ML DSO Impact Scoring for Collection Worklist Prioritization
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Solution Overview
Problem
Conventional methods for collecting open invoices lack intelligence in worklist prioritization, leading to inefficient manual efforts and limited insights, resulting in suboptimal Days Sales Outstanding (DSO) management.
Innovation Solution
A Machine Learning (ML)-based system and method that calculates DSO impact scores by analyzing credit sale amounts, account receivables, disputed and skipped invoices, and predicted payment dates, using regression models like Light GBM, XGBoost, and CatBoost to generate scores and highlight key pain points for improved collection strategies.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If conventional methods are used for worklist prioritization, then manual effort is required, but productivity is low and insights are limited
Solution Approach 1:
The system enables self-service by automatically generating worklist prioritization, customer segmentation, and collection strategies without requiring manual analyst intervention. The ML models autonomously analyze payment behavior patterns, calculate DSO impact scores, and generate actionable insights, freeing collectors from manual analysis tasks while improving productivity.
Solution Approach 2:
Manual mechanical analysis methods are replaced with automated ML-based systems. The patent substitutes human analysts' manual review processes with algorithmic models that automatically process transaction data, identify payment patterns, and generate prioritization recommendations, significantly reducing manual effort time while enhancing collection efficiency.
2Loss of information
If conventional customer segmentation is used, then basic factors are considered, but payment behavior insights are limited
Solution Approach 1:
The system transforms the analysis by changing parameters from basic customer attributes to dynamic payment behavior metrics. ML models analyze temporal patterns in payment behavior, cash flow cycles, and DSO trends, converting static customer data into dynamic behavioral insights that reveal predictive payment patterns and enable more effective collection strategies.
3Ease of operation
If highest invoice value method is used for prioritization, then simple ranking is provided, but intelligent worklist prioritization is not achieved
Solution Approach 1:
The system segments customers into distinct payment behavior clusters using unsupervised learning algorithms. Each segment is characterized by specific payment patterns, risk profiles, and DSO impact potentials. This segmentation enables tailored collection strategies for different customer groups while maintaining operational simplicity through automated classification and scoring.
Solution Approach 2:
The ML models perform preliminary analysis of payment behavior patterns and generate prioritization recommendations before collectors begin their work. By pre-calculating DSO impact scores, identifying key pain points, and suggesting optimal collection approaches, the system prepares actionable intelligence in advance, guiding collectors on what actions to take and why.
Data Source
AI summary
A Machine Learning (ML)-based computing system and method for financial transaction based customer worklist generation is disclosed. A data determination module configured to obtain a credit sale amount, an account receivable as of a run date of the module (RD), a disputed invoice amount and a skipped invoice amount using an Machine Learning (ML) model. A DSO component calculation module configured to calculate the obtained DSO components for each entity corresponding to a grouping category at a given point of time period. A DSO impact score generation module configured to generate a DSO impact score based on the estimated open amount reduction, desired number of days in period and the credit sale amount. A Machine Learning insight module configured to calculate the generated DSO impact score based on historical customer information associated with one or more customers. A data output module configured to output the DSO impact score.