Order to Cash (O2C) - Account Receivables Process Hyper-automation using AI, ML and RPA

1.0??Preliminaries

While accounts receivable (AR) is typically the largest asset on most businesses financial statements, opportunities exist to manage it more effectively and generate higher profits. The consequences of ‘getting it wrong’ are growing – poor management of invoice to cash collection processes leads to overdue invoices piling up, which, in turn, leads to cash flow problems. As control over operating cash reduces, businesses need to rely more and more on expensive bank credit. When credit is available this challenge can be managed but today the access to bank credit cannot be taken for granted. And let’s not forget that the time taken to turn AR into cash (Days Sales Outstanding or DSO) is directly linked to the financial health of the business.

Companies have long sought to automate financial operations. In 2019, Gartner coined a new term—hyper-automation—to describe what was emerging to deliver on technology’s promise. Hyper-automation can deliver operational excellence and organizational resilience for accounts receivable. Most AR leaders are well-acquainted with technology that enables automation and reallocates their team’s bandwidth to more high-impact jobs like evaluating critical accounts and making data-backed decisions. Such technology includes Robotic Process Automation (RPA) and Artificial Intelligence (AI). Natural Language Processing (NLP) is a branch of AI that helps people to interact with machines when using natural human language. When it comes to receivables management, AI engines can tackle vast amounts of manual processes that overwhelm employees, including processes around cash applications, credit management, collections management, and dispute management. And there’s still plenty of room for new ecosystems of innovation, including chatbots and digital assistants that can offer customized customer management and AI systems to reduce fraud.

2.0??AR Business Practices

A business must follow the steps mentioned below to optimise the collection of accounts receivables:

2.1????Establishment of credit policies

The first step involves devising policies and procedures to be followed by the persons involved in collections. This sets a base for the sales and accounts receivable department on invoicing and collections. Businesses must invest time into this step since it can improve the efficiency of the forthcoming processes. The tasks that this step covers include: Mechanisms to determine credit worthiness of the customers, setting credit limits for different types of customers, Establishment of collection periods, Deciding on late payment penalties or early payment rebates.

2.2????Invoicing and documentation

Invoices are the main proofs of sales that act as a basis for the contract. Invoicing must be done in a way that provides clarity of transactional information to all parties. The business must be unique, detailed regarding the goods/services sold, and easily retrievable for future references. For the customers, it must specify credit terms, the value of goods/services sold, due dates for payments, and give options to different modes of payment. A quick invoicing process is a key to quick settlements. Hence, invoice creation and delivery processes must be accelerated. Businesses have recently adopted the e-invoicing system. Electronic invoices offer cost-efficient methods for invoicing since they offer convenience, quicker invoice generation, prompt delivery and invoice tracking.

2.3????Tracking and monitoring accounts receivables

Regardless of the size, every business must undertake this step of the accounts receivable process. Once a sale is executed, the accounts receivable department takes over and monitors the accounts receivable. The accounts receivable personnel must review every account regularly, depending on the credit period. This helps identify the probability of receipt of accounts receivables early. In case of any threats in realising any accounts, prompt corrective actions can be taken. Follow-ups are essential to the timely realisation of debts. Specialised tools are used to monitor accounts receivables. Ageing reports show records of invoices that are outstanding, along with the duration. This analytical tool is handy to identify slow-paying clients and prevent bad debts.

2.4????Receipt and accounting of receivables

Once payments are received, the accounts receivable department reconciles the receipt with the invoices and account balance. The department must, post-payment entries to the accounts, ascertain the remaining balances. The accounts receivable personnel will then take action regarding the balances.

3.0??Intelligent AR Technologies

Hyper-automation is the application of advanced technologies like robotic process automation (RPA), artificial intelligence (AI), and machine learning (ML) and process mining to automate processes in ways significantly more impactful than rudimentary automation capabilities. The list of key technologies for AR hyper-automation follow:

3.1????Robotic Process Automation

RPA mimics human action to handle formerly manual, repetitive tasks. It follows a sequence of steps to produce a desired outcome without human intervention. Applied to the order-to-cash process (O2C), RPA “bots” gather unstructured data from portals and websites or enter received data and post it in ERPs. Mundane daily administrative and repetitive tasks are getting eliminated by bots. RPA, DL and AI cognitive technologies can increase productivity on repetitive tasks, as mentioned:

·??????Data Gathering Bots – Bring information from credit?bureaus, shipping and logistics sites, vendor portals

·??????Invoice Posting Bots – Post invoices on various customer portals

·??????Document Data Capture – OCR and data extraction?from invoices, remittances, checks, bank statements?global advices, lockbox images

·??????Data Entry Bots – Posting data into internal and?external systems

3.2????Artificial Intelligence

AI exhibits human traits such as the ability to sense, reason, converse, read, learn, and solve problems. AI includes natural language processing (NLP), deep learning (DL) and computer vision (CV). For O2C, AI supports cognitive data capture and intelligent, conversational digital assistants. Virtual assistant (AI/NLP) can be used to augment and even automate many of these lower-level tasks — freeing up valuable financial organizational resources to focus on higher-level strategic tasks.

3.3????Machine Learning

ML is a branch of AI. It analyses data and automates analytical model building. Systems learn from user and data, identify patterns, and arrive at decisions with minimal human intervention. It is used in O2C - include predictive analytics, identifying customer payment behaviour, the customer’s propensity to default or dispute, assigning probable reason codes, and cash application matching. Recently, companies have turned to structured machine learning to speed up/streamline key reconciliation, collections, cash management, and deductions.

3.4????Business Process Automation

Business process automation, also known as digital transformation, streamlines sophisticated business functions. The primary objective is to bring integration and simplicity to operational workflows. AR vendors must work to embed intelligence within the order to cash workflows to unleash the full power of AI. Applications include collections strategy, dispute resolution workflow, and cash application matching rules. Automated processes combined with advanced analytics and an educated workforce lead to several benefits: Greater productivity, Instant and accurate insights, Greater compliance and reduced risk, Increased collaboration, Increased workforce capacity.

3.5????Application Programming Interfaces

Application programming interfaces (APIs). APIs allow developers and managers the opportunity to quickly add/modify data flows into and out of the software application.

3.6????Analytics

Data-driven processes can be set on autopilot using machine learning (ML) and computational AI technologies. Patterns are derived from data and insights from patterns can be used to drive the process in an automated and self-learning environment. Digital Order-to-Cash platforms can provide on analytical insights as follows:

·??????Descriptive – Insights on global portfolio information?across customers, business units, product lines etc

·??????Predictive – Derive customer payment behaviour, disputes propensity, cash flow forecasting

·??????Prescriptive – Provide suggested matches on cash?application, suggested next steps on collections strategy

·??????Embedded – Drive letters and correspondence based on analytics. Drive credit release/hold, cash posting?and dispute process

3.7????Virtual Assistance

More and more, finance operations managers are open to the integration of virtual assistants (VA) to perform searches of both internal and external data sources to provide the treasury managers with the necessary timely information. Virtual assistants will have their greatest impact by supporting lower-level tasking like bank reconciliations and other bank account management tasks. Potential Task for Virtual Assistants in AR:

·??????Organization – Virtual assistants can send a list of?accounts that have not responded to dunning request

·??????Research – Virtual assistant, following guidance, can research accounts on LinkedIn, new wire sites.

·??????Reporting– Virtual Assistants can create ageing reports at pre-determined time intervals

·??????Create and send collection emails - With the help of templates, a VA can create collection emails and send them to customers.

4.0??Intelligent AR Management Practices

Accounts Receivable is a good point to kick off automation in finance, as it’s less reliant on external documents, as opposed to Accounts Payable. Days Sales Outstanding (DSO) heavily depends on the human element on both the payee’s and the recipient’s side. If bots are adopted as digital workforce, they will issue and email invoices automatically. By automating this task, consistent cash flow will be achieved without deficiencies. Apart from eliminating cash gaps, RPA can help input information, sparing accountants from juggling multiple information systems. And, here is a list of possible accounts receivable tasks and processes that can benefit from intelligent automation:

4.1????Customer Master Data Management

Credit limits, payment terms, discounts, tax rates and return policies, and any other relevant terms (i.e. delivery address, e-mail address etc.) are assigned to specific customers, those terms must be accurately reflected in billing and collection systems. Customer master data should indicate what the customer is allowed to purchase, any dollar limits that apply, payment terms, whether they get volume discounts or advertising credits, and any other relevant terms.

·??????Multi-level parent/Child Hierarchy - Connect customers at multiple hierarchy levels

·??????Customer Master Roll up/down -Consolidate credit risk, AR, statements, invoices, disputes and payments

·??????Customer Master creation -Interface to ERP / MDM to create Customer Master

·??????Customer Master Updates - Update key customer credit risk attributes based on periodic and adhoc credit reviews.

4.2????Credit Management

Credit management helps companies determine credit limits for account receivables customers. The company uses a risk-based approach to decide whether or not to extend credit to an account receivable customer based on the likelihood of payment defaulting from that particular account receivable customer/debtor.

·??????Mobile Responsive Credit Applications - Mobile self-service forms with digital signatures?and digital reference checks

·??????Pre-packaged RPA Verification Bots - Hyper-efficiency with business validation, license?verifications, resale tax certificates

·??????Integrated Credit Bureaus Reports - DnB, Experian, CreditSafe, Credit Risk Monitor

·??????Credit Scoring and Auto Decisions Engine - Highly configurable scoring model and risk-based?decisions

·??????Delegation-of-Authority - Automate approval workflows

·??????100% credit checks – hold and release sales order from every ERP is captured?and evaluated

·??????Cognitive Order Processing - Assess risk on every order without causing any delays in hold and release process

·??????Sales Order Inquiry - Digital assistant for sales and credit reps for checking the status on the sales order

4.3????Collections Management

Collection management processes help companies contact customers’ stakeholders to pay overdue account receivables. The challenges faced in collection management include timely payment of account receivables, customer relationships, and legal issues.

·??????Invoicing and Payments - Shift from EIPP (Electronic Invoicing Presentment and Payment) to DIPP (Department of Industrial Policy and Promotion) and bots assisting posting of the invoices to customer portals

·??????Predictive Collections and Strategy Automation - Data-driven personalization in collections strategy and correspondence

·??????AI-Powered Digital Assistants - Powering Collection process with direct customer reach?and enabling payments, promises and dispute resolution

·??????Prioritized Task List - Strategy-based communication

·??????AR Portfolio Alerts and Third Party Integrations?Credit Risk Monitor, NCS – Liens and Bonds,?Bankruptcy Monitoring

4.4????Deductions Management

Deduction management is a process to identify account receivables that can be withheld (deducted) from invoices. The challenges with deduction management include classifying the deduction claims as valid or invalid.

·??????Auto Deductions Processing - Auto identify short pay, over pay with reason codes,?auto generate and resolve disputes through workflow

·??????Dispute Reason Codification - Best practices reason codes

·??????Automated workflow approvals - Routing based on dispute reason codes and workflow process. Audit trails.

·??????Rules-based bulk claims processing - Auto-approvals based on business decisions

·??????Digital Assistant to Resolve - Reminders and communication on resolution process

4.5????Cash Application

Cash application is a part of the accounts receivable process that applies incoming payments to the correct customer accounts and receivable invoices. In order to do this, the first step is to determine where to apply the payments. This is normally done by matching the payment to the associated invoices.

·??????Cognitive Remittance Data Capture - Eliminate manual lockbox, email remittance PDFs and bank statements payments document handling

·??????Banking Integrations - BAI2, MT940, EDI data formats. Integration with banks, Open banking APIs

·??????Global Payer/payee Relationship - Complex third-party payment relationships

·??????AI-driven Cash Application - Hundreds of rules and machine learning. Auto match invoice-to-receipts and posting automation

·??????Digital Assistant for Cash Application - Automated digitally assisted requests to customers to collect remittance information

4.6????Electronic Invoicing Presentment and Payment (EIPP) Portal

EIPP stands for Electronic Invoicing Presentment and Payment and is a type of software used by finance teams for accounts receivable management. With an EIPP, companies can automate the process of emailing invoices and account statements in bulk to their customers when it’s time for them to pay their bill. (That’s Electronic Invoicing Presentment part of EIPP.) EIPP software also includes an online payment portal that enables companies to accept payments on those invoices from their customers (hence, the Payment part of EIPP).

·??????Customer Portal EIPP - Onboarding new customers for all customer financial transactions

·??????Global Payment Gateway Integrations - Credit Card, ACH Debit, Wire Payments, Chase?Paymentech, Visa, Master, PayPal, CardX

·??????24X7 Digital Assistant - Digital assistant for customer account interactions – remittance,?payment, statements

·??????Account Statements - Automated digitally assisted requests to customers to collect?remittance information

·??????Split payments, Discounts capture - Straight through invoice-to-receipts matching and posting?automation

4.7????Reporting and Dashboards

·??????Global O2C Insights - CFO Dashboard, Controller Dashboard and All insights across all processes

·??????Multi-dimensional visual drill-down?- Analyse across business units, regions and?other dimensions

·??????Standard Dynamic Reporting - Tools for auto creation of report

·??????Customized Reporting - Reports with governance structure

5.0??AR Automation Use Cases

It’s time for a more streamlined and efficient process to enable collectors to handle other high-value duties like maintaining the billing system, creating reports, and investigating any irregularities. Accounts Receivable teams must have the tools to do their jobs efficiently. That’s where automation comes in. Automation, or in this case, “bots,” can simplify and streamline the collection process for Accounts Receivable teams. Utilizing innovative technology such as Natural Language Processing (NLP) and machine learning can create workflows that support efforts to maximize output.

5.1????Account Classification and Predictions

Account classification is crucial for prioritizing. Automated account classification means collection specialists can categorize their outreach based on intelligent insights from several payer types—fast payers, slow payers, at-risk accounts, strategic accounts, government agencies, and resellers—and ensure up-to-date information to make Collections workflows faster. Collection team will know who pays late or early, and everything in between. Automated accounts receivable software can also handle predictive remittance forecasting. Collection team will automatically know which customers are likely to make late payments or no payments at all, so they’ll be better prepared to implement the right incentives and strategy.

5.2????Intelligent Communication

Sending and receiving correspondence is a fundamental feature of any collections process. It’s essential to get it right, but it’s also imperative to ensure that collection team can do it efficiently. By applying automation, all customers can receive targeted correspondence with the right tone and content to strengthen customer relationships and help your business collect payments on time. The bi-directional correspondence automation works in conjunction with the payer classification so that all payers receive the most relevant messages.

5.3????Automated Metrics

Understanding where a customer is in the payment process can enable an Accounts Receivable team to send out the right correspondence and prepare for payments as they arrive. Better yet, when payment tracking is automated, the team receives real-time updates without having to stay in constant communication with customers. In addition to payment tracking, automated systems track collection metrics that a team can use to gain valuable insights and recognize trends, including Days Sales Outstanding (DSO).

5.4????Classify whether deduction claim is valid or invalid

Machine learning classification models can be trained to classify account deduction claims as valid or invalid. This can help account receivables managers to improve the quality of deductions and reduce bad debts arising out of incorrect account deduction claims. Classical machine learning algorithms such as random forest could be used for this machine learning use case.

5.5????Automated account reconciliation

Machine learning models can be trained to enable automatic reconciliation of account receivables invoices and remittance information from multiple connected sources such as ERP, accounts payable system, etc., which helps companies reduce account reconciliation manual efforts. OCR techniques along with classification machine learning models (random forest) can be used to match the invoice line items with remittance line items information.

5.6????Predict customer creditworthiness

Machine learning models can help companies predict account receivable customer’s ability to pay and their willingness to pay by assessing the account receivable customer’s financial data. Classical machine learning algorithms such as random forest, gradient boosting machines (GBM), etc., can be used to predict account receivables customers’ creditworthiness. Based on account receivables customers’ creditworthiness, companies can decide whether or not to extend credit. This can also be used to determine whether or not to block the orders in case customers do not meet certain creditworthiness criteria.

5.7????Estimate customer’s credit limit

Machine learning models can be used to estimate account receivable customer’s credit limits by assessing customers’ financial data and other risk factors of the account receivables customer such as bankruptcy.

5.8????Payment date prediction

Predicting payment date can result in triggering of collection account receivables reminders and account receivable dunning letters to customers as well as provide account managers with a quick view of potential bad debts arising out of late payment. Machine learning models such as logistic regression, the random forest could be used for this machine learning use case. Machine learning algorithms understand the payment behaviour of customers and come up with the most accurate prediction on when each individual invoice is going to be paid.

·??????On-time prediction – this model looks at current invoices and is designed to predict if an invoice is going to be paid before the due date. The resulting prediction of this algorithm is a probability of the invoice being paid in time.

·??????Overdue prediction – this algorithm is used for overdue invoices only and it is designed to predict which bucket this invoice is going to end up in. The following buckets are used for the predictions: 1–30 days overdue, 31–60 days overdue, 61–90 days overdue, 90+ days overdue.

5.9????Ranking customers for collection processes

Machine learning models can help account receivables managers to rank their customers based on the risk of non-payment, so that high-risk customers are given priority for the account receivable dunning process.

5.10??Predict account delinquency

Predicting account receivable delinquencies and early warning indicators of customer financial distress using classical machine learning algorithms such as random forest or GBM models has been a key area of research in the account debt collection domain.

5.11??Detect account receivable fraud

Machine learning models can be used to detect account receivables fraud by identifying non-genuine transactions and accounts, which helps in increasing the security of the account assets as well as maximizing recoveries from fraudulent customers. Fraudulent transactions or account activities could be detected using machine learning techniques such as random forest along with account receivables account transaction-level data.

5.12??Automated bad debt management processes

Machine learning classification models are useful in determining whether account receivables account balance is likely to become bad or not, so that account receivables managers can take early action for recovery of the account.

5.13??Accounts consolidation

Identify customers having offices at different locations and consolidate account receivables account to the main office address. This helps companies save on account receivable efforts for managing multiple accounts.

5.14??Customer segmentation

Machine learning models can help companies identify account receivables customers having similar characteristics, so that account managers could target them at the same time for account debt collection efforts or provide other personalized services to increase customer satisfaction levels with the organization.

5.15??Predict churn probability of account receivables customers

Machine learning models can be used to predict account receivables customer churn and come up with strategies for account retention.

Machine learning is a powerful tool that account receivables (AR) managers can use to automate account receivable management processes. For example, machine learning classification models are useful in determining whether AR account balance is likely to become bad or not so AR managers can take early action for recovery of the account. Machine learning algorithms such as GBM and random forest could also be used by companies to predict customer creditworthiness, rank customers based on the risk of non-payment, detect fraudulence within accounts, estimate customer’s credit limits, and more. If you want help applying these principles to your own company’s unique situation please let us know!

6.0??AR Automation Benefits

Accounts Receivable brings a few challenges for organizations of different sizes and structures. There is continuous pressure to get better at fund management and keeping track of accounts receivables will keep the organization in good shape financially. This can be taken care of by adopting automation technologies. It will bring numerous benefits that will help organizations to compete better. Applied to accounts receivable operations across the O2C cycle, hyper-automation is producing world-class performance. Operationally, companies can achieve:

·??????85% or higher current AR performance

·??????85% or higher cash application auto-match rates

·??????85% or higher credit approval cycle acceleration

·??????30% or higher DSO reduction

·??????50% or more global FTE redeployment

The combined power of intelligent technologies is producing the kind of automation envisioned:

·??????Automatic credit checks on all orders, with “touchless” release of up to 85 percent

·??????Credit bureau integration

·??????Automatic credit scoring and decisions on 100 percent of orders, with touchless approvals of 85+ percent

·??????100 percent touchless invoicing and posting to vendor portals

·??????Automated collections with 100 percent digital assistant-initiated request-to-pay

·??????85+ percent touchless payments

·??????85 percent touchless deductions gathering & automated processing

·??????85 percent auto-match, with 85 percent touchless remittance data capture (with bank integrations)

·??????Customer portal with digital assistant enabling 100 percent self-service and digital payments

7.0??Conclusion

The bottom line is that AI, ML and RPA is here to help. It offers financial professionals the opportunity to redefine and move up their value and contribution to their company, moving from manually intensive or repetitive work to more critical and value-creating work. It helps get information delivered to the right people, at the right time and empowers better relationships with partners and customers.

References

Hyper-automation for accounts receivable operations: Technology’s Promises Met, https://www.emagia.com/blog/hyper-automation-for-accounts-receivable-operations/

Ajitesh Kumar, Account Receivables Use Cases for Machine Learning, September 2021,?https://vitalflux.com/account-receivables-use-cases-machine-learning/

Vincent Ryan, AI is Ushering in a New Era of Integrated Receivables, October 2019, https://www.cfo.com/corporate-finance/2019/10/ai-is-ushering-in-a-new-era-of-integrated-receivables/

Adam Bryk, Huey Lee, Patrick Thibault, Brian Stewien, Strategies for optimizing your accounts receivable, https://www2.deloitte.com/content/dam/Deloitte/ca/Documents/finance/ca-en-FA-strategies-for-optimizing-your-accounts-receivable.pdf