AI for Credit Scoring in Lending Decisions

Unsecured loan system that allows customers to obtain loans with favorable terms closer to secured loans by using their existing credit accounts as collateral. The system determines interest rates and pre-authorization hold amounts using machine learning models. It analyzes factors like credit scores, account balances, and payment histories to categorize borrowers and calculate confidence scores. This allows optimizing collateral selection and hold amounts. By leveraging existing credit, lenders can offer more options with lower risk compared to traditional unsecured loans.

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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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Automated system for building customized machine learning models tailored to specific applications like credit underwriting, using a sampling process to speed up model development. The system prompts users to input business-specific data like credit bureau, region, loan type, etc. It then samples a subset of national credit data matching those criteria. This lookalike sample is used to train the customized model instead of the full lender dataset. This allows faster model development by reducing data gathering. The system also presents a user interface to test the customized models using real data.

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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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Intelligent lending platform using microservices, blockchain, and AI to enable adaptive and automated lending transactions. The platform has services for data collection, blockchain, smart contracts, and user interfaces to handle lending activities and events. It leverages multi-modal data collection from IoT, crowdsourcing, and social networks to monitor collateral and loan conditions. Smart contracts automate loan terms based on monitored data. AI optimizes loan terms and conditions. The platform provides adaptive lending solutions across the loan lifecycle.

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Intelligent lending platform that uses machine learning to analyze denied loan applications, identify the main reasons for denial, and provide customized and actionable explanations back to the applicant. The platform trains a complex machine learning model on historical loan data to accurately predict why a particular loan application was denied. It then extracts the most significant reason(s) and presents them in a clear and understandable way to the applicant. This allows the applicant to see exactly why they were denied and provides specific actions they can take to improve their chances for future loans. The platform monitors interactions and adjusts lending decisions based on actions taken on the recommendations.

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Credit scoring model that improves accuracy and robustness by leveraging social network data. The model calculates a user's credit score using their personal score as well as scores of other users they have social connections with. This expands coverage and reduces errors compared to just using personal data. The model is trained iteratively with user scores, relative scores based on connections, and default annotations.

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Estimating creditworthiness of loan or credit applicants who have insufficient domestic credit history by analyzing their email messages. If a credit request has insufficient domestic credit data, the credit decision platform obtains access to the applicant's email account and uses machine learning to identify relevant messages. It analyzes the content using natural language processing to generate non-domestic credit data. Metrics from this data are used to estimate creditworthiness.

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Automated credit building system that uses machine learning to recommend actions to users for improving their credit scores. The system collects user data, activity, and credit scores, assesses potential actions, and provides personalized recommendations to reach credit goals. It also rewards users for meeting thresholds and leverages third-party apps to suggest income sources based on user needs.

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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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A system for training fairer predictive models in domains like credit risk, drug evaluation, and college admissions. The system uses adversarial training techniques to improve fairness while maintaining accuracy. It involves training a primary model to predict an outcome, then training a secondary model to predict the sensitive attribute based on the primary model's output. The primary model is then retrained to maximize the secondary model's accuracy while minimizing its own. This forces the primary model to learn features that are invariant to the sensitive attribute, making it more fair. The system also provides tools for selecting and justifying the best fairness-accuracy tradeoff.

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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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Predicting financial well-being of individuals without requiring them to complete surveys, using machine learning models trained on personal data. The models learn to correlate personal data sets of users with their assessment scores. To predict a user's score, the model matches their personal data to that of other users. If a difference between predicted and test scores exceeds a threshold, actions are taken like sending communications or changing account settings. The models use unsupervised learning on user data sets without labels.

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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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Aggregating a user's transaction data from multiple sources to determine a credit metric for credit decisions. The method involves receiving aggregated transaction data from various third-party sources where the user has accounts, analyzing it using machine learning to determine a credit metric, and providing the credit metric to interested parties. This provides a more complete view of the user's financial activity for credit evaluation than just relying on a credit bureau score.

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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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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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Predicting credit scores for people who don't have traditional credit histories using their driving behavior data. The method involves training a credit score prediction model using historical user data, vehicle telematics data, and credit scores. The model is then used to predict credit scores for new users based on their driving behavior and other data.

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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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