Dynamic LLM Adaptation for Open Banking Data Automation

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Solution Overview

Problem

Existing open banking services face challenges in efficiently aggregating and automating data due to the lack of adaptation of artificial intelligence solutions, requiring manual intervention and inefficient decision-making, which hinders timely and dynamic service provision.

Innovation Solution

A computer-implemented method and system using a dynamic large language model (LLM) to automatically adapt to open banking objectives, involving prompt generation, evaluation, and retraining based on predefined actions and modifications to enhance data processing and analysis.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Reliability

If manual intervention is used for data aggregation and decision-making in open banking services, then reliability of data processing is maintained, but productivity and timeliness of service provision deteriorate

Engineering Contradiction:

Improvedata processing reliabilityVSAvoidservice provision productivity

Core Design Contradiction:

ReliabilityVSProductivity

Solution Approach 1:

The system enables automated self-service through the dynamic LLM that autonomously performs data aggregation, evaluation, and decision-making without manual intervention. The LLM automatically adapts to open banking objectives and processes data-rich vernacular, eliminating the need for human operators while maintaining service reliability and improving productivity.

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

The patent replaces manual mechanical operations with an intelligent automated system. The dynamic LLM substitutes human decision-making processes with algorithmic evaluation based on objective functions, automatically identifying training actions and prompt modifications to achieve open banking objectives without human intervention.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

2Extent of automation

If existing AI solutions are used for open banking services, then automation is partially achieved, but adaptability to open banking objectives and data-rich vernacular deteriorates

Engineering Contradiction:

Improveautomation levelVSAvoidadaptability to open banking objectives

Core Design Contradiction:

Extent of automationVSAdaptability or versatility

Solution Approach 1:

The system implements a dynamic LLM that continuously adapts to open banking objectives through real-time evaluation and retraining. The LLM is not static but dynamically adjusts its parameters and behavior based on objective function evaluations, automatically identifying training actions and prompt modifications to improve its performance on open banking tasks.

Inventive Principle:

Principle #15Dynamics

Solution Approach 2:

The patent incorporates feedback mechanisms where the LLM output is evaluated against open banking objectives using an objective function. Based on the evaluation results, the system automatically identifies training actions and prompt modifications, creating a closed-loop feedback system that continuously improves the LLM's adaptability to open banking requirements.

Inventive Principle:

Principle #23Feedback

3Device complexity

If static LLM models are used for open banking services, then device complexity is reduced, but adaptability to changing open banking objectives and data structures deteriorates

Engineering Contradiction:

Improvesystem complexityVSAvoidadaptability to data structures

Core Design Contradiction:

Device complexityVSAdaptability or versatility

Solution Approach 1:

The system transitions from static to dynamic LLM modeling, where the model automatically adapts to changing open banking objectives and data structures. The dynamic LLM continuously retrains on curated training datasets and adjusts its parameters based on objective function evaluations, enabling it to handle evolving data formats and requirements without manual reconfiguration.

Inventive Principle:

Principle #15Dynamics

Solution Approach 2:

The system performs preliminary actions by automatically curating training datasets and preparing retraining processes before the LLM needs to adapt to new objectives. The dynamic LLM proactively identifies training actions and prompt modifications, performing necessary adaptations in advance to maintain optimal performance on open banking tasks.

Inventive Principle:

Principle #10Preliminary action

Data Source

PatentUS20260050899A1Computer-implemented methods, systems comprising computer-readable media, and electronic devices for providing large language model dynamic open banking services

Publication Date: 2026.02.19 MASTERCARD INT INC

AI summary

A computer-implemented method for providing dynamic large language model (LLM) open banking services that includes: generating an output based on a prompt to an LLM, the prompt including open banking data; evaluating the output with an objective function to determine a difference between the output and an open banking objective; based on the difference, automatically identifying a predefined training action and a predefined prompt modification; based on the predefined training action, curating a training data set and retraining the LLM on the training data set to generate a retrained LLM; based on the predefined prompt modification, generating a second prompt, the second prompt including second open banking data; generating a second output based on the second prompt to the retrained LLM; and evaluating the second output with the objective function to determine a difference between the second output and the open banking objective.