Revenue Stream Assetization via Continuous ML Scoring
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Solution Overview
Problem
The existing technology for online lending is non-linear, inconsistent, and time-consuming, requiring borrowers to go through the loan process again if their financial conditions improve, which taxes the computational resources of the lending platform.
Innovation Solution
A computer system that directly accesses a revenue stream and uses a machine learning model to continuously analyze and score the revenue stream, generating smart contracts on a decentralized network to manage repayments using stablecoins.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the loan process is repeated for borrowers with improved financial conditions, then the lending platform can reassess and adjust loan terms, but the computational resources are taxed and the process becomes time-consuming
Solution Approach 1:
The system performs preliminary actions by continuously monitoring and updating borrower financial data in real-time, so when a borrower applies for a new loan, the assessment can build upon previously collected and analyzed data rather than starting from scratch. This reduces the time required for repeated assessments while maintaining accuracy.
Solution Approach 2:
The system implements continuous action through ongoing monitoring of borrower financial conditions, credit scores, and repayment histories. This continuous data collection and analysis allows the platform to maintain updated profiles of borrowers, enabling faster loan processing decisions without sacrificing assessment thoroughness.
2Adaptability or versatility
If the loan process is repeated for borrowers with improved financial conditions, then the lending platform can adjust to new financial realities, but the computational resources are consumed
Solution Approach 1:
The system prepares and maintains updated borrower profiles, credit assessments, and financial models in advance. When a borrower's financial conditions improve, the platform can quickly reference this pre-collected data to determine appropriate loan term adjustments without performing complete re-assessments, thus reducing computational resource consumption while maintaining adaptability.
Solution Approach 2:
The system dynamically adjusts loan terms based on real-time changes in borrower financial conditions. By continuously updating borrower profiles and credit assessments, the platform can flexibly modify loan parameters such as interest rates, terms, and amounts to match current financial realities, optimizing both adaptability and computational efficiency.
3Adaptability or versatility
If the loan process is made non-linear and inconsistent, then the platform can handle diverse loan scenarios, but the process becomes complex and time-consuming
Solution Approach 1:
The system segments the loan process into distinct modules: data collection, credit assessment, risk evaluation, loan term determination, and disbursement. Each module handles specific aspects of loan scenarios independently, making the overall complex non-linear process more manageable and easier to implement while maintaining versatility across different loan types and situations.
Solution Approach 2:
The system introduces intermediary components such as automated credit scoring models, risk assessment algorithms, and data normalization layers that mediate between diverse input data and loan decision outputs. These intermediaries simplify the complexity by providing standardized processing steps that can handle various loan scenarios consistently, reducing the apparent complexity while maintaining adaptability.
Data Source
AI summary
A system includes a payment gateway that is configured to directly access a revenue stream and financial information about the revenue stream. The system continuously processes financial information about a borrower and the borrower's revenue stream to continuously analyze and score the revenue stream.