Machine-Learning Server Account Segmentation for Payment Contact Timing
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Solution Overview
Problem
Existing systems face challenges in effectively reaching and collecting payments from users with low, zero, negative, or overdue account balances, leading to difficulties in managing and preventing further activities under new accounts, which can impact both users and more responsible account holders.
Innovation Solution
A network server device performs machine learning operations to segment accounts based on balances and behavioral data, determining target accounts for personalized communication methods and times to contact users, using GPS, social media, and sensor data to increase the likelihood of payment reminders.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional contact methods (phone calls, messages) are used to reach users with negative balances, then the system can attempt to collect payments, but the majority of users cannot be reached or ignore the contact attempts
Solution Approach 1:
The system segments users based on their balance status (negative, zero, low) and behavioral patterns, then applies different contact strategies to each segment. This segmentation allows the system to focus resources on users most likely to respond and pay, improving both contact success rate and payment collection efficiency.
Solution Approach 2:
The system performs preliminary analysis of user behavior patterns, device usage habits, and historical payment data before initiating contact. This preliminary action enables the system to predict the optimal time and method to contact each user, significantly increasing the likelihood of successful contact and payment collection.
2Adaptability or versatility
If users with negative balances are allowed to continue making commitments and opening new accounts, then they can maintain activity on the platform, but they continue to accumulate debt and engage in poor financial decisions
Solution Approach 1:
The system applies preliminary anti-action by proactively contacting users with negative balances before they can open new accounts or make additional commitments. The system uses behavioral data to predict when users might attempt to create new accounts and intervenes with targeted communications to prevent further debt accumulation while allowing legitimate account activity to continue.
3Ease of operation
If the system contacts all users with negative balances using the same method, then the process is simple to implement, but the contact success rate remains very low
Solution Approach 1:
The system applies local quality by tailoring the contact method and timing to each user's specific behavior patterns and device usage habits. Instead of a uniform approach, the system analyzes individual user data to determine the optimal contact strategy for each person, making the process highly effective while maintaining operational simplicity through automated decision-making.
Data Source
AI summary
A system, a medium, and a method are provided to exchange data packets over a communications network and perform machine learning operations. A network server device receives account data from client devices that correspond to account profiles. An account engine of the network server device segments the account profiles into profile groups based on a respective balance associated with each account profile. The account engine determines target accounts from profile groups based on behavioral data. Further, data processing components of the network server device determine a method of contact for each target account. The data processing components determine a respective time to communicate with a respective device for each target account. Further, communication components of the network server device initiate communications to the respective devices at the respective times for each target account.