News Alert Baseline Modeling for Real-Time Entity Risk Signals
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Solution Overview
Problem
Current alerting systems are inefficient in querying and extracting baseline values from news articles to trigger real-time alerts for managing credit risk, particularly for large financial institutions with numerous clients, as they struggle with varying news relevance and database inefficiencies.
Innovation Solution
A method and system that utilize AI models to analyze news articles for important content, extract relevant data, generate time series, and compare signal quantities to baseline values to issue alerts when thresholds are exceeded, optimizing the storage and retrieval process.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If current alerting systems query and extract baseline values from news articles, then real-time alerts can be triggered, but the systems are inefficient and struggle with database queries over long baseline periods
Solution Approach 1:
The system pre-computes and stores baseline values for each entity in a dedicated baseline store before they are needed for alerting. This preliminary action eliminates the need for inefficient long-period database queries during real-time operations, as the baseline comparison can immediately use pre-calculated values.
Solution Approach 2:
The patent segments the data storage into two distinct components: a news article store for storing individual news articles and a separate baseline store for storing pre-computed baseline values. This segmentation allows optimized query paths for each function, improving overall system efficiency.
2Loss of information
If news articles are stored with full text and metadata, then comprehensive analysis is possible, but database queries become inefficient when filtering for specific entities over long periods
Solution Approach 1:
The system extracts essential baseline information from news articles and stores it separately in the baseline store. This extraction allows the system to maintain complete news articles for analysis while having quick-access baseline data for efficient querying without scanning through full article texts.
Solution Approach 2:
Baseline values are pre-computed from historical news data and stored in advance. When alerting is needed, the system queries these pre-computed baselines rather than performing time-consuming searches through archived news articles, significantly reducing query time.
3Reliability
If credit officers manually monitor news for each client, then all news can be reviewed, but the process is time-consuming and difficult to perform on an ongoing basis
Solution Approach 1:
The system automatically performs news monitoring, entity identification, baseline computation, and alert generation without requiring manual credit officer intervention. The automated system serves itself by continuously ingesting news, processing it through AI models, and generating alerts when thresholds are exceeded.
Solution Approach 2:
The system implements continuous feedback loops where news articles are constantly monitored, analyzed against baseline values, and alerts are generated when deviations occur. This automated feedback mechanism ensures ongoing monitoring without manual effort while maintaining completeness.
4Measurement precision
If AI models analyze each news article to determine importance and extract signal data, then alert relevance is improved, but processing complexity increases
Solution Approach 1:
AI models perform preliminary analysis of news articles to extract entities, determine importance, and compute baseline values in advance. This preliminary processing organizes data into structured formats that simplify subsequent alerting operations, reducing the complexity of real-time decision-making.
Data Source
AI summary
A method and a system for issuing news alerts with respect to an entity are provided. The method includes: receiving a news article that relates to at least one entity; extracting, from the input information, signal data; generating a news article electronic document including the news article and the signal data; extracting, from the news article electronic document, a signal quantity for each entity of the at least one entity; generating a time series of signal quantities for the news article; storing each of the news article electronic document and the time series in a database; transmitting the time series to a news alerting model that compares the signal quantity of each entity from the time series to a respective baseline value of the corresponding entity; and generating an alert message when the signal quantity exceeds the baseline value by a predetermined threshold value.