Keyword Frequency Analysis for Operational Risk Detection
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Solution Overview
Problem
Current keyword analysis systems are inadequate for identifying potential issues in enterprises by failing to effectively analyze keyword frequencies across different dimensions and sets of records, leading to missed indications of operational risks and losses.
Innovation Solution
A keyword frequency analysis system that determines the frequency of keywords in sets of records, calculates expected frequencies based on observed data, and compares actual and expected frequencies to identify overrepresentation or underrepresentation, generating reports for display to administrators.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If current keyword analysis systems are used, then basic keyword counting is performed, but the systems fail to identify overrepresentation or underrepresentation of keywords across different dimensions, leading to missed operational risks
Solution Approach 1:
The system segments the analysis by dividing records into different dimensions (e.g., business units, time periods, record types) and performs keyword frequency analysis separately for each dimension. This allows comparison of keyword frequencies across dimensions to identify overrepresentation or underrepresentation, thereby improving measurement precision without requiring a complete redesign of the system.
Solution Approach 2:
The system introduces dimensional analysis as an additional layer beyond simple keyword counting. By analyzing keyword frequencies across multiple dimensions simultaneously and comparing them, the system identifies patterns and anomalies that single-dimensional analysis would miss, thereby improving risk detection accuracy.
2Reliability
If comprehensive keyword frequency analysis across all dimensions is performed, then operational risks are identified, but computational resources are consumed
Solution Approach 1:
The system performs partial analysis by focusing on comparing keyword frequencies across dimensions rather than analyzing every possible keyword combination in depth. It identifies overrepresentation or underrepresentation by comparing actual frequencies against expected frequencies based on dimensional distributions, achieving reliable risk identification with reduced computational effort compared to exhaustive analysis.
Solution Approach 2:
The system changes the analytical approach from absolute frequency counting to relative frequency comparison. By calculating expected frequencies based on dimensional parameters and comparing actual frequencies against these expectations, the system identifies anomalies more efficiently, improving reliability while reducing computational resource consumption.
Data Source
AI summary
According to embodiments of the present disclosure, a keyword frequency analysis system stores a plurality of sets of records. Each set of records may be associated with a dimension and may comprise a first keyword and a second keyword. The system may also receive the plurality of sets of records, determine a frequency of the first keyword in each set of records and determine a frequency of the second keyword in each set of records. The system may further determine an expected frequency of the first keyword in a first set of records associated with a first dimension, based on the frequency of the first keyword and the frequency of the second keyword. The system also compares the frequency of the first keyword and the expected frequency and, based on the comparison, determines whether the first keyword is either overrepresented or underrepresented in the first set of records.