AI Risk Management in Financial Operations

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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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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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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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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Automated short-term investment system that uses machine learning to recommend optimal blockchain investments for bank accounts based on account history and balances, with risk mitigation and insurance. The system analyzes user bank account data to determine safe temporary investment amounts and durations. It also monitors blockchain investments for risks and can pull funds out if issues arise. The blockchain investments are yield aggregators that provide lower returns but cover losses versus traditional vaults. The system uses pre-programmed blockchain wallets that stake funds from exchanges and auto-convert fiat to crypto.

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Determining an optimal set of financing program options to offer to merchants to increase sales while minimizing risk. It involves using simulation and machine learning techniques to optimize financing terms like loan length, frequency, interest rates, and merchant discounts. Historical loan data is used to generate simulated financing programs with replaced parameters. Selection probability scores and cash flow ratings are calculated for each simulated program. A valuation score combines these to rank programs. The top programs make up the optimized set for the merchant.

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A method to continuously assess and manage risk in systems with stochastic processes by monitoring discrete events and interactions over time. It involves tracking and scoring events experienced by one entity related to another entity, determining the relative contributions of events to subsequent events, and using machine learning to iteratively adjust the scores based on similar event sequences. This provides a continuous risk index for events leading to reference events. The scores can be used to select future interactions, set parameters, or present visual indications to modify risks.

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Providing financial services for battery electric vehicles (BEVs) using a matrix based on the residual value of the vehicles and their batteries. The residual value is calculated dynamically using AI learning algorithms based on battery degradation. This value is used to generate financial products like futures contracts. The products have varying risks and returns based on the residual value matrix. This allows customers to hedge against battery depreciation and enables financial companies to manage risks.

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Detecting fraudulent financial transactions using machine learning models to immediately flag and prevent potentially fraudulent actions. The system trains a machine learning model to predict the likelihood of unauthorized activity for a user's transaction. If the model indicates high risk, it generates an alert. Transactions are then processed and the model determines if they're fraudulent. If so, it stops, flags, or allows the transaction based on the alert level. The model is trained using factors like transaction type, amount, and user history.

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Controlled prioritization of transactions to prevent overdrafts and late payments using AI. The system predicts future transactions based on historical data, assigns priority scores, and approves/denies them based on account balance. This prevents approving low priority transactions that could leave insufficient funds for high priority ones. It uses AI to analyze transaction history, predict future transactions, and score them for prioritization. This allows approving/denying transactions based on balance and priority instead of just following scheduling.

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Detecting actionable transaction risks using a real-time risk assessment engine that evaluates and prioritizes risks. The engine homogenizes risk signals across an enterprise, groups them in real time, scores them against historical data, and presents a relative risk profile. It leverages AI/ML to continually adapt and learn to optimize risk scoring. The engine compares a target event group to actionable and non-actionable groups using entropy and mutual information measures to determine if the target is closer to the high-risk group.

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Automated path-based recommendation for risk mitigation that uses machine learning to generate personalized recommendations for improving risk assessment scores. The method involves classifying an entity based on its input attribute values, finding a path from the current score to a target score within the entity's feasible space, and recommending actions to follow that path. The feasible space is determined by analyzing historical entities and their attribute changes. The path finding involves optimization and feasibility constraints to ensure improvement is possible.

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Using machine learning models to accurately predict the likelihood of future events that impact product suitability for specific consumers, like loan defaults. The method involves iteratively training a risk-evaluation model using historical data to predict the likelihood of future events like payment defaults. This prediction is then used to evaluate product suitability and make informed decisions about providing products like loans. By leveraging machine learning, the model can account for variations in available data length and provide more accurate predictions compared to traditional rules-based approaches.

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