AI Loan Processing Automation

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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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 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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A small loan management system that uses automated analysis to improve efficiency, accuracy, and user experience compared to manual loan processing. The system evaluates loan applicants by analyzing their basic information and ability, returning applications with false data, and providing options for those who don't meet requirements. This automated loan screening reduces manual processing time and errors, improves loan credit monitoring, and provides targeted benefits to users with good loan history.

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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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Parallelizing steps in loan decision-making to improve efficiency and accuracy. The method involves making a comprehensive loan decision for a borrower by simultaneously performing two review steps - a core review and a risk control review - instead of sequentially. This reduces time compared to sequential review. The core review is done by a subsystem to evaluate the borrower's creditworthiness. The risk control review is done by another subsystem to assess risks like overdues or defaults. The risk control review relies on the results of the core review. By parallelizing the reviews, the overall loan decision-making process is faster.

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Automating loan application processing using RPA (Robotic Process Automation) and AI models to reduce manual intervention and labor costs. The method involves receiving a loan application, obtaining credit and repayment evaluation data, inputting it into an RPA robot that uses the scores to approve the loan based on preset rules. Smart contracts issue the loan and generate repayment plans. This leverages AI models to evaluate creditworthiness and repayment ability, and automation to approve loans without human review.

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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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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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Automated pre-loan review and evaluation method to improve efficiency and accuracy compared to manual offline investigation. The method involves determining the accuracy of financial data submitted by loan applicants using accuracy parameters specific to their industry. If the financial data accuracy meets preset conditions, the feasibility of the loan is determined using the financial data. This allows automated pre-loan review without manual investigation.

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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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Exchanging user data for credit decisions using federated machine learning to improve loan underwriting. The method involves training a shared neural network model using customer data from multiple entities like banks, lenders, and merchants. This model is iteratively trained using locally generated data from each entity. When a user requests a loan, the model is used to generate a risk score for that user based on the pooled customer data. This score is then transmitted to lenders to help them evaluate the loan request.

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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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Loan qualification review system to automate and streamline the loan application process by leveraging AI and machine learning techniques. The system aims to address the limitations of manual loan review processes by providing an automated review system that reduces manual review time, improves accuracy, and lowers costs. The system includes modules like customer drainage, recommendation, and qualification review to gather customer info, recommend financial knowledge, and automatically assess loan qualification. This allows faster, more accurate, and more efficient loan processing compared to manual review.

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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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A lending transaction enablement platform using data-integrated microservices like blockchain, AI, and IoT to improve lending efficiency, reduce risk, and enable adaptive intelligence. It leverages services like data collection, monitoring, smart contracts, crowdsourcing, and automation to enhance lending processes like loan negotiation, underwriting, marketing, compliance, rating, and debt management. The platform can leverage data like IoT sensor readings, social media, and crowdsourced inputs to dynamically adjust loan terms based on factors like regulatory requirements, market conditions, collateral value, and borrower reliability. It also provides features like automated loan restructuring, smart contract-based loan execution, and automated compliance monitoring.

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An AI-powered anonymized marketplace for connecting borrowers with lenders in a way that reduces time and cost while improving success rates. The system uses AI to analyze borrower and lender data to identify similarities and trends, allowing it to dynamically match borrowers with relevant lenders. This anonymized matching is done through a chat room where lenders' identities are revealed only after the borrower selects them. The AI also predicts likely terms for a borrower based on historical data. This helps borrowers anticipate financing options and lenders understand market competition. The AI learns from collective participation, evolving through machine learning.

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Automated underwriting and processing loans using machine learning to quickly approve loans without human intervention while complying with regulations. The system receives loan applications, collects external data, pre-processes it using ML models, does automated feature engineering, determines business objectives, creates ML models, and creates an ensemble model. It also maps adverse action notices to categories. This allows fully automated loan processing that maximizes lender valuation using ML models trained on pre-processed external data.

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Automating loan decision making by training a machine learning model to classify loan applications based on underwriter comments. The method involves extracting factors from unstructured underwriter comments using text mining, feeding those factors into a trained ML model, and using the model's output classification instead of manual decision making. This allows automated loan approval/rejection based on the same factors as human underwriters. The ML model is trained on labeled comments and decisions from a corpus of underwriter actions.

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Automated loan information processing to improve speed and accuracy of determining loan amounts for supply chain finance. The method involves analyzing factors like transaction history, repayment performance, and loan volume to calculate a loan capacity for a borrower. It uses machine learning algorithms to analyze loan and transaction data to determine the borrower's repayment ability and loan capacity. This automated analysis replaces manual review and calculation by financial analysts.

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Predicting targeted, agency-specific recovery events for delinquent loans using adaptively trained artificial intelligence processes. The method involves training machine learning models to predict recovery rates for each collection agency based on features like customer profiles, transaction data, delinquency history, etc. This allows assigning delinquent loans to the agency with the predicted highest recovery rate. The models are trained using historical data and validated on separate time intervals.

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Selectively using a heuristic model or a statistical model to analyze relationship attributes of a borrower to determine whether to approve or deny a lending-product request, in a system that determines a probability of a borrower repaying a loan over a predetermined time, and avoiding being charged off. The system uses a Statistical Risk Management (SRM) approach to evaluate borrower risk beyond just creditworthiness checks. It analyzes relationship attributes to predict loan repayment and default likelihoods. This allows more nuanced decision making for small, short-term loans where traditional creditworthiness checks are impractical.

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Automated mortgage servicing and whole loan valuation system that provides instant, accurate mortgage valuations and pricing for complex assets like mortgages and servicing rights. The system uses machine learning models like k-nearest neighbors to interpolate between loan characteristics and price points. It allows users to upload portfolios and receive real-time valuations without manual spreadsheet grids or modeling. The models adapt in real-time as market rates change.

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Intelligently matching and validating data for loan underwriting using predictive modeling techniques to optimize loan processing efficiency, accuracy, and approval rates. The system receives loan application data, compares it against external sources to verify accuracy, and leverages machine learning models to determine the best financing match. It uses quantum ledger databases for immutable, transparent record-keeping. The models incorporate historical loan data and intelligent acts like income verification. By validating and verifying loan elements, the system can quickly identify false data, request missing info, and adjust applications before funding.

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Intelligently optimizing a queue of actions in an interface using machine learning, like prioritizing loan applications, by building a predictive model from training data. The model determines probabilities of loan funding if a lender proactively engages vs reactively engages a loan arranger. This data is used to rank and order the loan applications presented to the user. The system analyzes the training data to build the predictive model, applies it to new loan applications to determine the probabilities, ranks the applications based on those probabilities, and presents them to the user in order.

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Intelligent underwriting system that uses machine learning to automate risk analysis and decision making in underwriting processes. The system trains a predictive model to identify and assess risks in underwriting cases based on preprocessed input data. It then applies the model to current cases to provide automated risk analysis and decision recommendations. This allows quick and efficient processing of high volumes of underwriting cases, reducing cost and improving accuracy compared to manual underwriting.

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Automated reconsideration of rejected loan applications using manual automated underwriting guidelines to provide real-time feedback and recommendations for loan approval. When an automated loan review system rejects an application, it triggers an automated reconsideration process using a manual automated underwriting system (MAUS) that applies manual guidelines in an automated way. The MAUS receives the application data, credit report, and loan form to reevaluate. If the reconsideration shows the application should be approved, the applicant gets immediate notification. If not, recommendations are provided for actions to take to qualify. This provides faster, automated review and feedback compared to manual review.

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Construction industry financing approval system using text recognition, analysis, and comparison to improve efficiency and consistency of loan cases in the construction industry. The system involves inputting credit case data, loan conditions, and approvals into a server. The server analyzes the case text, checks against regulations, compares against historical cases, generates risk scores, and sends back revised conditions. It also warns if thresholds are exceeded.

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System for assigning mortgage loan queries to streamlined processing instead of full appraisals. The system uses historical data to create models of property values and conditions. When a new query comes in with submitted data, the system compares it against the models to assess risk. If the submitted values fall within a certain range and the modeled values aren't too high, it flags the query for streamlined processing without requiring a full appraisal. If not, it flags for regular processing.

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Intelligent loan qualification based on future servicing capability using cashflow data from bank accounts instead of credit scores. The method involves requesting access to a user's bank account, analyzing transaction history, generating metrics like income stability and overdrafts, assigning weights based on machine learning, computing a user score, and matching to loan tiers based on threshold values. This provides a more accurate assessment of loan repayment ability compared to just credit scores.

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Securely verifying identity for loan transactions without physical presence. The method involves tokenizing and digitally transmitting loan requests between remote devices like merchant and personal computers. The loan requests include unique tokens representing the borrower's identity instead of physical identification. This allows remote loan provisioning and disbursement without requiring the borrower to physically present ID. The tokens are securely generated and verified using the borrower's device and lending organization systems.

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Intelligent loan qualification based on future servicing capability using cashflow analysis instead of credit scores. The system accesses a user's bank account and generates metrics from transaction records. It assigns weights and thresholds to the metrics for loan tiers. The user is placed in the highest tier where their metric values meet the threshold. This provides a more accurate prediction of future loan servicing ability compared to credit scores.

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Credit management system for customized loans based on big data analysis of customer behavior. The system involves a client, bank server, and risk decision server. When a user applies for a loan, the request is sent to the risk server which approves based on big data analysis. If approved, the bank server queries user behavior data to create customized loan products. These products, legal docs, and signing instructions are sent back to the client for customized loan processing. This integrates user habits into loan products. If risk approval fails, it transfers to manual review.

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Intelligent loan review system that uses machine learning models to automate and accelerate the loan application and approval process. The system leverages AI techniques like machine learning to establish intelligent models for anti-money laundering, credit scoring, and value evaluation. These models analyze customer data to comprehensively evaluate risks and provide quotes for loan amounts, interest rates, and future values. The goal is to provide fast, efficient, and fully-automated online loan services that eliminate manual steps like application filling, review, guarantee, and allocation.

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Intelligent digital twin system for lending transactions that uses blockchain, smart contracts, AI, and IoT to automate and optimize lending processes. The system enables automated loan negotiation, collection, consolidation, factoring, and syndication. It also provides automated underwriting, compliance, marketing, and rating. The system leverages multi-modal data collection from sources like social networks, crowdsourcing, IoT, and AI to train and improve lending decision making.

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Intelligently optimizing a queue of actions in an interface, like prioritizing loan applications for review, using machine learning to provide probabilities of funding for proactive versus reactive engagement by lending institutions. The system analyzes training data to build a predictive model, applies it to loan apps to determine probabilities of funding if the institution proactively engages the loan arranger versus reactively, and ranks the apps based on those probabilities. This provides an optimized queue for review that prioritizes apps more likely to be funded if the lender takes proactive action.

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Automated real time mortgage servicing and whole loan valuation system using machine learning and interpolation to provide accurate loan pricing in dynamic market conditions. The system receives a pricing file containing loan characteristics and applies a set of k-nearest neighbors regression models to interpolate continuous pricing between data points. It eliminates local maxima beyond a threshold to prevent overfitting. Real time updates to reference rates are received and the pricing file is updated accordingly. This provides more accurate and responsive loan pricing compared to static parameter models.

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Machine learning architecture for resource allocation applications like mortgage approvals that can provide accurate predictions even when training datasets are incomplete or lack variety. The architecture encodes historical dataset feature attributes into conditional distribution representations based on hierarchical relations. This allows resource allocation predictions to be generated by combining the representations even if some attributes have insufficient training data. The architecture also provides explanatory output like confidence levels for the predictions.

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A system for analyzing and matching personal real estate mortgage loan applications by inputting customer information online, evaluating the application, and suggesting appropriate loan categories and products based on preset rules. If the application cannot be automatically evaluated, a manual server intervenes to analyze it. The system involves a client for customer input, a server for analysis, and a database of loan products by category. It aims to provide accurate and comprehensive loan evaluation and matching compared to traditional methods using customer credit.

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Automated processing method for loan applications that uses machine learning models to assess repayment ability and creditworthiness without subjective judgment. The method involves determining loan applicants, predicting their income and expenditure, and calculating repayment capacity based on loan amount. This automated review replaces manual screening for loan scenarios like rental loans where repayment risk is high. It collects data like bank ratings, provident fund payments, and credit history to calculate actual repayment ability. This enables objective, consistent, and efficient loan reviews compared to subjective manual assessment.

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Interactive mortgage dashboard that pulls together multi-channel streams of mortgage information succinctly in one place and automatically processes it to take action and generate alerts. The dashboard combines internal loan data with external sources, identifies loan conditions, recommends actions, displays graphical indicators, and executes recommendations. It aims to provide a one-stop view of mortgage entities, requests, payments, collateral in real-time with auto refresh, processing, and alerts to improve warehouse lending efficiency.

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A decentralized system for optimizing loan approvals and providing instant financing to customers without exposing their personal information. The system involves executing multiple lender credit models using customer data in parallel at the loan originator's node. This allows comparing offers from multiple lenders without sharing customer info. A selection rule set picks the best loan. The selected offer is presented to the customer, who can claim it without further lender interaction. This provides optimized offers without multiple credit checks, avoids damaging credit scores, and enables point-of-need financing.

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