AI-Driven Market Analysis

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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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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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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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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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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Dynamic demand forecasting system that provides accurate and interpretable forecasts of product sales at a granular level like individual product-location combinations. The system uses machine learning models trained on historical data to make forecasts. It dynamically selects the best model based on real-time conditions, or retrains existing models. The system also evaluates model performance using key metrics and performs inventory simulations.

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Online demand prediction using hierarchical temporal memory (HTM) for automated sales pipeline optimization. The method involves training an HTM network using historical demand data to learn temporal patterns representing sequences of states. This allows the HTM to forecast demand for offers by analyzing live data inputs in real-time without needing labeled training sets. The forecasted demand enables prioritization of sales of predicted popular offers.

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Generating feature embeddings for financial transaction data that can be used in machine learning models to improve prediction accuracy and interpretability. The embeddings are generated in an automatic and unsupervised manner using a heterogeneous graph representation of the transactions. The graph connects users and merchants based on transaction pairs with specific characteristics. The embeddings are generated hierarchically by jumping between transaction pairs based on frequency and hyperparameters. This allows extracting features with context and relationships between transactions. The embeddings can be labeled, indexed, and consumed by models without extra training.

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Training an AI model to predict the minimum winning price in real-time bidding without splitting auction types. The model is trained using auction data with conditional minimum winning price probability distributions. The overall minimum winning price probability distribution is generated first, then conditional distributions for each auction history. The AI is trained using auction attributes as input and the conditional distributions as output. This allows accurate price prediction in both auction types without dividing them.

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A system for facilitating electronic trading of financial instruments that uses machine learning to match traders and determine trade likelihood. The system allows users to initiate electronic trading sessions for specific financial instruments with specific terms. It matches the initiating user with dealer users who have a history of trading similar instruments. It also ranks potential invitee users based on likelihood of trading the instrument. This matching and ranking is done using machine learning models on historical trading data. The system then coordinates the trading session with selective sharing of offers.

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Intelligent valuation data reporting system that allows efficient and dynamic valuation analysis of startup companies using interactive graphical user interfaces. The system retrieves attributes of the companies and displays a customizable interface where the user can select thresholds for certain attributes. The system then dynamically populates a sub-interface with the companies that meet the selected thresholds, calculates valuations using AI models, and updates indicators for each company. This allows real-time, iterative, and customized valuation analysis and visualization of startup companies based on user-selected criteria.

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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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Using AI and machine learning to predict and adjust demand for products and services across multiple channels like online, in-store, and delivery. An analytics platform with a machine learning network generates real-time and future demand metrics for each channel based on channel events. These metrics are then used to dynamically adjust prices and inventory allocations in each channel to optimize demand and availability.

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Method and system for high frequency stock trading that leverages machine learning models while avoiding issues like time gaps and latency. The method involves generating input data based on market data, feeding it to a machine learning model to predict stock prices at future times, and generating orders based on those predictions. To prevent time gaps, the input batch size is chosen based on latency to select a future time point before the batch arrives. This allows using the model without outdated predictions.

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