ENTERPRISE-SCALE RISK ANALYSIS THROUGH KNOWLEDGE DISCOVERY USING NEURO-GENETIC DATA SCIENCE ALGORITHMS WITH INTELLIGENT DIMENSIONALITY REDUCTION

Abstract

[030] This invention introduces an Enterprise-Scale Risk Analysis through Knowledge Discovery Using Neuro-Genetic Data Science Algorithms with Intelligent Dimensionality Reduction. This invention provides an enterprise-scale risk analysis system utilizing neuro-genetic algorithms and intelligent dimensionality reduction for effective knowledge discovery. By combining neural networks with genetic optimization, the neuro-genetic algorithm enables scalable, adaptive risk analysis. Intelligent dimensionality reduction reduces data complexity, employing techniques such as principal component analysis, autoencoders, and clustering-based reduction to retain essential information. The system identifies, scores, and predicts risk factors in real-time, adapting to new data as it becomes available. This solution offers enterprises an accurate, scalable, and dynamic framework for proactive risk management. Accompanied Drawing [FIG. 1]

Specification

Description:[020] While the present invention is described herein by way of example using embodiments and illustrative drawings, those skilled in the art will recognize that the invention is not limited to the embodiments of drawing or drawings described and are not intended to represent the scale of the various components. Further, some components that may form a part of the invention may not be illustrated in certain figures, for ease of illustration, and such omissions do not limit the embodiments outlined in any way. It should be understood that the drawings and detailed description thereto are not intended to limit the invention to the particular form disclosed, but on the contrary, the invention is to cover all modifications, equivalents, and alternatives falling within the scope of the present invention as defined by the appended claims. As used throughout this description, the word "may" is used in a permissive sense (i.e. meaning having the potential to), rather than the mandatory sense, (i.e. meaning must). Further, the words "a" or "an" mean "at least one" and the word "plurality" means "one or more" unless otherwise mentioned. Furthermore, the terminology and phraseology used herein is solely used for descriptive purposes and should not be construed as limiting in scope. Language such as "including," "comprising," "having," "containing," or "involving," and variations thereof, is intended to be broad and encompass the subject matter listed thereafter, equivalents, and additional subject matter not recited, and is not intended to exclude other additives, components, integers or steps. Likewise, the term "comprising" is considered synonymous with the terms "including" or "containing" for applicable legal purposes. Any discussion of documents, acts, materials, devices, articles and the like is included in the specification solely for the purpose of providing a context for the present invention. It is not suggested or represented that any or all of these matters form part of the prior art base or are common general knowledge in the field relevant to the present invention.

[021] In this disclosure, whenever a composition or an element or a group of elements is preceded with the transitional phrase "comprising", it is understood that we also contemplate the same composition, element or group of elements with transitional phrases "consisting of", "consisting", "selected from the group of consisting of, "including", or "is" preceding the recitation of the composition, element or group of elements and vice versa.

[022] Data Collection and Preprocessing
The invention begins with a data collection module that aggregates information from various sources, such as financial records, operational metrics, and market data. The system's preprocessing module performs data cleaning, handling missing values, and normalizing variables to ensure compatibility across data types. This preprocessing step also identifies and addresses any anomalies or outliers to prevent distortions in risk analysis results.
[023] Intelligent Dimensionality Reduction Module
Once data is preprocessed, it undergoes dimensionality reduction through the intelligent dimensionality reduction (IDR) module. The IDR module includes:

Principal Component Analysis (PCA): PCA transforms the data into a lower-dimensional space by identifying the primary components that explain the most variance. This transformation reduces redundancy and highlights key features in the data, enabling more efficient processing in subsequent steps.

Autoencoders: An autoencoder-based neural network compresses data into a latent representation, preserving critical information while eliminating noise. This latent representation is particularly valuable for identifying non-linear patterns that traditional linear methods may miss.
Feature Selection and Clustering-Based Reduction: Feature selection methods, such as recursive feature elimination (RFE), identify the most impactful variables for risk analysis. Clustering techniques, like K-means, group similar data points to reduce dimensionality further, ensuring that only unique, relevant patterns are retained.

[024] Neuro-Genetic Algorithm Module
Following dimensionality reduction, the neuro-genetic algorithm module is activated. This component combines neural networks and genetic algorithms as follows:

Neural Network Training: The neural network is trained on the reduced data to learn complex relationships between risk factors. Using a series of neurons organized in layers, the neural network identifies non-linear patterns that may indicate potential risks.
Genetic Algorithm Optimization: Genetic algorithms optimize the neural network's architecture by simulating evolutionary processes. This involves creating multiple network configurations, selecting the best-performing models, and combining them through crossover and mutation to improve accuracy. The iterative process refines the model's parameters, ensuring optimal performance.
Iterative Refinement: The neuro-genetic algorithm iterates through multiple training and optimization cycles, with genetic algorithms adjusting network parameters after each cycle. This adaptive learning process enhances the model's accuracy and resilience to changes in the risk landscape.

[025] Risk Factor Identification and Prediction
With the neuro-genetic model optimized, the system identifies and scores individual risk factors based on their contribution to overall risk levels. The model generates predictive risk scores by analyzing patterns and dependencies in the data. By scoring risk factors, the system enables enterprises to identify high-impact risks and allocate resources effectively.

[026] Continuous Learning and Adaptation
As new data becomes available, the model undergoes continuous learning to remain relevant in dynamic environments. This learning process allows the model to adapt to shifts in data patterns, such as emerging risks or changing regulatory conditions. The adaptive capabilities ensure that risk insights are current and accurate, providing a robust foundation for decision-making.

[027] It is to be understood that the above description is intended to be illustrative, and not restrictive. For example, the above-discussed embodiments may be used in combination with each other. Many other embodiments will be apparent to those of skill in the art upon reviewing the above description.

[028] The benefits and advantages which may be provided by the present invention have been described above with regard to specific embodiments. These benefits and advantages, and any elements or limitations that may cause them to occur or to become more pronounced are not to be construed as critical, required, or essential features of any or all of the embodiments.

[029] While the present invention has been described with reference to particular embodiments, it should be understood that the embodiments are illustrative and that the scope of the invention is not limited to these embodiments. Many variations, modifications, additions and improvements to the embodiments described above are possible. It is contemplated that these variations, modifications, additions and improvements fall within the scope of the invention. , Claims:1.A method for enterprise-scale risk analysis using neuro-genetic algorithms, comprising: Training a neural network to identify patterns within high-dimensional data; Optimizing the neural network with a genetic algorithm that selects, crosses over, and mutates model configurations to achieve optimal performance.

2.The method of Claim 1, further comprising intelligent dimensionality reduction, wherein data is transformed into a lower-dimensional space using principal component analysis (PCA), autoencoders, or clustering-based reduction.

3.The method of Claim 2, wherein the neural network is adapted to process reduced-dimensional data, with the genetic algorithm refining the neural network's parameters based on evolving risk factors.

4.A system for real-time enterprise risk analysis comprising: A data preprocessing module for cleaning and normalizing high-dimensional data; An intelligent dimensionality reduction module to streamline data by selecting essential features; A neuro-genetic algorithm module that integrates a neural network with genetic optimization for adaptive learning.

5.The system of Claim 4, wherein the neuro-genetic algorithm is configured to score individual risk factors, enabling dynamic adjustment based on real-time data.

6.A method for continuous learning in risk analysis, comprising: Updating model parameters as new data is received; Adjusting feature selection and optimization criteria to adapt to changes in risk factors over time.

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