Machine learning (ML)-based system and method for customer segmentation and worklist generation

A Machine Learning (ML)-based computing system for processing of open invoices for the customers, the ML-based computing system comprising:

• one or more hardware processors;
• and
• a memory coupled to the one or more hardware processors, wherein the memory comprises a plurality of modules in the form of programmable instructions executable by the one or more hardware processors, and wherein the plurality of modules comprises:
• a data receiver module configured to receive one or more open invoices from one or more electronic devices associated with a user, wherein the invoice data comprises customer number, payment terms, invoice date, baseline date, due date, discount date, clearing date, posting date, invoice type, transactions within a company, invoice amount, discount amount and payment block status;
• a data processing module configured to process the received one or more open invoices to generate granularity level instances, granularity level detected pattern, payment pattern consistency score and predicted payment date of one or more open invoices based on a trained ML model,
• wherein to process the received one or more open invoices, a payment pattern selection module is configured to:
• determine an adherence evaluation window and a look-back period for each date-shift-payment-pattern feature corresponding to each set of granularity level instances based on historically closed invoice data;
• estimate a cumulative error distribution of historical deviations between predicted and actual clearing dates of invoices within the look-back period;
• determine, from the cumulative error distribution, a revenue concentration level within the adherence evaluation window;
• and
• identify the date-shift-payment-pattern feature having the highest probability of adherence as an optimal payment pattern feature based on the determined revenue concentration level;
• a data prediction module configured to:
• predict a financial transaction completion pattern for the set of open invoices based on the optimal payment pattern feature, the set of date-shift-payment-pattern features and the set of granularity level instances by using a financial transaction pattern prediction-based ML model;
• predict the payment date for the one or more open invoices and the payment pattern consistency scores based on the optimal payment pattern features and the financial transaction completion pattern;
• a segment clustering module configured to:
• determine a payment behavior and customer risk for each of the one or more customers using a clustering based ML model, wherein the clustering based ML model groups similar data points associated with the generated payment pattern consistency scores, delay propensity factor, current state defining factors of the invoice and customer, contact behavior based features of customer, granularity level detected pattern and predicted payment date into clusters based on at least one of:
• distances between each of the data points, density of the data points, hierarchy of the clusters, and similarity to one or more representative points, and wherein the clustering based ML model comprises at least one of:
• a K-means model, DBSCAN model, HDBSCAN model and K-medoids model, and wherein the clustering based ML model is automatically selected for datasets having at least one of:
• outliers and varying cluster densities to enable at least one of:
• a noise-resistant and hierarchy-based clustering;
• segment each of the granularity level instances dynamically into one or more categories based on the determined payment behavior and the customer risk using the clustering based ML model, wherein the one or more categories comprises payment behaviour based segment and high risk segments;
• a worklist generation module configured to generate a customer worklist based on a configured optimization metric and the one or more categories, wherein the configured optimization metric comprises working capital, collecting the payment for the one or more open invoices;
• an action recommendation module configured to recommend one or more collection strategies for each of the one or more categories based on type of a category, a severity, and the configured optimization metric, wherein the one or more collection strategies comprises one or more actions;
• a prioritization module configured to rank each of the one or more customers in the generated customer worklist based on at least one of:
• the one or more recommended collection strategies;
• wherein the ML-based computing system is configured to:
• monitor performance of the trained ML model in real time;
• identify instances where performance of the ML-based computing system falls below a predetermined threshold;
• automatically generate new training data based on the identified instances;
• and
• retrain the trained ML model using the newly generated training data;
• a data output module configured to output the one or more collection strategies and the ranked one or more customers on a user interface of one or more electronic devices associated with the user.

A non-transitory computer-readable storage medium having instructions stored therein that, when executed by a hardware processor, cause the processor to perform method steps comprising:

• receiving one or more open invoices from one or more electronic devices associated with a user, wherein the invoice data comprises customer number, payment terms, invoice date, baseline date, due date, discount date, clearing date, posting date, invoice type, transactions within a company, invoice amount, discount amount and payment block status;
• processing the received one or more open invoices to generate granularity level instances, granularity level detected pattern, payment pattern consistency score and predicted payment date of one or more open invoices based on a trained ML model, wherein processing the received one or more open invoices comprises:
• determining, by the one or more hardware processors, an adherence evaluation window and a look-back period for each date-shift-payment-pattern feature corresponding to each set of granularity level instances based on historically closed invoice data;
• estimating, by the one or more hardware processors, a cumulative error distribution of historical deviations between predicted and actual clearing dates of invoices within the look-back period;
• determining, by the one or more hardware processors, from the cumulative error distribution, a revenue concentration level within the adherence evaluation window;
• and
• identifying, by the one or more hardware processors, the date-shift-payment-pattern feature having the highest probability of adherence as an optimal payment pattern feature based on the determined revenue concentration level;
• predicting, by the one or more hardware processors, a financial transaction completion pattern for the set of open invoices based on the optimal payment pattern feature, the set of date-shift-payment-pattern features and the set of granularity level instances by using a financial transaction pattern prediction based ML model;
• and
• predicting, by the one or more hardware processors, the payment date for the one or more open invoices and the payment pattern consistency scores based on the optimal payment pattern features and the financial transaction completion pattern;
• determining a payment behavior and customer risk for each of the one or more customers using a clustering based ML model, wherein the clustering based ML model groups similar data points associated with the generated payment pattern consistency scores, delay propensity factor, current state defining factors of the invoice and customer, contact behavior based features of customer, granularity level detected pattern and predicted payment date into clusters based on at least one of:
• distances between each of the data points, density of the data points, hierarchy of the clusters, and similarity to one or more representative points, and wherein the clustering based ML model comprises at least one of:
• a K-means model, DBSCAN model, HDBSCAN model and K-medoids model, and wherein the clustering based ML model is automatically selected for datasets having at least one of:
• outliers and varying cluster densities to enable at least one of:
• a noise-resistant and hierarchy-based clustering;
• segmenting each of the granularity level instances dynamically into one or more categories based on the determined payment behavior and the customer risk using the clustering based ML model, wherein the one or more categories comprises payment behaviour based customer segment and risk customer segment;
• generating a customer worklist based on a configured optimization metric and the one or more categories, wherein the configured optimization metric comprises working capital, collecting the payment for the one or more open invoices;
• recommending one or more collection strategies for each of the one or more categories based on type of a category, a severity, the configured optimization metric and the generated customer worklist, wherein the one or more collection strategies comprises one or more actions;
• ranking each of the one or more customers in the generated customer worklist based on at least one of:
• the one or more recommended collection strategies;
• dynamically updating and retraining, by the one or more hardware processors, the trained ML model by:
• monitoring performance of the trained ML model in real time, identifying instances where performance of the ML-based computing system falls below a predetermined threshold;
• automatically generating new training data based on the identified instances and retraining the trained ML model using the new training data;
• and
• outputting the one or more collection strategies and the ranked one or more customers on a user interface of one or more electronic devices associated with the user.