Machine Learning Model Selection for Business Metric Prediction
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current methods for predicting corporate liquidity and other business metrics are inefficient and lack accuracy, as they do not effectively utilize machine learning techniques to analyze and project financial data from various business entities, leading to suboptimal decision-making in corporate finance and strategy.
Innovation Solution
The use of machine learning algorithms trained on data from multiple businesses to predict corporate liquidity and other metrics, such as market capitalization and credit ratings, by selecting the most accurate model from a set of candidates generated using various algorithms like Ridge Regression, Gradient Boosting, and Random Forest learning, to provide insights on optimal financial strategies and future projections.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional methods are used to predict corporate liquidity, then the process is simple, but the accuracy and reliability of predictions are insufficient
Solution Approach 1:
The patent segments the prediction process into multiple independent machine learning models (Ridge Regression, Lasso Regression, Gradient Boosting, Random Forest, etc.), each handling different aspects of the prediction. This allows the system to evaluate multiple specialized models rather than relying on a single complex model, improving accuracy while maintaining manageable complexity through modular evaluation.
Solution Approach 2:
The patent changes the parameters by using multiple different machine learning algorithms with varying complexity levels and mathematical approaches. By comparing predictions across models with different parameter structures (linear models vs. tree-based models vs. regularization approaches), the system achieves higher prediction accuracy without committing to excessive complexity in any single model.
2Measurement precision
If multiple machine learning algorithms are applied to generate candidate models, then the prediction accuracy improves, but the computational time and processing complexity increase
Solution Approach 1:
The patent applies partial action by generating multiple candidate models using different algorithms but evaluating them selectively. The system doesn't need to exhaustively analyze every possible model configuration - instead, it generates a representative set of candidate models using key algorithms and evaluates their performance on validation data, achieving sufficient accuracy without excessive computational investment in model selection.
Solution Approach 2:
The patent performs preliminary action by training and evaluating multiple candidate models on validation data before deploying the final prediction model. This advance evaluation allows the system to identify the most accurate model beforehand, so that when making actual predictions, the system can use the pre-selected optimal model efficiently without repeating the full model evaluation process.
3Reliability
If data from multiple business entities is used for training, then the model generalization improves, but the data processing complexity and computational resources increase
Solution Approach 1:
The patent applies universality by using the same machine learning framework and processing pipeline to handle data from multiple different business entities across various industries and contexts. The system processes diverse input data (cash holdings, cash flows, assets, liabilities, credit terms) through a unified multi-model approach, achieving good generalization without requiring separate complex processing systems for each data type or entity.
Solution Approach 2:
The patent segments the diverse training data into standardized input features that can be processed by multiple machine learning models. By organizing data from different business entities into consistent numerical features (liquidity ratios, cash flow metrics, asset classifications), the system can apply the same processing logic across heterogeneous data sources, reducing overall processing complexity while maintaining generalization capability.
Data Source
AI summary
This disclosure describes techniques that include predicting a business metric associated with a given business enterprise using machine learning algorithms that have been trained on data, from other businesses, about that same business metric. In one example, this disclosure describes a method that includes selecting, from a data set, a training data subset and a test data subset; generating a plurality of candidate models configured to generate a predicted output metric, wherein the plurality of candidate models is generated by applying a plurality of machine learning algorithms to the training data subset; evaluating, using the test data subset, each of the plurality of candidate models to select a chosen model; receiving production data that includes data representing input metrics for a business entity not included within the plurality of business entities; and generating a predicted output metric for the business entity.