Financial Regulatory Compliance with AI

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 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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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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Machine learning techniques for reviewing alerts in regulatory settings like anti-money laundering (AML) that provide insights and a flexible interface to improve efficiency and accuracy. The techniques involve calculating context-aware representations of entities like customers and transactions using self-supervised graph neural networks. These representations can be used to derive insights like clustering, anomaly scoring, and period detection to aid in AML alert review. The representations capture entity behavior based on surrounding context from a bipartite graph.

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Overlay network that provides AI-based compliance and preference management for legacy customer outreach platforms. The system uses AI templates to automatically generate compliant communication plans based on customer preferences. It transforms communications on legacy platforms like CRMs, dialers, and ACDs according to the AI templates. This allows retrofitting non-AI systems with compliance and preference capabilities. The system also stores scrubbed lists with channel preferences for use by non-AI platforms.

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Enhanced mapping and classification of transaction data to simplify regulatory reporting for organizations with presence in multiple jurisdictions. The technique uses feature identification and machine learning algorithms to automatically map source transaction data columns to target column structures defined by different jurisdictions. It also classifies rows based on features to ensure accurate reporting. Users can validate the mappings and classifications.

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Automatically identifying counterpart entities from transaction strings in financial accounts when the counterpart entity is not explicitly listed. The method involves using machine learning models to translate the transaction strings into identified counterpart entities. The models are trained on manually mapped transactions and then retrained based on their predictions to improve accuracy. This allows automating the identification of counterpart entities from transaction strings that don't explicitly list them.

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Maintaining ethical Artificial Intelligence (AI) in fraud detection by generating representative training datasets that mitigate bias in AI models. The method involves aggregating financial transactions by non-sensitive PII parameters, analyzing distributions, and sampling based on configurable rules to balance low-frequency values. This ensures fair representation of groups in training data. The AI model is then trained on the balanced dataset to reduce bias in predictions.

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Automatically extracting compliance profiles for organizations using natural language processing (NLP) techniques. The method involves extracting text data from sources describing compliance named entities, determining compliance profiles based on the extracted text, and identifying features like domains, types, locations, etc. A machine learning classifier is trained on these features to further enhance the compliance profile extraction.

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Estimating stability of dynamic datasets over time to predict financial stability of customers based on observable attributes. The method involves capturing dynamic data like financial transactions into vectors, mapping them to directionally similar template states, generating features from the sequence of template states, and applying a classification algorithm to identify trends in the underlying dynamic data. This allows estimating the stability of a customer's finances over time using observable data rather than just predicting creditworthiness at a single point in time.

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Data analysis platform using cloud-native technologies for generating intelligent alerts from financial supervision data. The platform extracts supervision data from sources, creates a data model, applies qualitative and quantitative analysis, integrates AI/ML for outlier detection, and generates alerts. It provides customizable, scalable, and cloud-native alerting with features like similarity, pertinence, and risk metrics.

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Detecting financial crimes like money laundering using machine learning models that leverage network effects between financial entities. The method involves generating network features by applying risk indicators to a graph model of financial entities and their relationships. These network features are fed into machine learning models trained on both network and non-network features to predict financial crimes. Alerts are generated when crimes are predicted, identifying the involved entities. The network representation helps reveal hidden connections and improve crime detection compared to traditional non-network features.

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