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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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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A cloud-based digital platform for analyzing and managing risk in financial transactions like loans. The platform ingests diverse types of data from various sources, extracts relevant information, validates accuracy, combines elements, analyzes to reduce credit risk, enables automated workflows, provides customizable reporting, and monitors loan life cycle. It aims to improve loan processing efficiency and risk control through enhanced data ingestion, accuracy checks, data quality control, integrated analysis, automated processing, and reporting.

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Detecting money laundering activities hidden in large volumes of legitimate transactions using neural networks to compare financial transaction patterns. The method involves generating target and baseline vectors representing transaction activity for a specific party and overall region, respectively. A Siamese neural network compares the vectors to detect deviations indicative of potential money laundering. A drift score between the vectors is calculated, constrained in a learned space between non-money laundering and money laundering transactions. An alarm is triggered if the drift score indicates potential money laundering.

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A method for improving reinforcement learning accuracy in applications like portfolio optimization by augmenting the state space with additional information. The augmented state includes predictions from machine learning models trained on external data sources like news articles and financial data. This allows the reinforcement learning agent to leverage diverse and heterogeneous information beyond just the raw input state. The augmented state is then used to train the reinforcement learning model for tasks like portfolio optimization. The augmented state helps improve performance compared to using just the original input state.

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Multi-dimensional creditworthiness evaluation using machine learning to provide more accurate and timely credit risk assessment compared to traditional credit scoring methods. The method involves analyzing a user's financial transactions to quantify their creditworthiness along multiple dimensions, such as financial behavior, priority expenses, credit exclusivity, financial discipline, and red-flag events. Machine learning models are trained using this multi-dimensional data to predict creditworthiness for new users. This provides a more comprehensive and dynamic assessment of creditworthiness that addresses shortcomings of traditional credit scores like lag time and limited history for thin-file users.

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An interactive annuity product design using machine learning to provide customizable annuities with transparent fees and flexible risk management. The system generates annuity recommendations and simulations based on user preferences and market predictions. It leverages machine learning to optimize annuity products for users' objectives and risk profiles. Users can select from a range of investment options and risk levels. The system provides transparent pricing for customized risk management options.

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Improving accuracy of automated predictions like credit scoring by identifying clusters of similarly situated underperforming entities (UEs) and boundary performing entities (PEs) around them. These clusters reveal nuanced patterns and differences between UEs and PEs within categories. By fine-granularly adjusting prediction models based on cluster insights, it allows tailoring predictions for individual entities rather than broadly penalizing categories. It involves calculating deep-credit scores, finding closest PEs for UEs, forming clusters, and tracking distances to evaluate servicing effectiveness.

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A credit risk assessment method using deep learning to analyze text and financial data together for more accurate credit risk modeling. The method involves a two-stage neural network to process documents and company data separately. The document model understands relationships between words and phrases in unstructured text to generate scores indicating likelihood of financial events. The company model aggregates document scores with financial data to produce default probability sequences. This allows leveraging the semantic context of text to improve credit risk modeling beyond just financial ratios.

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Using AI agents to generate credit attributes for customers based on their credit history and other factors, and using those attributes to recommend invoice deferrals and amounts. The AI agents analyze subsets of the credit data to generate individual credit attributes for each customer. These attributes are then used to determine appropriate deferral terms for pending invoices. The AI-generated credit attributes provide a more objective and consistent way to assess deferral requests compared to manual judgments by agents.

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Dynamic return validation using generative adversarial networks (GANs) and reinforcement learning to optimize returns policies for individual customers. The system receives a return request, transaction history, and return rules. It applies a trained GAN using the customer data to determine return validity. Based on the GAN output, it recommends return actions like refusal or discounts. This allows personalized returns policies based on customer behavior to mitigate fraudulent returns.

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Using reinforcement learning (RL) agents to measure the effectiveness of transaction monitoring systems in financial institutions. The RL agents are trained to evade the monitoring system and then simulated to evaluate its resistance to adversarial action. This provides objective metrics like time, accounts used, and alert rate to quantify monitoring system strength. It also shows weaknesses, allows testing new products, and optimizes rule contribution.

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Using machine learning models to assess risk for network transactions and automatically trigger mitigation actions like requiring payment info before executing offers if risk levels exceed a threshold. The models are trained on past transactions to determine risk factors. When a user makes an offer, attributes like account history are applied to the models to assess risk. If risk exceeds a threshold, an automatic payment flow is triggered where the user provides payment and shipping info before the offer is executed.

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Forecasting cashflows across user accounts using neural networks that account for constraints like equalities and inequalities between account balances. The forecasting involves constructing a graph representing transactions between accounts, determining constraints for each node, backpropagating a loss function through the neural network, and using the trained network to forecast time sequences for each account and transaction type. This allows more accurate and constraint-aware cashflow forecasting compared to conventional neural networks.

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Generating accurate cash flow forecasts for user accounts using machine learning to mitigate forecast drift over time. The system trains a combined prediction model to forecast both inflow and outflow simultaneously, penalizing errors for both and the difference between them. This reduces forecast error accumulation compared to separate inflow/outflow forecasts. Historical account activity is used to train the model, and current data is fed in to generate forecasted inflow, outflow, and balance.

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Machine learning (ML) based system and method for generating Days Sales Outstanding (DSO) impact scores to prioritize and optimize financial collections. The system uses ML models to calculate DSO components, estimate open amount reductions, and generate DSO impact scores for each customer. It also highlights key pain points, operational efficiency, and collection strategies. The scores rank customers based on potential impact on overall and customer levels. This provides intelligent prioritization and insights for collections beyond just highest invoice value first.

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Providing explainable risk assessments using multi-stage machine learning techniques. The method involves two risk assessment models: an explainable model and a second-stage model. For a risk assessment query, the explainable model is first applied to generate an initial risk indicator. If that indicates high risk, explanatory data is generated showing how each predictor variable affects the risk. Then the second-stage model is applied using the same predictors to get a final risk indicator. The initial and final risk indicators are sent back along with the explanatory data from the explainable model. This allows explainable risk assessment with some accuracy tradeoff compared to complex models.

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Using artificial intelligence to predict user behavior and proactively alert them when they are likely to have insufficient funds in their accounts. The system periodically retrieves user account data, runs an AI model to predict negative cash flow depth, duration, and likelihood, and if it meets a threshold, the secondary server initiates communication with the user's device to alert them. The AI model is trained using historical user data.

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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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Automated capital management system that uses machine learning to forecast cash flow and provide recommendations to optimize capital for a primary entity. The system analyzes financial data to determine a baseline cash flow forecast for a time period. It then identifies sub-timeframes with surplus or deficit cash flow. Based on these sub-timeframes, a recommendation engine suggests actions to improve capital management, like seeking early payment, extending terms, or subscribing to financial products. The system can also automatically execute recommendations and revise the engine based on results.

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Using artificial intelligence to detect financial fraud across multiple channels by leveraging specialized models trained on channel-specific data. The method involves training separate AI models for each payment channel using historical transaction data from that channel. These models monitor real-time transactions in parallel, sharing findings across channels. They build individual profiles for each customer in each model. By combining channel-specific expertise, the models can detect fraud that may be missed by a single-channel approach.

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Detecting abnormal financial transactions using adaptive features and machine learning to provide high detection rate for new abnormal patterns. The method involves preprocessing payment data, adaptively selecting features based on sampling rates between normal and abnormal transactions, and using a machine learning algorithm to classify transactions as normal or abnormal using the selected features. This allows detection of new abnormal patterns in addition to known ones. The feature selection and ML model are determined adaptively based on historical data.

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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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Method to improve profitability of financial transactions by accepting increased transaction risks of selected customers in selected transactions. The method involves using artificial intelligence and machine learning to analyze customer behavior and transaction patterns. If a customer's behavior deviates from normal but is still within acceptable bounds, it will override the fraud detection system and approve the transaction. This prevents false positives and lost business from overly conservative fraud scoring.

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Automatically creating risk scores for electronic records to provide faster, more accurate results and flexibility in decision-making compared to manual entry. It involves accessing historical data to create a scoring model, calculating risk scores for new associations based on the model, and automatically selecting workflows based on the scores. This automated scoring and workflow selection improves efficiency and accuracy compared to manual entry and gathering additional info.

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Real-time financial, inventory, and staffing optimization system for businesses using AI and machine learning. It leverages cloud-based predictive engines for finance, inventory, and staffing that analyze real-time data like location, time, user info, and third-party data to optimize decisions. It predicts patron attendance, staffing needs, inventory usage, cash flow, and generates recommendations for inventory adjustments, staff schedules, and loan risk profiles. The system integrates with user devices, databases, and gateways for vendors and financial institutions to optimize operations around business 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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Predicting the likelihood of failed settlement for financial trades using machine learning models trained on historical trade data. The models determine the probability of settlement failure and the most likely reason based on parameters of recently executed trades. This enables early detection and mitigation of at-risk trades, reducing costs and risks for brokers.

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An integrated cash network decision optimization platform that leverages machine learning and optimization algorithms to help individuals and businesses optimize their cash flow, profitability, and financial decision-making. The platform allows users to build integrated cash and profit projections, evaluate scenarios, and set optimization goals. It provides algorithms for cash network design, forecasting, optimization, and reconciliation. Users can customize prebuilt algorithms, optimize individual cash network areas, and create new algorithms. The platform also allows manual, scenario, and algorithm-recommended overrides.

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Optimizing credit limits using reinforcement learning to maximize profit and minimize risk for a bank. The method involves using reinforcement learning to determine initial credit limits based on user profiles and risk profiles, then iteratively adjusting limits and learning from feedback to find the optimal credit assignments. The learning is sequential and considers historical feedback from the trial-and-error credit limit adjustment process. This provides a feedback-based credit line management that explicitly models rewards and produces an optimal policy function for credit limit assignment based on past learning.

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