Credit Limit Calculation Using Virtual Obligor Data and Risk Metrics
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional methods for setting credit limits in financial institutions do not adequately consider risk amounts, leading to inappropriate credit limit settings and potential increased risk due to concentration on a few obligors, and fail to account for the actual number of obligors effectively.
Innovation Solution
An information processing apparatus that creates virtual data for effective obligor numbers, determines post-transition ratings using Monte Carlo or Quasi-Monte Carlo methods, calculates risk amounts based on these ratings, and sets a minimum diversification object number to ensure the credit limit is set based on a confidence level, thereby reducing risk and enhancing capital adequacy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If the credit limit is set based on expected loss (EL) alone, then the calculation is simple, but the risk amount is not appropriately considered leading to inappropriate credit limit settings
Solution Approach 1:
The patent changes the parameter from simple expected loss (EL) to a more comprehensive risk metric that incorporates probability of default (PD), loss given default (LGD), exposure at default (EAD), and correlation coefficients. This parameter transformation enables more accurate risk measurement while maintaining computational feasibility through structured formulas.
Solution Approach 2:
The patent introduces an intermediary risk calculation layer that bridges the gap between simple EL and complex risk assessment. By computing risk amounts through intermediate steps involving PD, LGD, EAD, and correlation adjustments, the system achieves accurate risk measurement without direct complex modeling.
2Ease of operation
If the credit limit is set without considering the actual number of obligors, then the process is straightforward, but the credit limit setting does not appropriately account for risk concentration
Solution Approach 1:
The patent implements a feedback mechanism where the calculated risk amount feeds back into the credit limit determination process. The system continuously adjusts credit limits based on risk calculations that incorporate the number of obligors, ensuring that credit allocation reflects actual risk exposure and concentration levels.
Solution Approach 2:
The patent makes the credit limit setting dynamic by incorporating the actual number of obligors as a variable factor. As the number of obligors changes, the risk calculation and subsequent credit limit determination automatically adjust, allowing the system to adapt to changing portfolio compositions and risk concentrations.
3Device complexity
If credit is concentrated on a few obligors, then the management process is simplified, but the risk amount increases and capital adequacy is compromised
Solution Approach 1:
The patent applies equipotentiality by distributing credit exposure across multiple obligors to equalize risk levels. The system calculates optimal credit limits that balance the portfolio, ensuring no single obligor or small group creates excessive risk concentration, thereby maintaining capital adequacy while managing complexity.
Solution Approach 2:
The patent segments the credit portfolio into multiple obligor exposures rather than concentrating credit on few entities. By dividing total credit allocation across numerous obligors, the system reduces individual exposure risks while maintaining overall portfolio management efficiency through aggregated risk metrics.
Data Source
AI summary
Aspects of the invention relate to creating virtual data of an effective obligor number N based on setting information, determining ratings after transition of the virtual data for a plurality of scenarios according to rating transition data representing transition of each rating, calculating a risk amount for each of the scenarios based on the rating after transition of the virtual data and specifying a risk amount corresponding to a confidence level as a predicted risk amount of the virtual data, setting a minimum effective obligor number N among the effective obligor numbers N when predicted risk amounts smaller than an allowable risk amount are specified, as a diversification object number, and calculating a credit limit based on the set diversification object number and a total credit.