Factored Scoring is a structured way to make decisions by looking at multiple criteria at once, assigning each of them a weight, and then calculating a combined score. Instead of relying on just one factor like price, skills, or performance, it balances several dimensions so that decision-making is fairer, consistent, and transparent.
For example, in Orion AI, factored scoring might evaluate a stock by blending fundamentals (like earnings and revenue growth), technical indicators (such as moving averages), and sentiment (news coverage or investor mood). Each factor has its own importance, and when combined, they create a more reliable stock rating than any single metric on its own. In Freddie AI, the same approach could be applied to recruitment: weighing a candidate’s skills, past experience, test results, and cultural fit to produce an overall ranking that hiring managers can trust.
The real power comes when AI enters the picture. Unlike traditional scoring systems that rely on static rules, AI can continuously process huge datasets, test how well the model works over time, and adjust the weightings dynamically. This makes the scoring system smarter and more accurate, because it learns which factors are truly predictive of success. Businesses benefit by making decisions that are not only data-driven but also context-aware, whether in investing, hiring, or customer analysis.
Factored Scoring is a method of evaluation that assigns weights to multiple criteria, then calculates a composite score to rank options. In Orion AI, factored scoring might weigh fundamentals, technical indicators, and sentiment to produce a final stock rating. In Freddie AI, it could combine skills, experience, test results, and cultural fit to rank candidates. Factored scoring improves decision-making by ensuring that no single criterion dominates the evaluation process. It also enables customization for different industries or use cases, making it adaptable to various decision contexts. AI enhances factored scoring by processing large datasets and continually refining the weighting model based on historical performance.
