SigmaCore.ai Benchmark Study Reveals Standard AI Achieves Only 63.5% Accuracy in Financial Mathematics
SigmaCore.ai Benchmark Study Reveals Standard AI Achieves Only 63.5% Accuracy in Financial Mathematics
PR Newswire
ALPHARETTA, Ga., Nov. 3, 2026
SigmaCore's deterministic calculation engine delivers 100% precision, eliminating structural calculation flaws common in standard generative models.
ALPHARETTA, Ga., Nov. 3, 2026 /PRNewswire/ -- SigmaCore.ai, an enterprise developer of deterministic AI systems, recently released the findings of a comprehensive benchmark study evaluating Large Language Model (LLM) calculation reliability in commercial financial deployments. The full research paper, titled "Can We Trust the AI to Do the Math?", highlights a systemic gap in precision across traditional generative models and outlines how SigmaCore's flagship engine, ElizaZH ™ (US Patent App 19/770,254), in short Eliza, maintains zero-defect computational performance.
Key Benchmark Findings Across 3,400 Financial Test Cases:
- Generative LLMs (Direct Computation): 63.5% exact precision rate.
- Augmented LLMs (Code Execution Enabled): 94.7% exact precision rate.
- Eliza Deterministic Computation Engine: 100% exact precision rate.
The rigorous multi-model study observed 10,200 total experimental executions assessing 34 standard commercial financial calculations, including loan amortization payment schedules, three-year financial projections, and break-even financial assessments utilized in Small Business Administration (SBA) application streams. Calculations were executed across 100 randomized market scenarios.
The benchmark study revealed that while augmenting standard LLMs with programmatic code execution reduced minor math slipups, it introduced deep logic vulnerabilities due to model confusion regarding correct formula selection. In two retail financial scenarios, accuracy plummeted to 18% when code tools were active, because the models mistakenly generated flawed financial formulas with high structural confidence. Crucially, the data points to no reliable proxy metrics—such as answer length or operational difficulty—capable of warning users of an error, with precision degrading sharply as arithmetic complexity advanced.
SigmaCore.ai protects enterprise accuracy across three core products:
- Eliza: Guides SMB owners through conversation-driven workflows to generate bank-ready business plans, three-year financial models, and complete SBA loan documentation.
- Lex: Automates compliance routing and streamlines legal workflows.
- Azile: Delivers instant, automated financial verification and audit functionality.
The complete technical framework, validation methodology, and dataset access parameters are available directly on request.
This study marks the latest release from SigmaCore.ai Inc.'s proprietary, multi-phase research initiative targeting generative AI hallucination trends. Spearheaded by SigmaCore.ai CEO Professor Subhashish Samaddar and his corporate architecture team—resident AI expert Mr. Ravi Periasamy and AI drift researcher Dr. Shyam Dasari—the project showcases SigmaCore.ai's wholly-owned Eliza platform as a prime example of successful regional technology commercialization.
Dr. Samaddar from GSU and SigmaCore.ai notes: "While SigmaCore.ai retains full asset ownership and research provenance, the deployment of Eliza highlights how, under the leadership of Dean Richard Phillips, the Robinson College of Business (RCB) and its Entrepreneurship Institute (ENI) foster innovation. This vital ecosystem—built on university and industry collaboration—incubates cutting-edge corporate AI research and accelerates a vibrant regional tech startup culture."
As a testament to this exemplary startup culture, as we are sending this to the press, we received the word, that SigmaCore.ai has been selected as a finalist in the Georgia AI Pitch Competition, hosted in partnership with Eficens Systems, AWS, SBA, and the Georgia SBDC. The company receives an award of $25,000 in PoC funds and qualifies to win $100,000 grand prize. Additionally, SigmaCore.ai is currently in advanced discussions with multiple financial institutions regarding potential Eliza deployments.
About SigmaCore.ai
SigmaCore.ai builds deterministic AI systems designed for high-stakes enterprise applications across finance, law, and business operations. By replacing generative approximation with rule-bound calculation engines, SigmaCore.ai delivers absolute accuracy, regulatory compliance, and total auditability for business-critical workflows.
Media Contact:
SigmaCore.ai Press Office
Dylan Archibald
dylan.archibald@sigmacore.ai
www.sigmacore.ai
SOURCE SigmaCore.ai Inc.
The articles, information, and content displayed on this webpage may include materials prepared and provided by third parties. Such third-party content is offered for informational purposes only and is not endorsed, reviewed, or verified by Morningstar.
Morningstar makes no representations or warranties regarding the accuracy, completeness, timeliness, or reliability of any third-party content displayed on this site. The views and opinions expressed in third-party content are those of the respective authors and do not necessarily reflect the views of Morningstar, its affiliates, or employees.
Morningstar is not responsible for any errors, omissions, or delays in this content, nor for any actions taken in reliance thereon. Users are advised to exercise their own judgment and seek independent financial advice before making any decisions based on such content. The third-party providers of this content are not affiliated with Morningstar, and their inclusion on this site does not imply any form of partnership, agency, or endorsement.
Popular
3 Stocks to Sell and 3 Stocks to Buy for November
3 of the Best Dividend Stocks to Own
The 10 Best Companies to Invest in Now
This Once-Hot AI Stock Now Looks Deeply Undervalued
United States