Description:Field of the Invention

The invention relates to AI-based enterprise risk management systems, particularly to deep learning architectures for predicting, classifying, and mitigating organizational risks in dynamic environments.

Background
The background description includes information that may be useful in understanding the present invention. It is not an admission that any of the information provided herein is prior art or relevant to the presently claimed invention, or that any publication specifically or implicitly referenced is prior art.
Risk management in enterprises has historically relied on heuristic models, rule-based assessments, and static risk registers maintained by compliance officers or risk analysts. These legacy frameworks typically depend on periodic audits, qualitative scoring matrices, and manual oversight to evaluate risk exposure and develop mitigation strategies. Although such methods provide basic guidance, they lack the granularity, scalability, and predictive power needed to navigate today’s fast-changing business landscape.
Conventional enterprise risk management (ERM) platforms often integrate data from financial records, supply chain reports, and compliance checklists. However, they are usually siloed, reactive, and incapable of real-time risk anticipation. Moreover, traditional statistical risk modeling approaches assume stationarity and linearity in underlying data, which proves ineffective when dealing with non-linear, high-dimensional, and time-dependent enterprise variables. Consequently, organizations remain exposed to hidden risks, slow response cycles, and missed early-warning signals.
While machine learning has been employed in isolated use cases such as fraud detection or credit scoring, comprehensive AI-driven systems for enterprise-wide risk prediction and response remain underdeveloped. Existing ML models often operate as black boxes with minimal interpretability, limited adaptation to novel risks, and no integration with real-time prescriptive decision engines. They also fail to retrain based on feedback or evolving operational contexts.
There exists a need for a scalable, adaptive, and explainable enterprise risk management platform that integrates data from diverse organizational and external sources, uses deep learning to model complex risk dynamics, and recommends actionable mitigation strategies. Such a platform must support continuous learning from feedback loops, scenario simulation, and explainable decision guidance. The present invention addresses these long-standing limitations through a unified neural architecture for dynamic enterprise risk prediction and management.

All references, including publications, patent applications, and patents, cited herein are hereby incorporated by reference to the same extent as if each reference were individually and specifically indicated to be incorporated by reference and were set forth in its entirety herein.
Summary
Various objects, features, and advantages of the disclosed subject matter can be more fully appreciated with reference to the following detailed description of the disclosed subject matter when considered in connection with the following drawings, in which like reference numerals identify like elements.
The invention relates to AI-based enterprise risk management systems, particularly to deep learning architectures for predicting, classifying, and mitigating organizational risks in dynamic environments.

The present disclosure provides an artificial intelligence-based system that applies deep learning techniques to the domain of enterprise risk management, offering capabilities for real-time risk prediction, classification, and mitigation. The system architecture is modular and comprises five core components that operate in a closed-loop configuration to ensure continuous adaptation and alignment with evolving organizational conditions.
The process begins with a multi-source data ingestion module that collects structured and unstructured data from diverse channels including internal business systems, financial ledgers, supply chains, regulatory archives, and external feeds such as news media, market data, and sentiment analytics. This data is harmonized and passed to a risk feature engineering module, which constructs multidimensional vectors capturing latent risk signals, process dynamics, and event correlations. Feature reduction techniques are applied to eliminate noise and emphasize high-impact attributes.
The feature vectors are input into a neural risk prediction engine. This engine employs deep learning architectures such as CNNs for local feature extraction, RNNs for sequential modeling, and transformers for long-range dependency capture. The engine outputs include risk classification labels, event likelihood forecasts, and severity ratings. These outputs are provided to a prescriptive mitigation engine, which evaluates intervention strategies using multi-objective optimization and generates ranked recommendations for risk containment, escalation, or resource deployment.
The system further includes a feedback adaptation layer, which collects outcome data from executed strategies and uses performance deviations to retrain the prediction and recommendation components. This enables continual learning and enhances long-term model reliability. Optionally, the system supports simulation of hypothetical risk scenarios, dashboard interfaces with explainable decision visualizations, and secure deployment over federated infrastructure. The invention thus delivers a comprehensive, adaptive, and intelligent solution for modern enterprise risk management.

Brief Description of the Drawings

The features and advantages of the present disclosure would be more clearly understood from the following description taken in conjunction with the accompanying drawings in which:
FIG. 1 is a system architecture diagram illustrating the integrated structure of the AI-based enterprise risk management system, including major functional modules for data ingestion, risk feature engineering, neural risk prediction, prescriptive mitigation, and feedback adaptation.
FIG. 2 is a data flow diagram showing the transformation of raw internal and external enterprise data through structured feature processing, prediction modeling, and intervention generation in a linear risk intelligence pipeline.
FIG. 3 is a sequence diagram illustrating the chronological interaction between system components during a full risk prediction and response cycle, from data intake to feedback-driven retraining.
Detailed Description
The following is a detailed description of exemplary embodiments to illustrate the principles of the invention. The embodiments are provided to illustrate aspects of the invention, but the invention is not limited to any embodiment. The scope of the invention encompasses numerous alternatives, modifications and equivalent; it is limited only by the claims.
In view of the many possible embodiments to which the principles of the present discussion may be applied, it should be recognized that the embodiments described herein with respect to the drawing figures are meant to be illustrative only and should not be taken as limiting the scope of the claims. Therefore, the techniques as described herein contemplate all such embodiments as may come within the scope of the following claims and equivalents thereof.
The detailed description is described with reference to the accompanying figures. In the figures, the left-most digit(s) of a reference number identifies the figure in which the reference number first appears. The use of the same reference numbers in different instances in the description and the figures may indicate similar or identical items.
Pursuant to the "Detailed Description" section herein, whenever an element is explicitly associated with a specific numeral for the first time, such association shall be deemed consistent and applicable throughout the entirety of the "Detailed Description" section, unless otherwise expressly stated or contradicted by the context.
The invention relates to AI-based enterprise risk management systems, particularly to deep learning architectures for predicting, classifying, and mitigating organizational risks in dynamic environments.

Pursuant to the "Detailed Description" section herein, whenever an element is explicitly associated with a specific numeral for the first time, such association shall be deemed consistent and applicable throughout the entirety of the "Detailed Description" section, unless otherwise expressly stated or contradicted by the context.
The present invention provides an artificial intelligence-driven system configured to enhance enterprise risk management through deep learning-based predictive modeling, classification, and mitigation. The architecture includes a modular integration of data acquisition, feature generation, deep neural inference, prescriptive optimization, and adaptive learning feedback layers, all orchestrated to deliver continuous and intelligent enterprise risk oversight.
The system initiates with a multi-source data ingestion module. This component is responsible for acquiring data from heterogeneous sources within and beyond the enterprise boundary. These include financial systems, compliance databases, supplier logs, employee records, and third-party information feeds such as government publications, macroeconomic datasets, social sentiment analysis, and market news crawlers. The ingestion layer formats, aligns, and time-synchronizes data streams to create a consistent representation across risk domains.
The harmonized data is forwarded to a risk feature engineering module, which applies techniques such as word embeddings for unstructured data, time-series decomposition for sequential data, and graph-based analysis for inter-entity relationships. The module generates high-dimensional feature vectors capturing potential indicators of risk, including transaction irregularities, workflow anomalies, stakeholder exposure, or control weaknesses. Dimensionality reduction mechanisms such as principal component analysis or variational autoencoding are used to retain signal quality while improving computational tractability.
These feature vectors are routed to the neural risk prediction engine. This engine comprises multiple deep learning layers, including convolutional layers for identifying spatial patterns across enterprise data matrices, recurrent layers (e.g., LSTM or GRU) for capturing evolving event dependencies, and attention mechanisms for identifying feature relevance. Additionally, transformer encoders are utilized to model long-term and non-linear risk interactions. The output includes probabilistic risk scores, category classifications (e.g., operational, regulatory, reputational), and severity projections. These are structured into interpretable formats suitable for downstream decision-making.
Next, the prescriptive mitigation engine receives the risk output set and applies optimization logic to formulate targeted responses. This may include escalation to compliance managers, reallocation of mitigation budgets, temporary process suspensions, or resource buffer provisioning. A multi-objective optimizer considers cost, urgency, regulatory exposure, and likelihood of success when ranking interventions. Optional simulation modules allow evaluation of these strategies under varying market conditions, policy changes, or internal capacity thresholds.
Executed strategies are monitored by the feedback adaptation layer, which gathers post-intervention metrics and compares them to pre-intervention predictions. This layer computes learning gradients and updates neural weights through reinforcement learning techniques or supervised backpropagation where labeled data is available. The layer also logs new outcomes into training sets, enabling the continual evolution of the predictive and prescriptive modules. This adaptive mechanism ensures the system remains effective under novel risk emergence, structural changes, or data distribution shifts.
In one embodiment, the system is deployed in a multinational retail organization to monitor supply chain risk. It integrates supplier reliability scores, logistics disruptions, geopolitical risk indexes, and invoice anomalies. The system predicts potential delivery disruptions and recommends alternate routing or supplier diversification strategies.
In another embodiment, the system is used by a financial institution to predict regulatory non-compliance events by analyzing employee emails, policy acknowledgments, transaction logs, and audit trail gaps. The mitigation engine suggests proactive training modules, transaction reviews, or policy revisions.
In a third embodiment, the system serves an energy company to manage infrastructure and safety risks by aggregating sensor readings, maintenance records, weather data, and incident reports. The prediction engine detects equipment failure risk, while the prescriptive module schedules preventive maintenance and dispatches alerts.
Each embodiment benefits from the system's capability to render explainable AI outputs through dashboards that display contributing features, risk confidence intervals, and simulated outcomes. The architecture supports distributed training and inference, allowing enterprise-wide integration across global business units. The invention thus enables a shift from reactive, manual risk management to proactive, AI-augmented decision intelligence, reducing risk exposure and enhancing organizational resilience.
FIG. 1 illustrates a system architecture diagram depicting the core structural components of the disclosed artificial intelligence-based enterprise risk management system. The architecture includes five principal modules arranged in an integrated processing pipeline. At the data layer, the system begins with a multi-source data ingestion module configured to collect both internal and external data streams. These inputs originate from financial ledgers, compliance systems, supply chain records, employee communications, and external intelligence sources such as real-time news feeds, regulatory publications, and social sentiment indices. The data ingestion module ensures that disparate data sources are normalized, timestamped, and semantically aligned for downstream modeling.
The normalized data is forwarded to the risk feature engineering module, which extracts relevant attributes indicative of latent risk signals. This module performs dimensionality reduction, relationship extraction, and entity mapping to generate optimized feature vectors. These vectors are input into the neural risk prediction engine. This engine houses a stack of deep neural architectures that include convolutional layers for spatial risk pattern extraction, recurrent layers for capturing sequential dependencies, and transformer encoders for modeling long-range temporal and semantic interdependencies across risk indicators.
The risk prediction engine outputs event probabilities, categorical risk classifications, and severity confidence scores. These outputs are processed by the prescriptive mitigation engine, which synthesizes actionable recommendations for intervention. The engine ranks mitigation options using a multi-objective utility function and communicates outputs to operational interfaces or dashboards for execution. The feedback adaptation layer monitors real-world results of mitigation deployments, calculates deviations from predicted outcomes, and updates both the neural model and mitigation logic accordingly. This closed-loop system architecture allows continuous refinement of enterprise risk forecasting and response intelligence.
FIG. 2 depicts a data flow diagram showing how raw data is transformed into actionable enterprise risk responses using the system. The process begins with structured data such as compliance records, supply chain metrics, and financial indicators being collected in parallel with unstructured inputs like social media signals and regulatory news articles. These are routed to the ingestion module, which harmonizes and tags them for semantic consistency. Output from the ingestion phase is fed into the feature engineering pipeline, where high-impact indicators are selected, encoded, and embedded into structured vectors.
These feature vectors are passed into the neural prediction module, which applies deep learning models to assess event likelihoods and risk scores. Prediction outputs include not only classification labels (such as fraud risk, compliance risk, or operational failure), but also probability values and confidence metrics. The prescriptive engine then receives these outputs and performs scenario analysis, recommending targeted interventions based on the predicted impact and cost-benefit trade-offs. The selected response is executed via connected enterprise systems, and the outcomes are monitored to generate performance feedback. This feedback is looped back to retrain the prediction engine, enabling system evolution over time.
FIG. 3 illustrates a sequence diagram detailing the chronological interaction among the system’s components during a single risk prediction and mitigation cycle. The process starts with the data ingestion module initiating a pull request from internal databases and subscribing to external data feeds. Once the ingestion module receives and formats the data, it notifies the feature engineering unit, which then processes and transmits feature vectors to the neural risk prediction engine.
Upon receiving the processed features, the prediction engine initiates a forward pass through its neural layers, generating classified risk outputs and probability scores. These predictions are immediately transferred to the prescriptive engine, which invokes its optimization algorithm to compute ranked mitigation strategies. After selection, the recommendation is routed to enterprise execution systems and is simultaneously logged. A confirmation signal is sent back to the feedback layer once the intervention is implemented. The feedback layer monitors actual results and compares them with the prediction, triggering a retraining routine when prediction accuracy falls below a defined confidence threshold. This orchestrated sequence ensures accurate, explainable, and continuously refined enterprise risk management.
Further, while operations are depicted in a particular order, this should not be understood as requiring that such operations be performed in the particular order shown or in sequential order, or that all illustrated operations be performed, to achieve desirable results. In certain circumstances, multitasking and parallel processing may be advantageous. Likewise, while several specific implementation details are contained in the above discussions, these should not be construed as limitations on the scope of the subject matter described herein, but rather as descriptions of features that may be specific to particular embodiments. Certain features that are described in this specification in the context of separate embodiments can also be implemented in combination in a single embodiment. Conversely, various features that are described in the context of a single embodiment can also be implemented in multiple embodiments separately or in any suitable sub-combination. Moreover, although features may be described above as acting in certain combinations and even initially claimed as such, one or more features from a claimed combination can in some cases be excised from the combination, and the claimed combination may be directed to a sub-combination or variation of a sub-combination.
The term “memory,” as used herein relates to a volatile or persistent medium, such as a magnetic disk, or optical disk, in which a computer can store data or software for any duration. Optionally, the memory is non-volatile mass storage such as physical storage media. Furthermore, a single memory may encompass and in a scenario wherein computing system is distributed, the processing, memory and/or storage capability may be distributed as well.
Throughout the present disclosure, the term ‘server’ relates to a structure and/or module that include programmable and/or non-programmable components configured to store, process and/or share information. Optionally, the server includes any arrangement of physical or virtual computational entities capable of enhancing information to perform various computational tasks.
Throughout the present disclosure, the term “network” relates to an arrangement of interconnected programmable and/or non-programmable components that are configured to facilitate data communication between one or more electronic devices and/or databases, whether available or known at the time of filing or as later developed. Furthermore, the network may include, but is not limited to, one or more peer-to-peer network, a hybrid peer-to-peer network, local area networks (LANs), radio access networks (RANs), metropolitan area networks (MANS), wide area networks (WANs), all or a portion of a public network such as the global computer network known as the Internet, a private network, a cellular network and any other communication system or systems at one or more locations.
Throughout the present disclosure, the term “process”* relates to any collection or set of instructions executable by a computer or other digital system so as to configure the computer or the digital system to perform a task that is the intent of the process.
Throughout the present disclosure, the term ‘Artificial intelligence (AI)’ as used herein relates to any mechanism or computationally intelligent system that combines knowledge, techniques, and methodologies for controlling a bot or other element within a computing environment. Furthermore, the artificial intelligence (AI) is configured to apply knowledge and that can adapt it-self and learn to do better in changing environments. Additionally, employing any computationally intelligent technique, the artificial intelligence (AI) is operable to adapt to unknown or changing environment for better performance. The artificial intelligence (AI) includes fuzzy logic engines, decision-making engines, preset targeting accuracy levels, and/or programmatically intelligent software.
Claims

I/We Claims

Claim 1.
An artificial intelligence-based enterprise risk management system using deep learning architectures to predict, classify, and mitigate business risks, the system comprising:
a multi-source data ingestion module configured to acquire, standardize, and process heterogeneous enterprise data from internal systems including finance, operations, compliance, supply chain, and external feeds such as news reports, market data, and regulatory bulletins;
a risk feature engineering module operatively connected to said data ingestion module, said module being configured to extract latent risk indicators, generate structured feature vectors, and apply dimensionality reduction to emphasize high-impact variables across risk dimensions;
a neural risk prediction engine configured to receive said feature vectors, said engine comprising deep learning architectures including convolutional neural networks, recurrent neural networks, and transformer-based encoders trained to predict event likelihoods, classify risk categories, and compute severity scores;
a prescriptive mitigation engine operatively linked to said neural risk prediction engine, said engine being configured to recommend risk response strategies, allocate mitigation resources, and simulate the impact of potential interventions based on predicted risk distributions and organizational priorities;
and a feedback adaptation layer configured to monitor the outcomes of executed mitigation actions, compute discrepancies between predicted and realized risk occurrences, and update model parameters and inference rules to improve future prediction accuracy and strategy alignment.
Claim 2.
The system of claim 1, wherein said data ingestion module includes a real-time stream processing component adapted to continuously collect, filter, and align time-sensitive risk signals from volatile data sources including stock indices, regulatory news feeds, and social sentiment trackers.
Claim 3.
The system of claim 1, wherein said neural risk prediction engine includes an attention-based transformer layer configured to learn interdependencies between sequential business events and evolving external risk indicators.
Claim 4.
The system of claim 1, wherein said risk feature engineering module incorporates a knowledge graph generator configured to map relationships between risk entities, causative factors, and control vectors.
Claim 5.
The system of claim 1, wherein said prescriptive mitigation engine comprises a multi-objective optimization module that prioritizes risk responses based on cost, urgency, regulatory impact, and strategic importance.
Claim 6.
The system of claim 1, wherein said feedback adaptation layer utilizes reinforcement learning techniques to refine action-value mappings and mitigate response strategy drift over iterative cycles.
Claim 7.
The system of claim 1, further comprising a risk scenario simulation interface configured to construct and evaluate hypothetical risk events using Monte Carlo or Bayesian sampling techniques.
Claim 8.
The system of claim 1, wherein said neural risk prediction engine is trained on labeled historical risk event datasets, augmented synthetic anomalies, and domain-specific expert annotations to enhance classification generalizability.
Claim 9.
The system of claim 1, wherein said prescriptive mitigation engine provides real-time risk dashboards with intervention suggestions, risk thresholds, and confidence bands rendered in explainable AI formats.

/
DATED 07 July 2025 PALLAVI SINHA
IN/PA- 4068
AGENT FOR THE APPLICANT

Deep Learning Approaches to Risk Management in Enterprises: Using Neural Architectures to Predict, Classify, and Mitigate Business Risks

A deep learning-based enterprise risk management system is disclosed. The system includes a data ingestion module, a risk feature engineering module, and a neural risk prediction engine configured to forecast and classify risks using deep neural architectures. A prescriptive mitigation engine generates intervention strategies based on predicted risks, while a feedback adaptation layer updates model parameters based on observed outcomes. The system supports scenario simulation, explainable output rendering, and secure enterprise-wide deployment. It enables real-time, AI-driven risk identification, prioritization, and mitigation in complex business environments.

, C , Claims:I/We Claims

Claim 1.
An artificial intelligence-based enterprise risk management system using deep learning architectures to predict, classify, and mitigate business risks, the system comprising:
a multi-source data ingestion module configured to acquire, standardize, and process heterogeneous enterprise data from internal systems including finance, operations, compliance, supply chain, and external feeds such as news reports, market data, and regulatory bulletins;
a risk feature engineering module operatively connected to said data ingestion module, said module being configured to extract latent risk indicators, generate structured feature vectors, and apply dimensionality reduction to emphasize high-impact variables across risk dimensions;
a neural risk prediction engine configured to receive said feature vectors, said engine comprising deep learning architectures including convolutional neural networks, recurrent neural networks, and transformer-based encoders trained to predict event likelihoods, classify risk categories, and compute severity scores;
a prescriptive mitigation engine operatively linked to said neural risk prediction engine, said engine being configured to recommend risk response strategies, allocate mitigation resources, and simulate the impact of potential interventions based on predicted risk distributions and organizational priorities;
and a feedback adaptation layer configured to monitor the outcomes of executed mitigation actions, compute discrepancies between predicted and realized risk occurrences, and update model parameters and inference rules to improve future prediction accuracy and strategy alignment.
Claim 2.
The system of claim 1, wherein said data ingestion module includes a real-time stream processing component adapted to continuously collect, filter, and align time-sensitive risk signals from volatile data sources including stock indices, regulatory news feeds, and social sentiment trackers.
Claim 3.
The system of claim 1, wherein said neural risk prediction engine includes an attention-based transformer layer configured to learn interdependencies between sequential business events and evolving external risk indicators.
Claim 4.
The system of claim 1, wherein said risk feature engineering module incorporates a knowledge graph generator configured to map relationships between risk entities, causative factors, and control vectors.
Claim 5.
The system of claim 1, wherein said prescriptive mitigation engine comprises a multi-objective optimization module that prioritizes risk responses based on cost, urgency, regulatory impact, and strategic importance.
Claim 6.
The system of claim 1, wherein said feedback adaptation layer utilizes reinforcement learning techniques to refine action-value mappings and mitigate response strategy drift over iterative cycles.
Claim 7.
The system of claim 1, further comprising a risk scenario simulation interface configured to construct and evaluate hypothetical risk events using Monte Carlo or Bayesian sampling techniques.
Claim 8.
The system of claim 1, wherein said neural risk prediction engine is trained on labeled historical risk event datasets, augmented synthetic anomalies, and domain-specific expert annotations to enhance classification generalizability.
Claim 9.
The system of claim 1, wherein said prescriptive mitigation engine provides real-time risk dashboards with intervention suggestions, risk thresholds, and confidence bands rendered in explainable AI formats.

/
DATED 07 July 2025 PALLAVI SINHA
IN/PA- 4068
AGENT FOR THE APPLICANT

Deep Learning Approaches to Risk Management in Enterprises: Using Neural Architectures to Predict, Classify, and Mitigate Business Risks