公开(公告)号：US20250014118A1

公开(公告)日：2025-01-09

申请号：US18461713

申请日：2023-09-06

Applicant: Oracle International Corporation

Inventor： Vikas AGRAWAL ,  Krishnan RAMANATHAN ,  Praneeth Medhatithi SHISHTLA ,  Jagdish CHAND

IPC: G06Q40/12 ,  G06N20/00

Abstract: Embodiments predict a target variable for accounts receivable using a machine learning model. For a first customer, embodiments receive a plurality of trained ML models corresponding to the target variable, the plurality of trained ML models trained using the historical data and comprising a first trained model having no grace period for the target variable and two or more grace period trained models, each grace period trained model having different grace periods for the target variable. Embodiments determine a Matthews' Correlation Coefficient (“MCC”) for the first trained model. When the MCC for the first trained model is low, embodiments determine the MCC for each of the grace period trained models, and when one or more MCCs for each of the grace period trained models is higher than the MCC for the first trained model, embodiments select the corresponding grace period trained model having a highest MCC.

公开(公告)号：US20250014097A1

公开(公告)日：2025-01-09

申请号：US18470555

申请日：2023-09-20

Applicant: Oracle International Corporation

Inventor： Vikas AGRAWAL ,  Krishnan RAMANATHAN ,  Praneeth Medhatithi SHISHTLA ,  Jagdish CHAND

IPC: G06Q40/03

Abstract: Embodiments analyze a customer of an organization. Embodiments select the customer and receive historical data corresponding to a plurality of transactions of the customer with the organization, the historical data including, for each of the transactions, a target variable including a number of days of delayed payment for each transaction. Based on the historical data, embodiments determine a cost of a delayed payment from the customer and determine an average delay of payments of the customer. Embodiments convert the cost of delayed payments to a first Z-score and the average delay of payments to a second Z-score. Embodiments then determine a reliability score of the customer comprising determining a Euclidean distance of the first Z-score and the second Z-score.

公开(公告)号：US20250014060A1

公开(公告)日：2025-01-09

申请号：US18236048

申请日：2023-08-21

Applicant: Oracle International Corporation

Inventor： Vikas AGRAWAL ,  Krishnan RAMANATHAN ,  Praneeth Medhatithi SHISHTLA ,  Jagdish CHAND

IPC: G06Q30/0204 ,  G06Q20/08

Abstract: Embodiments predict a target variable for accounts receivable in response to receiving historical data corresponding to a plurality of transactions corresponding to a plurality of customers, the historical data including, for each of the transactions, the target variable. Embodiments segment each of the customers based on the historical data corresponding to each of the customers, the segmenting including determining a variation of the target variable for each customer and, based on the variation, classifying each customer as having a low variation, a medium variation, or a high variation. For each low variation customer, embodiments create a regular ML model without a grace period that is trained and tested using the historical data. For each medium variation customer, embodiments create the regular ML model and create two or more grace period ML models, each grace period ML model adding a different grace period to the target variable.