Loan Allocation Optimization via Lagrangian Risk Scoring
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Solution Overview
Problem
Resource allocation systems face challenges in optimizing loan borrowing requests across multiple financial institutions while managing risk and serving capacity constraints effectively.
Innovation Solution
A method and system that utilize historical data to construct an optimization model, incorporating projected approval rates, risk factors, and serving capacity, to determine the best entity for loan allocation by transforming the model into a Lagrangian function and applying multipliers to score entities, ensuring globally optimal allocation strategies.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If a loan servicing platform allocates borrowing requests to multiple cooperative financial institutions, then the number of requests that can be satisfied increases, but the complexity of managing risk and serving capacity constraints across multiple entities increases
Solution Approach 1:
The patent introduces an intermediary optimization system that acts as a mediator between borrowing requests and multiple financial institutions. This system formulates and solves optimization models that incorporate risk constraints and serving capacity limitations, thereby coordinating allocations across multiple entities without requiring direct complex management between each institution pair. The intermediary handles the complexity centrally while allowing individual institutions to operate independently.
Solution Approach 2:
The patent transforms the allocation problem by changing parameters through mathematical optimization. It formulates objective functions and constraints that convert qualitative risk and capacity considerations into quantitative parameters. By solving these optimization models, the system dynamically adjusts allocation parameters to maximize satisfied requests while maintaining control over risk and capacity constraints across multiple financial institutions.
2Reliability
If the platform considers risk tolerance and serving capacity constraints for each financial institution, then allocation reliability improves, but the computational complexity and time required for optimization increases
Solution Approach 1:
The patent applies preliminary action by pre-formulating optimization models with embedded risk tolerance and serving capacity constraints for each financial institution. These models are prepared in advance with all necessary constraints and objective functions defined, allowing the system to quickly solve for optimal allocations when requests arrive. The preliminary setup of optimization frameworks eliminates the need for real-time complex negotiations or ad-hoc constraint handling.
3Measurement precision
If the platform uses historical data to construct optimization models, then allocation precision improves, but the complexity of data processing and model construction increases
Solution Approach 1:
The patent implements self-service by enabling the optimization system to automatically construct and solve allocation models using historical data. The system independently processes historical data to extract relevant patterns, formulates appropriate optimization models with suitable objective functions and constraints, and executes the optimization without requiring manual intervention. This automated self-service approach handles the data processing complexity internally while delivering precise allocation results.
Data Source
AI summary
Methods, systems, and apparatus, including computer programs encoded on computer storage media, for optimizing resource allocation. The method may comprise: receiving a resource request from a user; iterating through a plurality of entities to obtain a corresponding score for each entity based at least on: a projected approval rate for the entity to approve the resource request, a projected risk for the entity to serve the resource request, and one or more multipliers applied to the projected approval rate and the projected risk; and recommending one of the plurality of entities to serve the resource request for the user based on the corresponding score, wherein the one or more multipliers are obtained by solving an optimization model constructed based on historical data collected from a previous period of time, the historical data comprising approval rates projected for the plurality of entities during the previous period of time.