Machine Learning Credit Risk Management With Confidence Scoring
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Solution Overview
Problem
Existing credit risk management systems face challenges such as reactive reviews, delayed credit limit adjustments, high alert complexity, and the need for proactive and efficient credit risk assessments, particularly in industries with long order-to-delivery cycles.
Innovation Solution
A machine learning-based computing system that processes inputs from electronic devices, retrieves data from databases, preprocesses it to remove noise and outliers, determines credit risks using fine-tuned multi-classification models, generates credit decisions with confidence scores, and provides automated approvals for credit limits.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual credit risk assessment processes are used, then credit analysts can thoroughly evaluate creditworthiness, but the review process becomes time-consuming and creates backlogs
Solution Approach 1:
The patent introduces machine learning models as intermediaries between raw credit data and final credit decisions. These models automatically process and analyze credit data, generating preliminary assessments that credit analysts can then review and refine, thereby reducing manual analysis time while maintaining assessment quality
Solution Approach 2:
The patent replaces manual mechanical review processes with automated machine learning-based systems. The ML models automatically evaluate credit data, identify patterns, and generate credit decisions, substituting the time-consuming manual mechanical review process while preserving or enhancing assessment accuracy through algorithmic analysis
2Reliability
If frequent credit reviews are conducted, then credit risks are identified timely, but the workload and complexity for credit teams increase
Solution Approach 1:
The patent implements self-service credit monitoring where the machine learning system automatically continuously monitors credit data, identifies risks, and generates alerts without requiring manual initiation of reviews. The system serves itself by autonomously detecting when credit conditions change and when human intervention is needed
Solution Approach 2:
The patent performs preliminary credit risk assessments automatically using machine learning models before human reviewers are involved. The system pre-processes credit data, identifies potential issues, and prepares preliminary findings in advance, so that when human analysts do review cases, the work is already partially completed, reducing overall system complexity
3Reliability
If credit limit upgrades are delayed, then credit risk exposure is reduced, but sales and business performance are hindered
Solution Approach 1:
The patent uses machine learning models to perform preliminary credit risk assessments and predict future creditworthiness trends. Based on these predictions, the system proactively recommends credit limit upgrades before customers would naturally request them, allowing businesses to expand sales opportunities while maintaining appropriate risk controls through data-driven decisions
Solution Approach 2:
The patent implements continuous feedback loops where credit performance data is constantly monitored and fed back into the machine learning models. This feedback mechanism allows the system to learn from actual customer payment behavior and adjust credit limit recommendations dynamically, balancing risk control with sales optimization in real-time
4Adaptability or versatility
If credit limit downgrades are procrastinated, then customer relationships are maintained, but high-risk credit exposure and bad debt increase
Solution Approach 1:
The patent applies preliminary anti-action by using machine learning models to predict deteriorating credit conditions before they materialize into actual defaults. The system proactively identifies at-risk customers and recommends downgrades in advance, allowing businesses to take protective action before credit problems worsen, thus preventing bad debt while maintaining courteous customer communication
Data Source
AI summary
A machine learning based computing method for automatic managing credit risks of first users, is disclosed. The machine learning based computing method includes: receiving inputs from electronic devices associated with second users; retrieving data associated with first users from databases; preprocessing the data to remove noises, outliers, and missing values, from datasets; determining, the credit risks of the entities based on the pre-processed data by machine learning models; generating credit decisions for the entities; generating confidence scores for credit decisions to classify the credit decisions, based on correlation between the data and credit decisions; determining recommended credit values, recommended first credit limits, and recommended second credit limits, based on classification of the credit decisions; and providing an output of the credit decisions, the recommended credit values and the recommended credit limits, to the second users on user interfaces associated with electronic devices.