AI-Driven Credit Scoring and Lending

Reducing credit card fraud by using AI and customer profiles to authenticate payments and detect fraud attempts. The method involves maintaining a database of customer spending habits, which is used to verify the authenticity of credit card payments. An AI algorithm processes the customer data to identify spending patterns. When a customer makes a credit card payment, the AI compares it to the customer's normal spending behavior. Deviations could indicate fraud. The AI can also verify the cardholder's identity and check for other fraud indicators like card numbers flagged as stolen. By analyzing customer-specific factors, it aims to better detect fraud compared to generic transaction analysis.

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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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Using natural language processing (NLP) and machine learning to analyze user interactions on social platforms to determine whether to grant predefined statuses like credit cards. The method involves retrieving textual data from a user's interactions, analyzing it with NLP techniques to extract language usage patterns, then using machine learning to compare the user's patterns to reference users with granted or denied statuses. This determines whether to grant the user's request.

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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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Determining creditworthiness of individuals who don't have established credit scores using machine learning models trained on visual choices. The method involves having users make selections from visual options presented on a device, calculating trait scores based on those choices, and training an ML model to correlate trait scores with credit history. The ML model then predicts a financial conscientiousness score for users based on their visual choices.

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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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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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Predicting financial wellness scores for users without requiring them to complete surveys. The method involves training a machine learning model using unsupervised learning on personal data sets from multiple users. The model correlates personal data from a new user to the data sets of existing users. It then predicts their financial wellness scores without needing them to fill out surveys. The model can also intervene to improve scores based on the predictions.

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Using machine learning to predict financial wellness scores for individuals without requiring them to complete surveys. The method involves training a machine learning model using personal data from a set of users. The model predicts financial wellness scores for new users based on correlating their personal data to the training set. Changes in predicted scores indicate potential events like relationship changes. The model can also send notifications about these events. By leveraging existing user data, the model can provide timely financial wellness insights without requiring repeated surveys.

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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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