Monte Carlo Simulation Machine Learning Risk Assessment

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Solution Overview

Problem

Monte Carlo simulation methods provide approximations and non-optimal results, which are inadequate for applications seeking definite answers, particularly in risk assessment and management, such as financial risks, as they lack precision and do not offer enhanced or optimal solutions.

Innovation Solution

The integration of machine learning with Monte Carlo simulations to repeatedly run scenarios and refine results, using the outputs as input for machine learning processes until enhanced or optimal answers are achieved, specifically for financial risk management by optimizing household and financial portfolios.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Measurement precision

If Monte Carlo simulation methods are used to evaluate scenarios, then a great number of scenarios can be analyzed to discover risks, but the results provide only approximations and are not precise enough for definite answers

Engineering Contradiction:

Improveprecision of risk assessment resultsVSAvoiddefiniteness of answers

Core Design Contradiction:

Measurement precisionVSReliability

Solution Approach 1:

The patent implements feedback by using machine learning models to learn from Monte Carlo simulation results and generate refined recommendations. The system continuously iterates between simulation and learning, where the ML model adjusts its predictions based on simulated scenario outcomes, thereby improving the precision and definiteness of risk assessment answers through iterative refinement.

Inventive Principle:

Principle #23Feedback

Solution Approach 2:

The patent introduces machine learning models as an intermediary between Monte Carlo simulations and final risk assessment conclusions. The ML model acts as a mediator that processes the probabilistic outputs from simulations and transforms them into more definitive recommendations, bridging the gap between approximation and precision.

Inventive Principle:

Principle #24Intermediary (Mediator)

2Reliability

If Monte Carlo simulation methods are used to evaluate scenarios, then risks can be discovered amongst scenarios, but the results provide non-optimal solutions and lack enhanced answers for avoiding risks

Engineering Contradiction:

Improveability to discover risksVSAvoidquality of solutions for risk avoidance

Core Design Contradiction:

ReliabilityVSEase of operation

Solution Approach 1:

The system uses feedback loops where machine learning models learn from simulated risk scenarios and continuously improve risk avoidance recommendations. The ML models analyze patterns from multiple simulated scenarios and generate enhanced solutions that go beyond simple Monte Carlo outputs, providing actionable insights for optimal risk management.

Inventive Principle:

Principle #23Feedback

Solution Approach 2:

The patent replaces the purely mechanical Monte Carlo simulation approach with a hybrid system incorporating machine learning. The ML component substitutes for the limitation of traditional simulation by interpreting results and generating optimized recommendations, transforming raw simulation data into enhanced risk avoidance strategies.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

3Adaptability or versatility

If Monte Carlo simulation methods are used, then probabilistic problems can be solved through repeated random sampling, but further processing is needed when definite answers are required

Engineering Contradiction:

Improveability to solve probabilistic problemsVSAvoidprocessing requirements for definite answers

Core Design Contradiction:

Adaptability or versatilityVSDevice complexity

Solution Approach 1:

The patent merges Monte Carlo simulation with machine learning into an integrated system. The combination allows the system to maintain the adaptability of probabilistic simulation while incorporating ML's ability to extract definitive patterns, thereby reducing the need for extensive post-processing and directly providing definite answers when required.

Inventive Principle:

Principle #5Merging (Combining)

Solution Approach 2:

Machine learning models serve as an intermediary that processes probabilistic simulation outputs and transforms them into definite answers. This intermediary layer reduces the complexity of further processing by automatically interpreting simulation results and generating conclusive recommendations, eliminating the need for manual analysis of probabilistic data.

Inventive Principle:

Principle #24Intermediary (Mediator)

Data Source

PatentUS11468384B2Scenario evaluation and projection using Monte Carlo simulation and machine learning

Publication Date: 2022.10.11 KULKARNI NEIL PRADEEP

AI summary

Described herein are improved systems and methods for overcoming technical problems associated with the use of Monte Carlo simulation methods, such as problems associated with applications of Monte Carlo simulation methods that are searching for more definite answers. In some embodiments described herein, improved systems and methods overcome the technical problem of the results of Monte Carlo simulations providing approximations and/or non-optimal results (or at least non-enhanced results). Thus, such embodiments can provide more enhanced answers to limiting risks; and in some cases, such embodiments can even provide optimal answers to limiting risks. In some embodiments, machine learning can be used to provide more enhanced answers to limiting risks; and in some cases, such embodiments can use machine learning to provide optimal answers to limiting risks discovered through Monte Carlo simulations.