AI Repossession Prediction for Delinquent Auto Loans
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Solution Overview
Problem
Existing financial institution processes for managing delinquent auto loans are inadequate in predicting and addressing the likelihood of repossession events, as they often initiate mitigation steps only when loans are significantly delinquent and fail to adapt to underlying causes of delinquency.
Innovation Solution
A machine-learning or artificial-intelligence process, such as a gradient-boosted decision-tree process, is trained to predict the likelihood of target repossession events for delinquent auto loans across different delinquency checkpoints, using input datasets and generating output data indicative of predicted likelihoods for future temporal intervals, enabling proactive intervention.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If traditional financial institution processes are used to manage delinquent auto loans, then mitigation steps are initiated only when loans are significantly delinquent, but the ability to predict and address repossession risks early is lost
Solution Approach 1:
The system performs preliminary prediction of repossession risks by analyzing historical interaction data and generating probability scores before delinquency reaches significant levels. This allows financial institutions to take preventive actions early in the delinquency timeline, rather than waiting until loans are significantly delinquent, thereby reducing loss of time in identifying repossession risks.
Solution Approach 2:
The patent replaces traditional mechanical rule-based monitoring systems with an artificial intelligence/machine learning system that automatically analyzes patterns in historical data. This substitution enables the system to predict repossession events with higher precision by identifying complex relationships in the data that traditional systems cannot detect, thereby improving measurement precision of repossession risk prediction.
2Adaptability or versatility
If traditional rule-based processes are used, then mitigation steps are standardized and easy to implement, but the ability to adapt to underlying causes of delinquency is reduced
Solution Approach 1:
The system implements dynamic adaptation by continuously learning from historical interaction data and adjusting its prediction models accordingly. The AI/ML processes are trained on diverse datasets representing various underlying causes of delinquency, enabling the system to adapt its behavior to different scenarios. This dynamic capability allows the system to customize mitigation strategies based on the specific patterns it detects in the data, thereby improving adaptability to delinquency causes.
Solution Approach 2:
The system creates simplified representations (copies) of complex delinquency patterns through trained AI models. These models capture the essential relationships between historical interactions and repossession outcomes, allowing the complex adaptive behavior to be encoded in relatively simple prediction algorithms. This copying approach enables adaptability while managing system complexity, as the complex patterns are replicated in a computationally efficient form.
3Reliability
If AI processes are applied to predict repossession events, then early identification of risks is enabled, but computational resources and processing time are increased
Solution Approach 1:
The system performs preliminary training of AI models using historical data before deployment. This preliminary action creates ready-to-use prediction models that can be applied to new data with minimal computational overhead. By doing the heavy lifting of pattern recognition during the training phase, the system achieves high reliability in predictions while reducing the computational energy required during actual operation, as the trained models can quickly process new interaction data.
Data Source
AI summary
The disclosed embodiments relate to computer-implemented systems and processes that facilitate a prediction of occurrences of product-specific events during targeted temporal intervals using trained artificial intelligence processes. For example, an apparatus may generate an input dataset based on elements of first interaction data associated with an occurrence of a first event. Based on an application of a trained artificial intelligence process to the input dataset, the apparatus may generate an element of output data representative of a predicted likelihood of an occurrence of each of a plurality of second events during a target temporal interval associated with the first event. The apparatus may also transmit the elements of output data to a computing system, which may perform operations that are consistent with the elements of output data.