Financial Market Analysis with Artificial Intelligence

Objectively evaluating data from a business and relating it to customized lists of merchant services. The technique involves using machine learning models to analyze business data and merchant service data, then deriving tailored recommendations of relevant services for each merchant. The models transform numerical representations of merchants and services into a common vector space to compare and correlate them. The models are trained on merchant profile data including statistics, categorical info, historical usage, and search history. This enables intelligent, near-real-time service recommendations based on aggregated merchant data.

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Demand sensing and forecasting system for supply chain using AI and big data processing to accurately predict product demand and optimize inventory levels. The system receives historical data, creates a hierarchical dataset, trains forecasting models, and uses a neural network to determine optimal weights for each level. It also considers factors like revenue, logistics, and political risks to provide recommended forecast frequencies. The system can dynamically process forecasts based on factors like engagement and pricing models, and categorize and classify products based on demand.

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Artificial intelligence (AI) powered real-time portfolio valuation system that accurately and efficiently values private company portfolios. It uses AI models to identify comparable public companies for each private company, retrieve financial data for those public companies, and then derive valuations for the private companies based on their identified comparables. This allows real-time portfolio valuation without subjective human estimation. The AI models use algorithms like boosting trees, k-nearest neighbors, and linear regression to cluster and compare companies.

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Visualizing interest charges based on payment options in electronic payment systems to provide users with real-time insights into projected interest charges and help them make informed decisions to minimize interest payments. The system generates a calendar view for recurring payment accounts that shows daily balances and interest charges. It dynamically updates the view in real-time as users select payment amounts. If a projected charge exceeds a threshold, an alert suggests using a different payment method. The system uses machine learning to optimize payment schedules.

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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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Dynamic forecast adjustment optimization using machine learning for collective optimization of an objective factor in a use-case like consumer goods. The method involves generating probabilistic forecasts, a relationship model, and an optimization model using ML systems. It recommends adjustments to collectively optimize the objective factor by dynamically generating recommendations based on the optimization model. This enables accurate forecasting and optimization across factors, objective, and constraints.

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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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Automatic generation of personalized livestream carousels using machine learning and user action statistics to efficiently generate a customized set of livestreams that are likely to be of interest to a user. The method involves retrieving candidate livestreams based on user and livestream data, organizing them, inputting user and livestream info into a neural network, and generating a carousel widget with ranked livestreams based on the network output.

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Method for converting order book data into 2D format suitable for machine learning models. The method involves generating a tensor data structure with a time axis and price axis in tick units. The tensor has multiple 2D data items representing quantities for bid and ask prices at specific coordinates defined by time and price. A mid price calculated from the bid and ask is used as the tick price. This converts the tabular order book data into a format that can be directly input into 2D CNN models for stock price prediction.

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Improving the accuracy of market research data collection through autonomous evaluation and iteration. The method involves using machine learning models to identify potentially inaccurate collection information and then determining whether to obtain replacement data or generate simulated data to fill gaps. The decision is based on factors like event information and cost assessments. This allows cost-effective, iterative improvement of data collection accuracy.

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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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Contact center system that generates and disseminates real-time data during customer interactions to improve representative performance and contact center efficiency. The system uses thick client devices with containerized applications and a server. Representatives interact with customers using containerized applications within a desktop app. Data from the sessions is captured in real-time and sent to the server. Machine learning models analyze the data to provide insights back to the representatives during the sessions. This allows real-time quality tracking, assistance, and workflow optimization based on live interaction data.

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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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Preventing authorized fraud in real-time by using data element analysis and machine learning to detect fraudulent transactions before authorization. The method involves analyzing user actions like account openings, transfers, and registrations to generate individual risk values for fraud data elements. Rules are applied to these values to determine if a transaction is potentially fraudulent. This allows flagging and mitigating fraud in real-time by actively monitoring user actions. The fraud analysis uses machine learning to adapt to changing fraud tactics over time.

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Wavelet-based data processing for real-time financial transaction fraud detection using wavelets, key-value databases, and machine learning. The method involves generating wavelets from transaction data, storing them with keys in a database, and feeding the wavelets to machine learning models for fraud detection. It aims to provide indicators easy to use by machine learning systems that improve performance compared to traditional financial indicators. The wavelets are generated from transaction data, stored with keys in a database, and fed to machine learning models for fraud detection.

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A platform for analyzing transactions on decentralized exchanges running on blockchains. The platform collects transaction data from multiple nodes on the blockchain, validates it, calculates metrics like volume, and stores them. It allows monitoring with alerts triggered when metric thresholds are reached. It also uses machine learning to make predictions about metrics. The platform reduces blockchain interaction by collecting data once and storing it versus repeatedly querying blockchains.

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Configuring AI decision nodes throughout a communication decision tree to dynamically define iteration data that corresponds to a trajectory through the tree. The decision nodes can support successive iteration of AI models to select the next node based on current profile data, learned data, and event detections. This iterative AI decision making can be availed through a canvas interface to visually define and connect the nodes, with each node representing an AI model or communication specification. The iterative AI selection allows customized and adaptive communication workflows based on real-time user context rather than static rules.

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A data analytics platform for forecasting commodity prices using machine learning and natural language processing on structured and unstructured data sources. The platform analyzes structured data like weather and futures prices, as well as unstructured sources like podcasts, to forecast commodity states. It uses techniques like neural networks, sentiment analysis, and time-series modeling to process the data and generate forecasts. The platform aims to provide accurate commodity price projections by leveraging multiple data sources and machine learning algorithms.

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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 for providing real-time security actions for blockchain operations based on AI models trained on labeled blockchain data. The system receives blockchain data, identifies operations, generates feature inputs based on blockchain characteristics, processes through AI models trained on labeled data, determines security actions, and alerts users of potential risks before execution. The labeled data is from sources and previously processed blocks. This allows customized risk assessment and prevention for new operations.

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