Textual Risk Identification With Responsible-Party Alert Routing

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Solution Overview

Problem

Existing risk management systems rely heavily on manual, event-driven processes for risk identification and remediation, often using single data sources, which are inefficient and time-consuming, especially in detecting operational risks such as fraud and cybersecurity vulnerabilities.

Innovation Solution

A computing system that includes a network interface, database, and processing circuit to generate affinitized data sets from textual inputs, identify responsible parties, and transmit early risk alerts based on contextual keywords and enrichment data, leveraging natural language processing to enhance risk detection and response.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Loss of time

If manual, event-driven processes are used for risk identification, then risk management can be performed with simple systems, but the mean time to identification and resolution of risks increases

Engineering Contradiction:

Improvemean time to identification and resolution of risksVSAvoidautomation level of risk identification processes

Core Design Contradiction:

Loss of timeVSExtent of automation

Solution Approach 1:

The patent replaces manual, mechanical risk identification processes with an automated computer-based system that uses natural language processing and machine learning algorithms to analyze text data, extract risk indicators, and generate alerts automatically, thereby reducing the mean time to risk identification and resolution

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Solution Approach 2:

The system enables self-service risk identification by automatically monitoring multiple data sources, performing textual analysis, and generating risk alerts without requiring manual intervention at each step, allowing the system to autonomously identify and report risks

Inventive Principle:

Principle #25Self-service

2Adaptability or versatility

If single data sources are used for risk identification, then the system complexity is reduced, but the ability to identify comprehensive risks and issues is limited

Engineering Contradiction:

Improverisk detection capabilityVSAvoidsystem architecture complexity

Core Design Contradiction:

Adaptability or versatilityVSDevice complexity

Solution Approach 1:

The patent implements a multi-functional system that can process and analyze multiple types of data sources (internal communications, external news, social media, regulatory filings) through a unified natural language processing platform, enabling comprehensive risk identification across diverse information sources while maintaining system coherence

Inventive Principle:

Principle #6Universality (Multi-functionality)

Solution Approach 2:

The system merges multiple data sources and analysis functions into an integrated risk identification platform that combines internal organizational data with external industry information, combining textual analysis, keyword matching, and machine learning capabilities in a single cohesive system

Inventive Principle:

Principle #5Merging (Combining)

3Measurement precision

If advanced textual analysis and data enrichment are implemented, then risk identification accuracy is improved, but the processing time and computational resources increase

Engineering Contradiction:

Improverisk identification accuracyVSAvoidprocessing speed

Core Design Contradiction:

Measurement precisionVSProductivity

Solution Approach 1:

The system performs preliminary actions by pre-processing and indexing text data from multiple sources, maintaining updated databases of risk indicators and contextual information, and pre-computing affinities between data elements, so that when risk analysis is needed, the system can quickly query and retrieve relevant information without performing complete analysis from scratch

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The patent segments the risk identification process into distinct modular components including text ingestion, natural language processing, keyword extraction, affinity calculation, and alert generation, allowing each component to be optimized independently and processed in parallel where possible, thereby improving both accuracy and processing efficiency

Inventive Principle:

Principle #1Segmentation

Data Source

PatentUS20260037896A1Systems and methods for enhanced risk identification based on textual analysis

Publication Date: 2026.02.05 FANNIE MAE

AI summary

A computing system includes a processing circuit including a processor and memory. The memory is structured to store instructions executable by the processor and cause the processing circuit to tokenize text, using at least one of first machine learning or first artificial intelligence, from a text file into a token. The instructions further cause the processing circuit to, based on a location of the token in the text file, determine, using at least one of second machine learning or second artificial intelligence, the token is indicative of a potential risk event, assign a responsibility score to each of a plurality of responsible parties, and, in response to matching the potential risk event to the responsibility score of a responsible party, transmit an early risk alert to the responsible party. The early risk alert includes a link to a webpage that receives data files associated with the potential risk event.