Heuristic Credit Risk Engine for Unstructured Data
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Solution Overview
Problem
Current customer service technologies face challenges in processing large volumes of unstructured data in real-time, leading to an unreasonable burden on customer service representatives and a lack of context, which affects the ability to provide personalized and effective customer service, especially in dynamic business conditions.
Innovation Solution
The implementation of heuristic algorithms that process unstructured data sets, including transaction records and natural language inputs, to generate credit scores, cross-selling recommendations, predict business impacts, and suggest financial literacy improvements, thereby improving data contextualization and adaptability over time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If customer service representatives manually process large volumes of unstructured data in real-time, then they can provide personalized customer service, but the burden on representatives becomes unreasonable and efficiency decreases
Solution Approach 1:
The patent introduces an automated data processing system that acts as an intermediary between unstructured data sources and customer service representatives. This system processes transaction records, natural language inputs, and other unstructured data to generate structured insights, credit scores, and recommendations, thereby reducing the manual burden on representatives while maintaining high productivity
Solution Approach 2:
The system enables self-service by automatically processing and analyzing data without requiring manual intervention from customer service representatives. The automated generation of credit scores, risk assessments, and personalized recommendations allows the system to serve itself in processing data, freeing representatives from manual data processing tasks
2Ease of manufacture
If organizations use off-the-shelf customer service software, then implementation cost is reduced, but the software lacks flexibility to react to changing business conditions
Solution Approach 1:
The patent implements a dynamic system that can adapt to changing business conditions through automated processing of real-time data. The system uses machine learning algorithms that continuously learn from new transaction records and natural language inputs, enabling it to flexibly respond to changing conditions without requiring costly software revisions, thus maintaining adaptability while using cost-effective off-the-shelf infrastructure
3Ease of manufacture
If customer service representatives work in outsourced environments, then operational costs are reduced, but representatives lack necessary context to provide high-level customer service
Solution Approach 1:
The system implements feedback mechanisms that automatically capture and process contextual information from transaction records and customer interactions. This feedback loop ensures that even in outsourced environments, representatives receive real-time contextual insights and recommendations, preventing information loss while maintaining cost-effective operations
Solution Approach 2:
The automated system acts as an intermediary that bridges the gap between outsourced representatives and the organization's data resources. It processes and delivers relevant contextual information to representatives, ensuring they have access to necessary insights despite working in distributed, cost-effective environments
Data Source
AI summary
A heuristic engine includes capabilities to collect an unstructured data set and a current business context to calculate a credit worthiness score. Providing a heuristic algorithm, executing within the engine, with the data set and the context may allow determination of predicted future contexts and recommend subsequent actions, such as assessing a credit risk of a customer transaction and reducing the risk of customer transactions by processing the available data. Such heuristic algorithms may learn from past data transactions and appropriate correlations with events and available data.