Risk Management Techniques Using AI

Automatically assessing and mitigating risks associated with end-user computing tools like spreadsheets using machine learning models. The models are trained on labeled data to determine risk levels and types. They can then be applied to unseen tools to automatically classify risks. High-risk tools can be mitigated through actions like review, tracking, or monitoring. This enables scalable and objective risk management for widely used but potentially hazardous end-user tools.

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A method for detecting anomalous activity, like fraud or money laundering, using a dynamic approach that combines transaction analysis and social network analysis. The method involves calculating anomaly scores for user transactions using unsupervised and supervised algorithms trained on transaction attributes. It also calculates network anomaly scores based on interconnected user profiles. The potential for anomalous activity is determined by combining the transaction and network scores. This provides dynamic detection from multiple angles rather than just transaction analysis alone.

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Secure electronic financial transactions system that uses AI, biometrics, and multimodal data analysis to prevent fraud and improve user experience. The system collects multimodal data from visual, auditory, and tactile sensors during transactions. It uses AI modules like biometric authentication, transaction anomaly detection, geospatial analysis, behavioral analysis, and third-party data integration to analyze this data for fraud prevention. The system also communicates with users through modalities like vision, audio, touch, taste, smell, temperature, pain, and balance to address fraud concerns or request verification.

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An artificial intelligence system for analyzing financial data and making investment recommendations using a decentralized ecosystem of agents like models, recommenders, and verifiers. The agents compete to provide the best financial models for analyzing data. Verifiers evaluate the models and recommenders select them. The agents are incentivized to perform well through a competition where winners collect stakes from losers. This creates a stable equilibrium where agents strive to provide accurate models. The ecosystem also uses costly signaling with cryptographic tokens to distribute rewards and rents.

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Reducing risk of transactions like ACH transfers by predicting account balances at settlement time using historical data and machine learning models. When a request is received to initiate a transaction, the system retrieves the account history and uses a trained model to project the balance at settlement. This allows assessing the risk of the transaction completing successfully without needing real-time account balance checks.

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Autonomous fraud risk management system that quickly identifies and mitigates emerging fraud trends using machine learning and automation. The system performs feature engineering, rule recommendation, testing, and implementation in a closed loop process. It leverages machine learning techniques to develop fraud rules based on features extracted from data. The rules are tested in silent mode, approved, and upgraded to production. This automated and adaptive system allows efficient and timely creation of fraud rules to combat changing fraud trends.

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A debt collection system that uses AI to optimize debt recovery for financial institutions. The system clusters accounts into groups based on attributes, assigns recovery agents to groups using a classification model, and recommends optimal recovery strategies for each account within a group using machine learning. Feedback from agents is used to refine the strategy recommendations.

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A method for building predictive analytics from text data extracted from earnings call transcripts using natural language processing (NLP) and machine learning. The method involves parsing the transcripts using NLP, creating intermediate metrics from the parsed text, combining the metrics into headline analytics, testing the headline analytics for standalone predictive power, selecting high-performing ones, then testing them again for additive predictive power when combined with existing market analytics. The selected headline analytics with additive predictive power are applied to new earnings call transcripts to predict financial performance.

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Automatically deciding which bills to pay and how much to pay them using artificial intelligence. The system analyzes bills, user goals, and account balances to determine optimal payment strategies. It can also negotiate with billers to get better terms. This aims to automate bill payment decisions to optimize financial outcomes like minimizing interest, maximizing returns, or prioritizing important bills.

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Detecting fraudulent transactions using a mixed classical-quantum ensemble method that combines classical and quantum machine learning models to improve fraud detection accuracy while reducing false positives. The method involves: (1) Using a classical model to initially score a transaction for fraud. (2) Using a quantum model to score the same transaction. (3) Comparing the scores from both models. (4) If there's disagreement, inputting the transaction attributes and scores into a third classical model to determine which model's prediction is more accurate. (5) Outputting the final fraud prediction based on the third model's determination. The mixed ensemble leverages the strengths of classical and quantum ML to enhance fraud detection.

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Evaluating transaction requests received by a computer system using a machine learning algorithm to improve accuracy in granting versus declining transactions. The computer system trains a machine learning model using historical transaction requests. When a new request comes in, the model scores it and compares to a threshold. If above, the request is granted, if below, declined. This improves accuracy compared to just using a fixed threshold as the model learns from prior requests. However, wrongly rejecting or granting transactions can degrade performance. To mitigate this, the model is retrained periodically using a subset of recent requests to adapt to changing conditions. This allows updating the model without having to retrain from scratch.

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Probabilistic framework for modeling financial time series that accounts for the impact of stochastic events like company earnings releases. The framework uses machine learning models to learn event intensity and magnitude, and how they affect time series. This improves forecasting accuracy by capturing event impacts. The models are trained on historical data to generate forecasts that factor in event shocks. The framework can also optimize portfolios by considering event risks.

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Asynchronous processing of electronic communications data in a near-real-time manner to improve risk analysis and user experience compared to synchronous processing. The technique involves starting asynchronous computations in response to trigger events like user actions, completing them before the final event like transaction initiation, and leveraging other services during the asynchronous analysis. This allows more data retrieval and analysis compared to synchronous risk analysis limited by SLAs. The asynchronous processing is done via a graph database system that stores transaction graphs with nodes representing entities and edges representing transactions. The asynchronous computations can traverse multiple hops in the graph to analyze more transactions.

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Using neural networks to make financial investment predictions and recommendations based on user data, news, financial data, and predictions. The neural networks are trained to determine investment movements, prices, and recommendations by processing this input data. An interactive system like a dialogue agent can use the neural networks to provide financial advice when users ask questions about investments.

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Using neural networks to make financial investment predictions and recommendations. The neural networks analyze data like user information, news, financial data, and predictions to determine financial movements, stock prices, investment suggestions, etc. This allows more accurate and personalized investment advice compared to relying solely on external sources. The system can also be interactive, like a chatbot, to provide customized financial insights and advice to users.

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Compiling an optimal portfolio of assets using a genetic algorithm and evolutionary iterative process. The method involves generating a large number of random portfolios by selecting assets with weightings that sum to 100%. These portfolios are scored based on financial evaluation factors. The highest scoring portfolios are selected and the process repeats with their offspring. This iterative evolution process continues until marginal score improvements are below a threshold. The final top-scoring portfolio is selected.

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Dynamic modeling system for credit scoring that uses machine learning and real-time credit value modeling to provide customized credit options and conditions for users. The system generates user interface elements that dynamically present account-specific credit values and conditions based on user activity and risk analysis. It uses an activity machine learning model to calculate a risk score from user activity data, then a credit value model to determine dynamic credit ranges and conditions for selected credit values. This allows flexible, efficient, and accurate presentation of customized credit options and conditions tailored to each user's activity level and risk profile.

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Automatically assessing and mitigating risks associated with end-user computing tools like spreadsheets. The method involves training a machine learning model to classify risks based on training data with labeled end-user tools. The model determines risk levels (high, medium, low) and types (financial, reputational, regulatory) for new tools. If a tool's risk exceeds a threshold, mitigation actions like review, tracking, or monitoring are applied. The model can also consider context like business processes and user roles to enhance risk assessment.

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System for predicting future cash flow based on aggregated transaction data. The system collects cash inflows and outflows from a user's accounts, trains a machine learning model to predict future inflows and outflows, and dynamically updates a GUI to show future cash flows rearranged by category. It also sends notifications to reduce spending in categories with high predicted usage during future periods where outflows exceed inflows.

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Training a monotonic recurrent neural network (MRNN) for risk assessment or other outcome predictions that has explainable outputs. The MRNN is trained using monotonicity constraints to enforce monotonic relationships between input variables and output. This involves using activation functions with nonnegative derivatives, nonnegative node weights, and in the case of LSTMs, strictly nonnegative activation ranges. These constraints make the MRNN output a monotonic function of the inputs, enabling easier interpretation and explanation of the model's predictions compared to standard recurrent neural networks.

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