The credit and A/R teams across companies and industries are dedicated to evaluating all available solutions and leveraging the full potential of top-notch technologies in order to improve their order-to-cash functions. This section dives deeper into the four core A/R processes and gives a brief overview of how AI and RPA revamp these functions.

5.1. RPA and AI in Credit Management

Credit management is an important business function in any organization as it drives revenue enhancement and risk containment. The process involves gathering credit information from different Credit Agencies, Credit Groups, Public Financials and Credit Insurance Bureaus followed by capturing credit application data from online or offline forms. A/R teams engage in credit scoring and risk assessment before onboarding customers and setting up credit limits. Credit management also involves other functions like periodic reviews and blocked order management. Robotic Process Automation can be leveraged in credit management to capture credit application data by defining business rules and enabling template-based credit applications. RPA could also provide the functionality of auto-retrieval of credit reports from various sources. Further, it could also contribute to better collaboration for credit approvals, periodic reviews, blocked order release, customer correspondence by setting up pre-defined system responses. AI, on the other hand, could provide deep-routed insights which in turn could help in improving credit policies and ensuring effective risk mitigation. It could predict blocked orders and potential customer default based on the analysis of a multitude of factors.

5.2. RPA and AI in Cash Application

Cash application is another important A/R process that applies incoming payments to the correct customer accounts and receivable invoices. It involves downloading and aggregating payment and remittance information from a variety of sources including checks, emails, EDI files, websites or customer portals. This is followed by payment-remittance linking and three-way matching of payment, remittance and invoice information. Further, if there are short-payments, the reason codes are entered and discounts (if any) are applied. In cases of missing or incomplete remittances or any other discrepancy, analysts engage exception handling and finally, the cash is posted into the ERP. For cash application, RPA can be implemented to aggregate remittance details from multiple sources including emails, portals and EDI files. With predefined rules in the solution, the payment and invoice information can be easily matched and short-payments can be auto-coded. Moreover, automated correspondence can be sent to customers in cases of missing remittance. Artificial Intelligence enables data identification (example: identifying relevant details in check stubs) and accurate remittance information capture. Intelligent invoice mapping for unstructured or non-standardized data ensures that ?all? corresponding invoices, payments and remittance are matched irrespective of templates and formats. Auto-resolution of repetitive exceptions based on pattern recognition and trend analysis is another important feature provided by AI. Moreover, it can enable auto-triggered emails to customers with high probability of sending incomplete or incorrect information or altogether not sending the remittance.

5.3. RPA and AI in Deductions Management

Deductions Management consists of aggregating back-up documents like PODs, BOLs, and claims from different sources like carriers and warehouses, linking the documents to the corresponding deduction line item, followed by cross-department correspondence (example: with sales), verification and root-cause analysis. Based on the workflow and analysis, the deduction is approved or rejected and the correspondence is sent to the customer accordingly. As a result, the dispute is resolved and the system is updated. RPA can be implemented for deductions management to aggregate back-up documents, enable automated correspondence for cross-department collaboration and support customer correspondence for approval/denial of deduction line items. AI can be leveraged to predict the validity of deductions which could, in turn, be used for prioritizing the deduction cases. This would ensure time and resource savings as the deduction analysts would spend more time on more critical research. Intelligent linking of backup research documents with deduction is another crucial functionality that could be enabled by AI.

5.4. RPA and AI in Collections Management

Collections are one of the most important processes in Accounts Receivables. For a business to have a leading growth graph, a business ?needs? to put efforts into Collections Management. It involves creating a prioritized worklist based on numerous factors such as credit data, payment history, correspondence information, invoice values, and aging data and utilizing industry best methods like customer segmentation. It further involves customer correspondence, logging correspondence activity, tracking payment commitments, reminders, and follow-ups. This is followed by payment collection and reporting. RPA can be integrated into collections for generating prioritized worklist along with auto-recommended account level actions based on predefined business rules and strategies that take necessary factors such as credit data and payment history into account. It can enable automated collaboration with customers using templates and correspondence packages. Reconciliation of invoice payment and approval statuses from customer portals, third-party websites, is another feature provided by RPA for collections. AI could predict invoice payment dates at the customer/ invoice level based on historical data and trend analysis. Dynamic, real-time prioritization of the worklist can be realized with AI. Further, it could enable the prediction of customer preferences including correspondence time and mode, ensuring a high probability of collection success.