Review and Customer Onboarding Process
Assigning the right credit limit to customers helps firms to collect payments on time, reducing DSO.
The adidas Group has about 10 different credit application formats which are filled by customers for requesting lines of credit from its various business divisions around the world. In what was a very manual paper-based process, the credit team at adidas took more than four days to process a single credit application. In essence, what this meant was that the credit team was mostly focused on tactical activities while trying to get a complete credit application rather than spending it strategically on the credit review process.
The adidas group started deploying credit applications one by one at each of their business units. After deploying the HighRadius online credit application, the team was able to bring the new customer onboarding time to less than 2 days with more than 90% credit applications received with complete data on the first attempt. The end result was that the team was able to review more credit applications per given while using accurate data to reduce the credit risk exposure and alleviate the risk of high DSO due to wrongly estimated credit limits.
Dynamic Scoring for Credit Review
In the previous section, we discussed how inappropriate assignment of credit limits to new customers could lead to higher bad-debt and DSO. This section is about problems associated with the credit review process for existing customers.
Credit analysts are assigned hundreds to thousands of accounts for reviewing credit limits, onboarding new customers, and unblocking blocked orders. Since reducing credit risk exposure is the responsibility of the credit team, credit review processes need to be airtight. However, the sheer volume of accounts makes it impossible for credit teams to successfully cover all accounts which are due for periodic reviews. Constantly chasing the backlog of accounts means that credit analysts are often unable to focus on conducting a focused and accurate credit analysis.
Only after an objective analysis can a credit manager make an informed decision about extending credit or continuing the existing credit to the customer. While inconsistency in periodically reviewing existing customer credit lines is an issue, asynchronous events such as financial results, rating downgrades, and M&A activity could also be responsible for escalating credit risk. Figure 4 illustrates challenges related to both asynchronous events and internal SLA requirements that create a need for credit analysts to manually prioritize accounts for credit review.
Figure 4: Challenges with a Manual Credit Analyst Driven Process
This is where best-in-class companies leverage artificial intelligence to remove subjectivity from the process and help the analysts prioritize their worklist and simplify the review by suggesting credit scores based on several internal factors (including open A/R amount, aging details, ADD, dispute amount among others) and external factors (including dip in stock exchange, shift in credit rating, seasonal spikes, M&As among others)
Based on these factors, the system calculates a safe credit limit for the account and suggests it to the analyst along with all the documents used for research. Hence, empowering the analysts to make more informed decisions.
At the same time, it also automates credit review for all the small customers while triggering a credit review for customers accounts with troubled financials or credit ratings.
Figure 5:Internal and External Factors Used by Artificially Intelligent System
Figure 5 illustrates what all factors are considered the dynamic credit review of a customer account.
2.3. Credit Management Success Story: TechData
Tech Data Corporation, currently ranked #108 in Fortune 500, is one of the world’s largest wholesale distributors of technology products. The credit team at Tech Data dealt with legacy systems and manual processes for credit reviews. With a large portfolio of customers worldwide, the team had to log in daily to multiple portals for each region to fetch data from credit agencies.
The team automated credit data aggregation and workflows to achieve a productivity boost of 120% in the credit review process and also saved $160,000 annually through improved efficiency.
Improving the Invoicing and Payment Process
Studies have shown that companies are able to reduce DSO by three to ten days with electronic invoicing. By adopting electronic invoicing, credit departments are able to reduce dependence on print and mail for timely delivery of invoices while also reducing costs.
The top challenges associated with the paper-based invoicing process are listed below:
- Delays in sending and receiving invoices
- Inability to track delivery
- High cost
- Non-standardized customer experience
The top challenges associated with accepting electronic payments are:
- Expensive procedures for complying with PCI-DSS guidelines
- Extra work for internal teams to apply cash as remittance comes in from a variety of sources including EDI, website, and email
- Expensive to accept credit card payments because of the transaction costs
- Disconnected invoicing and payments systems that cause repetitive work for collections analysts to retrieve invoices and accept payments
Creating a self-service portal for both invoicing and payments is the first step. This removes the waste in the collections process where roughly 20% of collector’s time is wasted re-sending invoices to customers. This is enabled with a centralized online repository of invoices that could be accessed anytime by customers.
Top features that organizations leverage in these portals are:
- Self-service portal where customers can retrieve their invoices
- Ability to make payments through multiple options including ACH and credit cards
- PCI-DSS compliance for accepting credit-card payments by enabling processor tokenization
- Lower card payment acceptance costs by leveraging Level III Interchange fees
- Tight linking into the A/R module to enable customers to create deductions right from the EIPP portal
- Ability to send/receive invoices by fax
- Automated print and mail service to customers who still demand paper-based invoices
Reckitt Benckiser(RB), a leading consumer goods manufacturer, had an expensive paper-based process for invoicing as SMBs depended on paper-based invoices while large customers were already using EDI for invoicing. By moving to an EIPP portal, RB was able to provide SMBs with lower cost e-invoicing options including e-mail and portal-hosted invoices to reduce the electronic invoicing cost by 98%.
Yaskawa America, Inc., an American manufacturer of HVAC products, saved $12,000 on credit card payments with a PCI-DSS compliant credit card processing solution by leveraging Level III data in the ERP for maintenance and repair orders.
Deciding Who to Contact on Any Given Day
On any given day, collections analysts spend up to 30% deciding on who to contact. Studies state that 30% of collections analysts’ time is spent prioritizing and preparing for calls.
At the outset, the collectors relied on their intuition, skill, and experience to skim through the worklist in to order to prioritize accounts and reach out to the customers. As collections management has developed into a reactive process, the collectors use static parameters such as due-date, invoice value, and current customer segment to tweak their collections strategies. With the numerous factors involved in customer’s A/P functions, these factors are grossly insufficient to deal with dynamic changes.
Most companies assign hundreds or thousands of accounts per collections analyst depending on the size of the company.
Figure 6: 16 Variables that Impact Collections
Figure 6 lists some of the most common attributes including dunning data, credit risk, and number of promises to pay that effect whether a collections analyst should contact a customer on any given day. Companies generally use average days delinquent(ADD) into account while prioritizing the worklist.
But, is it really the best way to go about it?
The subsequent figure illustrates payment date prediction using ADD as a metric for a small-to-medium business (SMB) customer where the A/P team runs its cycle in the middle or end of the month and the payment terms are 30 days. This is just one potential pattern with one customer where the A/P cycle plays a vital factor in payment.
Figure 7: Customer Payment Behavior According to ADD
In the above scenario, the invoicing date is February 20th, and the due date is March 22nd according to the 30-days payment term. The ADD value of 7 indicates that on an average the customer delays the payment by 7 days after the due date. Based on this estimation, the predicted payment date is March 29th. However, the A/P cycle schedule has not been considered in this assessment.
Figure 8: Predicted Delay vs. Actual Delay
The dynamics of real-world overshadow this prediction. The A/P cycle of the customer runs on March 15th, i.e. mid-month when the payment terms are still valid and the payment is not due, and therefore, the A/P team skips initiating the A/P process for this invoice. The next A/P cycle runs on March 31st, when the invoice is already past-due. This is when the A/P team begins the invoice approval and payment process, and the actual payment comes in on April 3rd. As a result, the actual delay is 12 days as compared to the 7 days prediction delay.
In another scenario, the invoicing date is February 12th, and the due date is March 14th, while the predicted payment date, based on ADD on 7 days, is March 21st.
Figure 9: Predicted Delay vs. Actual Delay
In this case, the A/P cycle runs on March 15th, one day after the due date, and approves the payment for the invoice. As a result, the actual payment is made on March 18th with a delay of 4 days, as compared to the predicted payment date of March 21st with a delay of 7 days.
The above scenarios clearly demonstrate that ADD as a metric is not sufficient to predict payment date and needs other customer-specific factors such as A/P cycle schedule to predict the payment accurately. Moreover, the above represents an instance of a single customer. With the gigantic number of customers with whom the collectors deals, a nearly infinite number of factors come into play. It is not practically feasible for collections teams to identify each influencing factor and corresponding pattern for each customer to tweak collections strategies.
Machine learning powered Artificial Intelligence being the hottest trend today, is incredibly powerful for predictions or calculated suggestions based on large amounts of data. In today’s aggressive economy, it is time for collections teams to recognize the necessity and worth of a proactive collections management process. This will only be achieved with collections strategies and correspondence game plans that resonate well with dynamic variables in the collections process. This chapter explains how AI/ML enabled collections operates on dynamic factors.
Figure 10: A list of key factors considered by machine learning algorithms
The question is how does machine learning powered AI works?
It takes training data or values of existing invoices to train the training model along with the actual results to compare the AI’s results against. So, for example in collections, AI can predict customer payment dates with up to 90+% accuracy. How does it reach that level of accuracy? It starts with training the models for the invoices that have already been paid for. It aggregates all the invoice attributes, accounts history among other things are part of the training data. The training models for the algorithm use this data to make predictions and ultimately, they are compared against the actual payment dates of the customer that has already happened. Based on the difference between actual payment date and AI’s prediction date helps the model to self-learn and improve.
Proactive Collections Management not only helps analysts de-clutter their collections worklists by prioritizing the right accounts but also enables greater output with less cost by optimizing collections efforts.
Due to manual prioritization of worklists and mounting volume of receivables, ShurTech faced numerous challenges in terms of broken customer satisfaction, lack of visibility and execution for collections. However, by leveraging the artificial intelligence to understand customer behavior, ShurTech was able to reduce the overall past-due A/R by 50% and 31+ days past due A/R by 75%.
Collections Correspondence Strategies
A study by McKinsey found that 70% of collections calls go to customers who would have paid anyway. This is yet another place to optimize the process to increase the efficiency of customer contact and improve DSO.
Figure 11: The Biggest Challenge with the Collections Process – Too Many Assigned Accounts
The biggest challenge continues to be the account coverage and the limitation in the number of collections calls that could be made on any given day. Agents are assigned too many accounts at a time, and the preferred method of communication is phone with very limited time spent on other mediums such as email or physical letters. Collections analysts focus on invoice value and aging data and make calls while ignoring a large number of assigned accounts; there are always accounts from the SMBs that makeup to 80% of the collections worklist but fall through the cracks since analysts tend to focus on high invoice value accounts. It is also impractical for collections analysts to call these customers for small amounts.
Figure 12: Automating Correspondence to Let Collectors Focus on Calling Only the Most Critical Accounts
As Figure 12 illustrates, Robotic Process Automation (RPA) systems have to be designed so that the collections analysts call only the most critical accounts. This automation is an extension of the previous section where we talked about how to let the system handle credit policies and score customers on the risk that they pose for the organization.
Based on each account score, the system will be able to tell the analyst what the next step should be. For example, if habitual late payers belong to a customer segment, the system should suggest that collections analyst to send out email correspondence 15 days before the invoice is past-due. The system should also have ready-made email templates so that the analyst is able to send mass correspondence to all customers in that segment with the click of a button. Taking correspondence automation further, the system should be able to automatically send proactive reminders for all accounts depending on the credit policies.
While email correspondence automation takes care of account coverage and brings the money to the bank through proactive reminders, collections analysts continue focusing on high priority large accounts. This optimizes the collections process and reduces DSO.
Yaskawa America, the leading manufacturer of HVAC equipment, adopted automated collections correspondence and was able to contact more customers. The process improved overall to help Yaskawa in reducing DSO by 5.5 days and achieve a Collections Effectiveness Index of 90%.
Doubling Down on Collections by Eliminating Waste from the Cash-Application Process
Cash application is one of the more resource intensive processes in accounts receivable, and organizations are better off automating cash application and shifting resources to higher value activities including collections and credit. The other disadvantage of a slow cash application process is that it is an upstream process that further slows down the deductions and collections processes. As an example, if a customer has already paid for an invoice but the status is not updated as closed in the ERP, collections analysts will waste time by calling a customer who has already paid.
Organizations today receive payments through checks as well as electronic formats including ACH, real-time payments, and credit cards. Reconciling cash for checks and e-payments pose different challenges.
Figure 7: Challenges with Processing Check Payments
For checks, A/R teams typically receive a bank lockbox file that contains the payment details, however, ERP systems need reconfiguration to be able to read the lockbox file and apply payments. This is a recurring problem whenever organizations either add a new lockbox service or switch banks. Also, lockbox services tend to be quite expensive to maintain as banks also charge significant key-in fees for capturing remittance data. Nevertheless, this keyed-in data is usually insufficient for successfully posting the payments. More than 80% of the cash posting exceptions resolved by analysts are repetitive in nature. This is a result of customers repeatedly sending remittance information with the same inconsistencies or errors
The cash application analysts still have to resolve exceptions due to incomplete remittance data or code the deductions while identifying short payments. All these steps have been illustrated in figure 10.
Figure 14: Challenges with Cash Application for e-Payments
Coming to electronic payments, while there is significant adoption of various formats, the cash application team is burdened with the cash reconciliation process as the remittance details are sent separately through email, EDI, or customer portals and websites.
Though the payment itself is electronic and fast, processing the payments is highly manual and time-consuming. The analysts collect remittance from different sources, associate them with incoming payments, link them to corresponding open invoices, check for short payments and discounts, and then post the cash in ERP which takes a huge amount of time and effort. All of these steps have been illustrated in the above figure.
As the challenges have highlighted, cost and resource wastage are problems that have to be addressed on the cash application front.
Figure 15: Automated Cash Application Process
By leveraging machine learning, the system is able to learn how different types of exceptions are being handled by analysts and identify the pattern of corrective actions for resolution. The engine is then able to replicate similar action using this intelligence, after undergoing thorough checks and verification for the proposed correction.
Figure 15 talks about how each of the steps – aggregating remittance documents, coding deductions and applying cash could be automated using an AI-based cash application system.
Danone North America’s shared service center had a complicated cash application process with four independent business units.
After leveraging an artificial intelligence-powered cash application automation solution, Danone was able to reduce the time spent on cash application by 75% and deployed the remaining FTEs to other A/R functions.
Providing Research Ready Deductions
Deductions volumes have been growing over the years, making it more challenging for companies to handle the work with existing resources. A Credit Research Foundation survey observed that more than two-third of polled credit managers did not see any decline in the volume of deductions over the last 12 months.
Automating deductions identification, research and collaboration is yet another low hanging fruit to refocus resources from deductions operations to higher value operations including credit and collections.
Figure 8: Manual Steps in Resolving Deductions
Typically, a deduction proceeds through a set of stages for resolution handled manually by the analysts. Some of the major challenges they face while going through these manual steps include:
- Cross-department collaboration is one of the most complex issues. Although deductions are a company-wide problem, the major responsibility to resolve a deduction remains with the credit and A/R department. This creates a complex scenario because the investigation responsibility is with the A/R team while the investigation execution may include one or more different teams. To solve this contention, companies have been experimenting with increased accountability for different teams. Cross-functional teams and vendor compliance groups have been deployed by many large firms to improve communication, both internal and external, and resolve deductions.
- Access to information: Deduction research has to be backed by documentation. But collecting all the documents and keeping them in order incurs significant costs. The rapid retrieval and association of backup documentation in a timely manner is a game-changer for deductions.
- Lack of resources: Data from a deductions survey by the CRF suggests that while 30% of companies saw an increase in deductions, only 17% of the companies deployed additional resources to handle the uptick in deductions.
Deductions analysts spend significant amount of time downloading and aggregating back up documents and collaborating with different stakeholders while research and resolution.
This is where the top companies leverage technology to auto-aggregate all claims and PODs while providing structured workflow for the analysts to research, catalog and amass all their collaboration at a single source of truth.
Figure 9: Two Steps to Automate the Deductions Process
Automating the deductions back up aggregation for claims and proof of delivery and attaching these to open deductions could significantly reduce the time take in backup retrieval and research. With this, the deductions become reasearch ready for analysts.
Going one step further and providing a structured workflow platform for analysts to take care of all internal and external collaboration enables smooth and fast resolution. Faster deductions resolutions in turn result in faster collections, thereby lowering DSO.
Land O’Lakes, one of America’s premier agribusiness and food companies, received more than 100,000 deductions per year with 50% being contributed by the top 12 large customers. Handling all these deductions manually required huge commitments from the analysts in terms of time and effort. With automation, Land O’Lakes was able to increase ~$2 Million in closed deductions (dollars) while reducing the Deduction Days Outstanding (DDO) by 23%.
Shifting Focus to Invalid Deductions
According to the study by Attain Consulting Group, about 90% of deductions are valid, and about 60-80% of the valid deductions are related to trade promotions. Whether the deductions are valid or invalid, analysts spend the same amount of time in resolving deductions.
Deduction Management teams and analysts process hundreds of thousands of deductions every year. However, even if a deduction is valid, it still requires a set of manual, time-consuming tasks to be executed before an analyst is able to determine its validity. With more than half of all deductions being valid, this means that credit and A/R teams lose productivity that could have been spent on resolving and collecting on invalid deductions.
Hence, there is value in weeding out invalid deductions and adding dollars back to the bottom-line.
Figure 16: The Deductions Paradox: Inability to prioritize deductions
As Figure 16 highlights that a high dollar value invalid deduction should be the highest priority for the deduction’s analyst whereas a low dollar value valid deduction should be at least priority. However, the analysts do not know which deduction is valid or invalid and hence, work on all the deductions. In other words, you do not really know whether a deduction is worth researching unless and until you have already worked on it.
As a result of this, the analysts end up:
- Wasting time on verifying valid deductions
- Having very low recovery-rate due to lack of time to pursue invalid deductions
- Writing-off deductions that could have been invalid
Figure 17: Two Steps to Automate the Deductions Process
In order to reduce the time-crunch on deduction resolution, the AI enabled system predicts the validity of a deduction by analyzing several factors like payment history, customer account etc. and then compairing against the characteristics of the new deduction in question. With these predictions, 70% of the deductions worklist is automatically resolved.
For the remaining 30% deductions, the system provides recommendations. These recommendations are based on certain confidence categories. For example, it might predict for a certain deduction that it is 80% Invalid. This means that there is an 80% probability of this particular deduction being invalid. Similarly, it can predict 65% Invalid or 50% Invalid. On the basis of these recommendations, deductions worklist gets re-prioritized to work on the highest probable invalid deductions first.
Keurig Dr Pepper, the beverage manufacturer, had a high volume of deductions before leveraging automation. By leveraging automation to reduce the deductions workload and focus on root cause analysis, Keurig Dr Pepper was able to recover an additional $1.4 Million in invalid deductions and also reduced the volume of deductions by 13%.
Leveraging a Connected Platform for All of Credit-to-Cash
As the previous sections show, there could be significant gains from optimizing each of the individual processes in the credit-to-cash cycle. However, credit and accounts receivable teams could unlock gains in multiples if they start breaking departmental silos and work as an engine.
Starting with the credit team, blocked orders directly affect the top-line of any company. Since the credit and collections teams work in silos, accounts that near credit utilization might be missed by collections and have new orders blocked. Sometimes, the problem is only that the customer is not aware of the existing credit limit.
Similarly, a lack of clarity on deductions means that collections team either call customers with open deductions and get nowhere or ignore resolved deductions which are low hanging fruit for collections.
Figure 2010: Blocked Order Process
The figure above outlines all the different people who need to coordinate together to resolve a blocked order. The end result is that the blocked order process is always a reactive process and, along with delaying revenue realization, it is also responsible for compliance problems in credit policies.
Figure 21: A Streamlined Blocked Order Process Supported by Integrated Systems and Artificial Intelligence
Leading organizations are strengthening process collaboration by leveraging integrated technology. These systems are able to read patterns based on past sales orders and trigger a workflow for potential blocked orders to either send automated correspondence or for the credit analyst to notify the customer about delivery blocks. The customer is then able to directly view the invoice in a buyer-supplier collaboration portal and pay through the same. The credit limit is then automatically freed, and orders don’t get blocked.
Collaboration with Buyer A/P teams
One of the biggest challenges in the credit-to-cash cycle is supplier-buyer collaboration for processes across credit, billing, collections, payment processing, and dispute resolution. Since teams collaborate manually over email, it proves to be a huge challenge to maintain a system of record especially on past communication and patterns of problems.
A/R and A/P collaboration is very important right from the credit application process where the credit terms are set to the collections process where the A/P team pays invoices and reaches a settlement on deductions.
Figure 22: Manual Process in The Invoice to Cash Process
Figure 22 outlines the number of manual steps from the invoicing process to the payment process. An error in even one of the steps will result in a late payment and increase in DSO.
Figure 23: Enabling Digital Collaboration Between Buyers and Suppliers
As Figure 23 outlines, establishing a digital collaboration portal between buyers and suppliers is the first step that organizations have undertaken. The platform is able to link the supplier ERP with the buyer ERP so that with a single click the supplier is able to post invoices directly into the buyer’s ERP.
Figure 24: Condensing a 10 step process to a 2 step process by leveraging a buyer-supplier network
Figure 24 outlines what the new process will be. A billing analyst generates an invoice for a product/service and posts it on to the network which in turn posts it directly on to the ERP of the buyer. This results in faster invoice delivery, seamless payment from the accounting system and straight through cash posting as the payment happens through the portal.
Accounts Receivable on Autopilot – Autonomous Receivables
Till now, you have only seen or read about piece meal automation that solves a problem for you as a quick fix. What if in addition to moving away from siloed processes to an integrated receivables platform for all A/R process, you leverage the benefits of digital assistants and artificial intelligence powered solutions to strategically contribute to your company’s growth.
Sounds too good to be true?
Here’s a thought – one of the AI use cases we discussed before was on how AI could help you predict customer behavior and probable payment date so you could start the collections process strategically.
What if there was a digital assistant who could:
- Prioritize your worklist based on the AI predictions for payment date
- Categorize your worklist into different buckets in terms of activity (call, correspondence among others)
- Centralize all the information related to an account at one place
- Standby and be ready to take notes when you are on a call with any customer
- Capture any payment commitments promised on the call- analyst just needs to save
- Summarize the call and send it to customer post analysts verification.
Again, sounds unrealistic? It’s not.
Here’s what Scott Phillips, Head of Shared Services, Kloeckner Metals Corporation had to say about using the call-in functions for day to day collections:
AI and technology usually sound too good to be true. But once we made the first set of collections calls using the HighRadius software, we knew that this could be something big. Automation aside, I think Autonomous Collections will allow collectors to really focus on customers who matter. As a manager, the auto-archiving of conversation history is a game-changer for internal training and feedback. We are excited to upgrade to the world’s first touch- and voice-enabled experience for collections and look forward to more use-cases for Autonomous Receivables across other order to cash functions.
Combining A/R Analysts with AI and digital assistants could laser-focus teams on strategic work with higher corporate impact. While the AI digital assistant will perform the clerical tasks and provide deep analytical insights, the A/R analyst could focus on making context-sensitive judgement to drive better outcomes.
This is what we call Autonomous Receivables – A/R on Autopilot
It is a new way of thinking about order to cash software that reduces the amount of clerical work done by the user, and puts them in a position where they are applying their contextual decision making for course correction.
To summarize, all of these strategies fall into the following three buckets:
- Fixing the Root Causes of High DSO: High DSO could either be because the customer in question is not creditworthy or simply because the customer was not contacted on time. Addressing the customer onboarding and credit review processes will take care of the former, and addressing the collections bottlenecks will take care of the latter.
- Eliminating Waste in A/R Operations: Sometimes, even after all of the optimizations within collections, organizations don’t have enough resources for collections. Addressing waste in resource-intensive processes including cash application and deductions will give organizations enough leeway to increase FTEs working on collections while flatlining the A/R team headcount as a whole.
- Breaking Silos in Operations and Facilitating Collaboration: Collections as a process involves communicating with internal and external stakeholders. Addressing how information flows within the A/R team, for example, from the cash application team to the collections team, and collaboration between buyers and suppliers will further unlock opportunities to reduce DSO and improve accounts receivable turnover.
As the success stories show, organizations of all sizes from Fortune 1000 companies including Keurig Dr Pepper and Danone and SMBs including Ivanti and ShurTech brands have been able to deploy these eleven strategies and reduce DSO by 10-20%.
HighRadius is a Fintech enterprise Software-as-a-Service (SaaS) company. The HighRadiusTM Integrated Receivables platform reduces cycle times in the order-to-cash process through automation of receivables and payments across credit, electronic billing and payment processing, cash application, deductions and collections.
Powered by the RivanaTM Artificial Intelligence Engine and FreedaTM Virtual Assistant for order-to-cash teams, HighRadius enables organizations to leverage machine learning to predict future outcomes and automate routine labor-intensive tasks. The radiusOneTM B2B payment network allows suppliers to digitally connect with buyers, closing the loop from supplier receivable processes to buyer payable processes.
HighRadius solutions have a proven track record of optimizing cash flow, reducing days sales
outstanding (DSO) and bad debt, and increasing operational efficiency so that companies may
achieve strong ROI in just a few months. To learn more, please visit www.highradius.com
HighRadius’ Integrated Receivables Platform
Integrated Receivables is a solution to optimize accounts receivable operations by integrating all receivable and payment modules to work as a unified business process. At the core of the Integrated Receivables platform are solutions for credit, collections, deductions, cash application, electronic billing and payment processing – covering the entire gamut from order-to-cash. The HighRadiusTM Integrated Receivables platform is a stand-out as it enables every credit and A/R operation to execute real-time from a unified platform with an end goal of lower DSO, reduced bad-debt, faster dispute resolution and improved efficiency, accuracy for cash application, billing and payment processing.
HighRadiusTM Integrated Receivables leverages RivanaTM Artificial Intelligence for Accounts Receivable to convert receivables faster and more effectively using machine learning for accurate decision making across credit and receivable processes. The Integrated Receivables platform also enables suppliers to digitally connect with buyers via the radiusOneTM network, closing the loop from the supplier A/R process to the buyer A/P process.