ML Credit Limit Adjustment for B2B Marketplaces
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Solution Overview
Problem
Existing B2B marketplace platforms rely on reactionary human reviews to adjust credit limits for retailers, which do not effectively utilize retailer data for proactive adjustments, leading to inefficiencies in risk management and revenue maximization.
Innovation Solution
Implementing machine learning (ML) models to analyze retailer data, identify cohorts based on snapshot dates, and produce sets of ML models for risk assessment, non-defaulter value prediction, and defaulter value prediction, enabling data-driven adjustments to credit limits.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If human reviewers manually adjust credit limits based on retailer data, then credit limit adjustments can be made with human judgment and oversight, but the process is reactionary and does not effectively utilize all available retailer data for proactive adjustments
Solution Approach 1:
The system performs preliminary actions by continuously analyzing retailer data in the background to identify retailers who should have their credit limits adjusted. The machine learning models predict future credit limit needs and risks before they materialize, enabling proactive adjustments rather than waiting for human reviewers to react to events. This includes identifying retailers who will benefit from credit limit increases or who pose future default risks.
Solution Approach 2:
The patent replaces the mechanical system of human reviewers manually analyzing retailer data with an automated machine learning-based system. The ML models process retailer data, purchase histories, and payment behaviors to automatically identify credit limit adjustment opportunities and predict outcomes, substituting human cognitive processes with computational algorithms that can analyze data more comprehensively and rapidly.
2Productivity
If machine learning models are implemented to proactively analyze retailer data and predict credit limit adjustments, then data-driven proactive adjustments can be made to maximize revenue and manage risk, but the system complexity increases significantly
Solution Approach 1:
The machine learning system is segmented into multiple specialized models, each performing a specific function: identification models that find retailers needing credit limit adjustments, risk models that predict default probabilities, and value models that estimate the financial impact. This segmentation allows the complex overall task to be divided into manageable components that can be developed, trained, and maintained independently while working together in an integrated system.
Solution Approach 2:
The patent introduces intermediary components that bridge the gap between raw retailer data and credit limit adjustment decisions. These include data processing layers that prepare and normalize retailer data, feature engineering components that extract meaningful signals, and model output aggregation mechanisms that combine predictions from multiple ML models into actionable recommendations. These intermediaries simplify the integration of complex ML models with the existing credit limit management system.
3Reliability
If comprehensive retailer data is collected and analyzed using multiple ML models, then accurate predictions of default risk and value can be made, but the computational resources and processing time required increase
Solution Approach 1:
The system applies partial action by not attempting to analyze all retailer data for all retailers simultaneously, but rather focusing computational resources on identifying a subset of retailers who are most likely to benefit from credit limit adjustments or pose the highest risks. The identification models filter the retailer population to prioritize those requiring attention, allowing the system to achieve high predictive accuracy for the target group without expending excessive computational resources on the entire retailer base.
Solution Approach 2:
The patent employs parameter changes by dynamically adjusting the complexity and resource allocation of ML model processing based on various factors such as retailer risk profiles, data availability, and system workload. The system can modify model parameters, sampling rates, and processing depths to optimize the balance between predictive accuracy and computational cost, allocating more resources to high-value predictions and fewer resources to lower-priority analyses.
Data Source
AI summary
Computer implemented method and systems for using machine learning to adjust a respective credit limit of one or more retailers that utilize a B2B marketplace platform to make product orders, are disclosed herein. Such a method includes collecting retailer data for retailers that utilized the B2B marketplace platform, and identifying cohorts based on the retailer data. The method also includes producing, based on the cohorts, a plurality of sets of ML models that include a set of risk ML models, a set of non-defaulter value ML models, and a set of defaulter value ML models, each of which sets include respective cohort-based ML scoring models and a cohort-based ML aggregation model. The method also includes using scores produces by the sets of ML models to determine whether, and to what extent, to adjust the credit limit of a retailer.