AI Robotic Process Configuration for Automated Lending Workflows
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Solution Overview
Problem
Lending transactions face challenges such as opacity and asymmetry of information, moral hazard, complexity in application and negotiation processes, burdensome regulatory regimes, and difficulties in determining collateral value and financial health of entities.
Innovation Solution
A lending transaction enablement platform with integrated microservices including data collection, blockchain, and smart contract services, utilizing IoT, crowdsourcing, and social network analytics to monitor assets and entities, automate interest rate adjustments, and facilitate robotic process automation for negotiation and loan management.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If traditional manual processes are used for lending transactions, then human judgment and flexibility are maintained, but information asymmetry and opacity increase, and processing time increases
Solution Approach 1:
The patent replaces manual mechanical review processes with automated robotic process automation (RPA) systems that collect, analyze, and evaluate lending data through software agents. These digital agents access multiple data sources, perform credit assessments, and generate decisions without human intervention, thereby reducing information asymmetry while increasing automation.
Solution Approach 2:
The system implements continuous feedback loops where RPA agents monitor lending transactions, collect data from external sources, analyze outcomes, and refine their decision-making algorithms. This feedback mechanism ensures that information asymmetry is progressively reduced as the system learns from actual transaction data and improves its assessment accuracy over time.
2Productivity
If complex manual negotiation and application processes are used, then flexibility in handling unique cases is maintained, but processing time and operational complexity increase
Solution Approach 1:
The patent segments the lending process into distinct modular stages: data collection, credit assessment, negotiation, approval, and monitoring. Each stage is handled by specialized RPA agents with specific functions. This segmentation enables parallel processing of multiple applications simultaneously, dramatically increasing productivity while managing complexity through functional decomposition.
Solution Approach 2:
The RPA system implements universal standardized processes that can handle diverse lending cases through a common framework. The same automated agents process different loan types (personal loans, mortgages, business loans) using consistent criteria and workflows, thereby increasing processing speed while reducing operational complexity through standardization.
3Reliability
If traditional lending systems are used, then established procedures are followed, but regulatory compliance burden and information opacity increase
Solution Approach 1:
The patent replaces manual compliance checking with automated RPA agents that systematically verify regulatory requirements, anti-money laundering rules, and lending regulations. These digital agents automatically cross-check applicant data against regulatory databases, generate compliance reports, and ensure adherence to legal requirements, thereby improving reliability while managing system complexity through automation.
Solution Approach 2:
The system introduces intermediary RPA agents that act as mediators between the lending process and regulatory requirements. These agents translate complex regulatory rules into automated checklists and validation protocols, serving as an intermediary layer that simplifies compliance monitoring while maintaining high reliability through systematic rule enforcement.
4Measurement precision
If manual collateral valuation and financial health assessment are used, then expert judgment is applied, but determination accuracy and processing time are compromised
Solution Approach 1:
The patent implements universal automated valuation models that can assess diverse collateral types (real estate, vehicles, equipment, inventory) using standardized algorithms. These RPA agents access multiple data sources (market prices, appraisal databases, asset registries) and apply consistent valuation methodologies across different asset classes, thereby improving measurement precision while reducing assessment time through parallel processing.
Solution Approach 2:
The system replaces manual expert appraisal with automated RPA agents that use algorithmic models to evaluate collateral value and assess borrower financial health. These digital agents analyze financial statements, credit histories, and market data instantaneously, providing accurate measurements without the time constraints of manual review while maintaining consistency through standardized algorithms.
Data Source
AI summary
A system for selection and configuration of a robotic process includes a data input module to receive a stream of inputs relating to a user engaged in a task of interest, an input analysis module to analyze the stream of inputs and provide a series of timestamped actions and associated action parameters, and a component selection module to select a component of an AI solution for use in an automated robotic process, based on, at least in part, an action of the series of actions, the associated action parameters, or the components ability to simulate one or more of the actions in the series of actions.