Automated Mortgage-Backed Security Grouping for Fee Minimization
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Solution Overview
Problem
Current methods for packaging mortgage-backed securities (MBS) into Mega-groups for fee calculation purposes are inaccurate, leading to higher processing fees due to the lack of automated tools that can efficiently identify optimal groupings for discounted fees, especially when dealing with large numbers of MBS.
Innovation Solution
A method and system for calculating a reduced fee by identifying and grouping MBS into Mega-groups using a recursive process that selects combinations of MBS that qualify for fee discounts, ensuring each MBS is in one group and no more than three MBS are in a group, thereby minimizing the total processing fee.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual methods are used to group MBS into Mega-groups for fee calculation, then the process can be performed without automated tools, but the accuracy of fee calculation decreases and processing fees increase
Solution Approach 1:
The patent replaces manual grouping methods with an automated computer-based system that uses algorithms to identify optimal Mega-group combinations. The system automatically processes MBS data, calculates potential groupings, and determines the configuration that minimizes fees, eliminating the need for manual intervention and ensuring accurate fee calculation.
Solution Approach 2:
The system enables bulk traders and investors to independently identify optimal Mega-group configurations without requiring assistance from intermediaries. By providing automated tools that calculate fees and suggest groupings, the system allows users to self-optimize their fee structures and achieve cost savings autonomously.
2Productivity
If automated tools are introduced to identify optimal MBS groupings, then fee calculation accuracy improves and savings increase, but system complexity increases
Solution Approach 1:
The patent divides the complex task of Mega-group optimization into distinct functional modules: data input for MBS characteristics, algorithmic processing for identifying valid groupings, fee calculation for each configuration, and output of optimal groupings. This segmentation allows each module to be developed and maintained independently, managing overall system complexity while achieving high productivity.
Solution Approach 2:
The system introduces a computer-based intermediary that acts as a bridge between raw MBS data and fee optimization decisions. This intermediary automatically processes the data, applies grouping rules, and generates recommendations, simplifying the overall process by centralizing the computational complexity in a dedicated system rather than requiring complex manual procedures.
3Measurement precision
If comprehensive automated analysis is performed to find all possible Mega-group combinations, then the lowest possible fee is guaranteed, but the calculation time increases
Solution Approach 1:
The system performs preliminary filtering and validation of MBS data before conducting the full optimization analysis. By pre-processing the input data to identify valid candidates and apply basic grouping constraints early in the process, the system reduces the search space for subsequent optimization algorithms, ensuring accurate fee minimization while reducing overall calculation time.
Data Source
AI summary
A method, system, and computer program product for calculating a reduced fee associated with combining previously securitized mortgage-backed securities to form a new and larger security called a Mega. The method includes identifying a group of Megas that correspond to at least one predetermined fee discount parameter. The group of Megas are members of a set of Megas and the set of Megas contain a number of Megas equal to or larger than a number of Megas in the group of Megas. The method includes calculating a fee for said group of Megas and repeating the steps of identifying a group and calculating a fee until a plurality of groups of Megas have been identified and corresponding Mega group fees have been calculated. The method also includes choosing from said plurality of groups of Megas a final set of Mega groups having a reduced total fee.