With HighRadius Rivana – Decision Making is now an integral part of credit-to-cash processing. Rivana combines the power of Machine Learning with rich A/R data, to help credit and A/R departments to significantly reduce the number of transactions they have to manually review, freeing up their time for high-value work. Additionally, the AI platform enables rich, data-backed insights enabling analysts to take actions resulting in more favorable outcomes.
Artificial Intelligence for high-impact decision making across HighRadius cloud solutions for credit, collections, deductions, cash application, billing and payments.
Embedded Decision Making with Your Credit and A/R Data
Regular Software Does Not Enable Decisions
Most software applications are developed on the CRUD (create-retrieve-update-delete) paradigm for electronically centralizing information, thereby enabling workflow collaboration between users. While this is a major upgrade over paper-based processes, such systems have minimal intelligence and completely rely on end-user judgement to make key decisions.
Although all business processes rely on human-generated insight and analysis for success, it is nearly impossible for a credit and A/R analyst to effectively scale, given that they spend 80% of their time in clerical tasks, processing huge volumes of transactions.
Additionally, it is nearly impossible for analysts to account for all the various dimensions and patterns that influence credit and A/R outcomes, and hence they rely on individual judgement based on experience. For example:
- Currently, most collections analysts chase past-due customers without complete knowledge of their payment behavior.
- Companies receive thousands of deductions every day. And while most of them are valid, deduction analysts still spend countless hours executing routine steps to confirm deduction validity for each one.
- Credit analysts perform credit reviews on blocked orders which for the most part result in releasing the order in an unconfirmed expectation of future payments.
Embedded Decision Making with Machine Learning
The current generally available platform relies on supervised learning techniques such as regression and classification done using Decision Trees and Random Forest methods. Unsupervised learning techniques will be made generally available in the platform by Q4 2017.
HOW IT WORKS
Accurate Analysis with your A/R Data
Companies need technology to complement human intuition and decision making. Your A/R data is a rich resource and foundation for making credit and A/R decisions that could result in better outcomes for the entire credit-to-cash process. With machine learning algorithms customized to meet use-cases across credit and receivables, HighRadius Rivana is set to help you leverage the true power of your customer and A/R data.
Artificial Intelligence across Credit-to-Cash
Working with the HighRadius Integrated Receivables platform enables HighRadius Rivana to tap into your A/R data and provide additional information and insight by taking inputs from across processes to predict and work across all modules and helps you arrive at more accurate decisions.
Decision Analytics for Credit and A/R
Invalid Dispute Identification
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.
HighRadius Rivana allows deduction analyst to focus on resolving disputes which are more likely to be invalid. Employing classification algorithms, Rivana is capable of identifying deductions which have a very high probability of being valid and is able to automatically resolve them or move them or de-prioritize them on the analyst’s worklist.
Payment Date Prediction
More than 70% of all collections correspondence is directed at customers who would have paid even without the dunning email or reminders. However, other than some form of static prioritization for their worklists, analysts have no means to identify customers or accounts which do not require any dunning. This means that analysts are able to spend less time on ‘at-risk’ accounts.
HighRadius Rivana allows collection analysts to focus on only the most critical customers and invoices. Leveraging regression using Decision Trees, Rivana is able to predict an actual payment date for every invoice. Analysts are able to use this additional insight available for each insight to determine the right course of action when following-up with a customer.
Learn more about how Machine Learning could enable your A/R teams
Supporting A/R teams in everyday decision making with data
Leverage your A/R data and machine learning to support users with accurate decision inputs and forecasts
An ever-growing customer base is impossible to keep up with. Get data-backed insight into customer preferences.
Save the time lost in looking for data in your open A/R and customer records. Focus resources on actual value-add work.