AI-Driven Efficiency in Loan and Mortgage Processing

Automated system to efficiently match and link data files associated with a loan that have different identification attributes. The system detects commonalities between files to assign smart keys and link files for the same loan despite different identifiers. It compares attributes like borrower info, property address, etc. using rules generated from machine learning to accurately match files processed by different tools. This eliminates manual keying and reduces errors compared to using fixed keys.

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Using blockchain to approve and update mortgage applications faster by maintaining a continuously updated mortgage-ready status for customers and properties. Blockchains are used to store customer and property data, allowing real-time monitoring and verification of mortgage readiness. This eliminates the need for repeated application reviews, appraisals, and title searches. Mortgage amounts and appraisals are calculated directly from the blockchain data. Agents are incentivized to update the blockchain for higher scores and rewards.

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Automating loan origination processing by optimizing routing and prioritization using AI. The method involves receiving loan applications, extracting parameter values, retrieving underwriter info, generating a task procedure with AI to assign the best underwriter, prioritize handling, and projected completion. The procedure is sent to the assigned underwriter who confirms acceptance. This automated routing and prioritization leverages AI trained on historical data to optimize loan processing efficiency.

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Dynamic escrow management system that enables real-time, online processing of escrow instructions and claims using a web interface and natural language processing. The system extracts information from escrow agreements using NLP, predicts authorized signors, and allows clients to submit, approve, and execute instructions via a portal. It replaces manual offline processes with a secure, automated system for escrow instructions and claims.

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Lending platform using microservices, blockchain, IoT, AI, and smart contracts to enhance lending transactions. The platform has features like: 1. IoT monitoring of collateral to validate guarantees and adjust interest rates. 2. Crowdsourcing to verify borrower reliability and collateral condition. 3. Automated loan adjustments based on regulatory and market factors. 4. Robotic process automation for tasks like loan negotiation, collection, consolidation, and factoring. 5. Compliance automation using smart contracts to facilitate regulatory requirements. 6. Cryptocurrency escrowing for licensing personality rights. 7. Blockchain custody for assets. 8. Rating system for loan entities using AI. 9. AI-assisted loan marketing to find prospects. 10. Smart contracts for automated loan underwriting. 11. AI-assisted loan

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Using blockchain to approve and update mortgage applications faster by continuously tracking customer and property data. The method involves maintaining blockchain records for customers and properties that are "mortgage ready". When a mortgage application is received, the blockchain is checked to see if the customer and property meet the ready criteria. If so, the application is approved faster because the required checks have already been done. The blockchain is continuously updated with new data to maintain mortgage readiness.

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Using machine learning to improve efficiency of asset-exchange platforms like loan marketplaces by predicting optimal listing parameters and prices based on historical data and attributes. The system trains machine learning models to determine importance scores for attributes like loan size, interest rate, etc. It then uses these scores to predict listing prices and categories for new assets. This feedback is provided back to users to optimize future listings and avoid delays.

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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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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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Facilitating peer-to-peer micro-loan transactions using smart contracts on distributed ledgers like blockchain. It involves calculating integrity scores for users based on factors like payroll data and peer feedback, then matching borrowers with lenders based on risk scores. Loan terms are negotiated and recorded on the blockchain. After repayment, feedback is collected to update scores. This leverages payroll and HR data to connect workers in need of small loans with those willing to lend at attractive rates, while using the blockchain for transparency, trust, and risk mitigation.

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Data exploration analysis based covenants categorization and recommendation system for loan origination that leverages historical loan data and machine learning to recommend covenants for new loans. The system trains binary and classification machine learning models using historical loan data to categorize covenants into two categories. It then iteratively trains intermediate models for the second category until the number of predicted covenants matches a threshold. This multi-step training process improves the accuracy of covenant recommendations for new loans. The system provides a real-time covenant recommendation engine using machine learning that leverages historical loan data to recommend covenants for new loans based on customer, industry, and loan details.

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Facilitating digital transactions using a distributed ledger to authenticate users and perform transactions based on community data. The system analyzes factors like service presence, demand gaps, and reviews to determine loan eligibility. It uses blockchain to securely store and verify community, identity, and reputation data. This enables automated loan disbursements when demand exceeds thresholds.

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Using artificial intelligence to improve loan approval decisions and expand lending opportunities to non-traditional applicants with limited credit and employment history. The AI agent analyzes applicant data beyond just credit scores and work history. It retrieves ratings from customer review sites to assess the applicant's reputation and character. This additional input, along with financial and employment info, is used to determine loan recommendations for non-traditional applicants. The AI also detects loan application fraud using video and audio analysis during virtual interviews.

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Using machine learning to more efficiently evaluate mortgages during real estate transactions to reduce manual review and catch missed mortgages. A machine learning model is trained to predict the likelihood that a mortgage on a property is still open based on historical data. This allows flagging mortgages for manual review if the prediction exceeds a threshold. The model can also catch missed mortgages by flagging subordinated mortgages that may not have been found manually. This reduces risk when traditional methods miss mortgages due to errors in the public record.

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Exchanging user data through federated machine learning to improve loan underwriting. The method involves training a shared neural network model across multiple entities like banks and merchants using their customer data. This federated model is used to generate risk scores for loan applicants based on their existing financial history. These scores are then shared with lenders to help them assess loan applications. The federated training allows leveraging of pooled customer data from multiple sources to build a more comprehensive creditworthiness model.

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Intelligently matching and validating data for loan underwriting using predictive modeling techniques. The system receives loan application data and compares it to guidelines and external sources to validate and verify key elements. It leverages blockchain-like ledgers for immutable underwriting history. Bayesian networks predict loan characteristics based on prior data. Adaptive logic determines linguistic distances between application data and stored/retrieved data to verify accuracy. The system automates underwriting using machine learning, deep learning, and blockchain technologies to optimize loan processing efficiency and reduce errors.

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A computer-implemented method for efficient, low-cost underwriting and monitoring of loans using automated document processing, decision support, and multi-channel communication. The method involves requesting information from borrowers, automatically validating documents, triggering journeys with tasks for further requests, document processing, and exception handling. The tasks are executed with customizable message templates and communication channels. The automated processing reduces time and cost versus manual underwriting.

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Artificial intelligence/machine learning-based lockbox document processing system that automates document intake, classification, extraction, validation, and delivery of payments from lockbox documents like checks and invoices. The system uses trained machine learning models to classify documents, recognize and extract data fields, validate data, and deliver payments without human intervention. This reduces manual labor, improves efficiency, and enables 24/7 lockbox processing.

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Using machine learning models to generate more accurate and efficient predictions for approving financial service requests (FSRs) like loan applications. The system involves a prediction manager that analyzes FSRs and historical data to generate prediction inputs. It also extracts comments from FSRs and agents to generate a request vector representing the authenticity of the request. The manager applies machine learning models to the prediction inputs and request vector to generate initial predictions. It provides the predictions to approvers and obtains comments to further refine the predictions using chained request vectors. This chained analysis improves subsequent predictions by capturing the authenticity of the request and approver comments.

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