AI-Driven Financial Compliance

A computer platform that helps online service providers comply with government regulations by automating understanding of regulation impacts and recommended control implementations. The platform ingests regulations, extracts relevant obligations, identifies affected software processes, recommends controls, and presents an explainable visual interface to illustrate the determination paths. This provides an intelligent and transparent way to assess and implement regulation compliance changes in software processes.

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Determining if an entity is potentially violating regulatory requirements using a machine learning model. The system collects data from multiple databases, processes it through a machine learning model to identify potential regulatory violations, and notifies the entity of potential issues. This allows proactive monitoring and remediation of regulatory compliance across disparate databases.

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Automatically mapping compliance controls from one framework to another using natural language processing. The method involves training a supervised machine learning model to determine correspondences between compliance controls based on their feature sets. The model is fed text-based features of reference and custom controls or questions to predict matching sets. By leveraging NLP to learn relationships between control descriptions, it can efficiently map compliance requirements between standards.

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Generating a dynamic compliance knowledge graph to find an optimum route to arrive at a target node for compliance purposes. The method involves building a knowledge graph from text in compliance documents, calculating metrics between nodes and a query vector, and providing compliance paths to target nodes based on the metrics. The knowledge graph is created by generating nodes from text entities, edges from relationships, and embedding the query vector into nodes to compare against.

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Intelligently applying operational rules to operational data using a computing processor. The method involves extracting and formalizing operational rules from knowledge graphs and domain expertise to identify non-compliant data. This allows automated filtering of operational data like claims to flag potential violations of policies and regulations. The rules are derived from knowledge graphs representing policy regulations and structured operational data. The rules can be learned and validated to transform policy knowledge into executable rules for compliance checking.

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Using machine learning to efficiently analyze financial data and identify exceptions to financial algorithms. The method involves applying dynamic, interdependent algorithms to financial data using ML classifiers trained on labeled data. The classifiers classify outcomes as algorithm compliant, potentially non-compliant, or non-compliant. The ML allows efficient analysis of large datasets with complex algorithms by reducing computation and memory requirements. The classifier identifies latent anomalies in the financial data.

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Automatically generating compliance documents for filing on behalf of registered entities using AI and machine learning to efficiently process raw financial data and generate interim outputs based on rules. The interim outputs have interactive fields for user input and linked formulas. The system verifies the outputs, examines responses, and generates the final compliance document.

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A financial risk management method using machine learning to provide accurate, real-time and adaptable risk assessment and management for financial institutions. The method involves collecting financial data from various sources like APIs, data subscriptions, social media, news, databases and real-time streams. The collected data is then analyzed using machine learning models to detect financial risks. The models are trained on historical data and continuously updated as new data is received. The method also allows flexible configuration of the models and thresholds to adapt to changing market conditions.

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Automatically identifying sub-funds within umbrella funds that are effectively traded as individual investment funds, to streamline KYC procedures for compliance. The method involves using AI to analyze entity records and extract umbrella names. For entities with high confidence of association, relationships are created between the umbrella and sub-fund LEIs. This allows KYC to be done on the umbrella as a whole instead of each sub-fund. For low confidence entities, further analysis using prospectus information is done to confirm no sub-fund association.

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Knowledge graphs that accurately represent complex relationships between entities, such as regulatory compliance functions, credit worthiness, and legal status, by using recursive, multi-dimensional functions instead of fixed Boolean connections. The knowledge graphs are generated by associating requirements with relationships that define iterative functions to determine the relationship state based on conditions, parameters, and factors. These functions can be complex, conditional, temporal, probabilistic, etc. The graphs are stored in memory and queries are automatically resolved by calculating the recursive functions.

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Determining financial risk based on self-supervised natural language extraction from unstructured data sets like long form financial narratives. The method involves converting unstructured financial narratives into condensed financial risk narratives using self-supervised natural language processing. A tokenization library is determined for the unstructured data and used to generate the condensed narratives. Security scores indicative of financial risk are calculated for the condensed narratives. If a score exceeds a threshold, security actions are executed. This allows autonomous risk assessment from unstructured financial data without manual summarization.

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Automatic iterative optimization method and system for intelligent risk control approval strategies in financial applications. The method involves processing customer data, classifying it into test and control groups, optimizing risk strategies using the test group, and iteratively refining the strategies based on the optimization results. The system includes modules for customer data processing, risk strategy approval, and database storage. It uses techniques like recurrent neural networks and adaptive classification to dynamically optimize risk strategies as market conditions change.

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Financial fraud risk screening system using standardized financial statements, indicator systems, and natural language processing to intelligently and precisely monitor customer financial data for fraud risks. The method involves mapping original financial statement items to standardized items, analyzing the standardized data using indicator systems, and using natural language reasoning models to identify potential fraud risks based on financial scenarios.

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Method and system for monitoring and prompting financial risk of customers based on unstructured key information extracted from their financial annotations. The method involves using natural language intelligence to identify important financial information from customer financial statement notes, instead of just structured data. It leverages a knowledge base of financial rules, interpretations, and standards to analyze the unstructured annotations and flag potential risks.

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Risk information collection system and architecture for automated and intelligent risk decision-making in financial transactions. The system allows building customized risk rules and decision-making processes using an intelligent decision-making platform. It converts source transaction data into standardized format, processes it using the customized rules, and returns risk decisions. This separates the decision-making logic from the data processing and storage, enabling more efficient and scalable risk management compared to direct database queries and custom code.

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Artificial intelligence-enhanced financial risk management system that leverages AI, machine learning, data analytics, and financial expertise to provide advanced risk assessment, prediction, and management capabilities to financial institutions, organizations, and individual investors. The system acquires financial data from diverse sources, uses AI algorithms to process and analyze it in real-time, identifies patterns, trends, and anomalies, assesses various types of financial risks like market, credit, operational, geopolitical, regulatory, and sentiment risks, and provides real-time risk assessments, graphical representations, and actionable recommendations to financial professionals and investors. The AI component uses techniques like natural language processing and sentiment analysis to monitor news and social media for sentiment shifts. The system also optimizes investment portfolios based on risk assessments.

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System to automatically suggest and facilitate adjustment of incorrect values in compliance reports to prevent errors and violations. The system uses AI to analyze past reports and trends to identify values that are likely incorrect. It then suggests adjusted values and allows the user to review and accept the changes. The system also provides trend analysis and visualization to help users understand the suggested adjustments. This reduces manual review time and errors compared to line-by-line checking.

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Assessment tool for consistently applying complex regulations across an organization by breaking them down into a series of questions presented through a user interface. The tool converts regulation rules into questions that represent a dynamic decision tree. It captures and stores user responses to provide visibility into how decisions are made. This helps ensure consistent interpretation and application of regulations. The tool dynamically generates an audit trail of each question and answer combination.

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Automating financial data auditing using AI and graph theory to replace manual review and improve accuracy and efficiency. The system involves three engines: an expression engine to determine audit parameters, a rule engine to generate audit rules, and a graph theory audit engine to create directed acyclic graphs for auditing. The expression engine extracts audit relationships based on AI and sends them to the rule engine to generate audit rules. The graph theory engine then creates directed acyclic graphs from rules and logic data for auditing. This allows automated review of complex financial data using AI-generated audit graphs.

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Data processing method, device, and storage medium for avoiding repeated processing and verification of financial data to improve management efficiency. The method involves processing financial data once to derive data used by multiple regulatory reporting applications. The derived data is then grouped based on reporting requirements and each app reads from its group to submit. This avoids duplicate processing and verification.

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