Predicting Disbursement Times in Online Remittance Systems
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Solution Overview
Problem
Online remittance systems face delays in confirming disbursement times for electronic funds transfers, making it impractical to provide timely updates to payers, due to factors like fraud detection, verification processes, and destination financial institution schedules.
Innovation Solution
An online remittance service system that predicts disbursement times by storing a model of the disbursement process, scheduling jobs using a job scheduling module, and determining next execution times to estimate when funds will be disbursed, allowing for immediate or near-immediate prediction of disbursement completion times to be provided to payers.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the system waits for disbursement confirmation from partners before providing update to payers, then the accuracy of disbursement status information is improved, but the response time to payers deteriorates
Solution Approach 1:
The system performs preliminary actions by storing a model of the disbursement process and scheduling jobs in advance that represent the steps of the disbursement process. When a transfer is initiated, the system determines next execution times for scheduled jobs and provides predicted disbursement time to the payer immediately, without waiting for actual disbursement confirmation from partners.
Solution Approach 2:
The system introduces an intermediary prediction mechanism that mediates between the payer's need for immediate information and the partner's actual disbursement process. The predicted disbursement time serves as an intermediary value that provides timely guidance to payers while the actual disbursement confirmation is still pending from partners.
2Ease of operation
If the system provides immediate prediction of disbursement time to payers, then the user satisfaction is improved, but the system complexity increases
Solution Approach 1:
The system segments the disbursement process into discrete scheduled jobs representing individual steps. Each job has a determined next execution time, allowing the system to calculate predicted disbursement time by aggregating these segmented time components without requiring complex end-to-end modeling.
Solution Approach 2:
The system changes the parameter representation from waiting for actual disbursement confirmation to using predicted execution times of scheduled jobs. This parameter transformation enables immediate prediction provision while managing system complexity through structured job scheduling.
3Measurement precision
If the system implements a detailed model of the disbursement process with multiple scheduled jobs, then the prediction accuracy is improved, but the computational overhead increases
Solution Approach 1:
The system performs preliminary scheduling of jobs representing disbursement steps before actual disbursement occurs. Next execution times are determined in advance based on the stored model, allowing accurate prediction calculation without real-time computational overhead during the actual disbursement process.
Solution Approach 2:
The scheduled jobs represent self-service components of the disbursement process, with predetermined execution times based on partner schedules. The system leverages these pre-established job schedules to generate predictions without requiring intensive real-time computation or continuous monitoring of partner systems.
Data Source
AI summary
An online remittance service system with a methodology for predicting disbursement times of online electronic funds transfers is disclosed. In one embodiment, for example, a method includes storing in computer memory a model of a disbursement process performed by one or more partners of the online remittance service for disbursing funds to payees in the destination country; based on the model, scheduling one or more computer-executable jobs with a job scheduling module representing one or more steps of the disbursement process; for each job of the one or more jobs, determining a next execution time for the job, the next execution time representing a time at which the job scheduling module will next execute the job after a specified time; and based on the next execution times for the one more jobs, determining a predicted time of disbursement of the online electronic funds transfer.