About FinGuard AI

The research and the people behind FinGuard AI

FinGuard AI turns peer-reviewed research in forensic accounting and machine learning into an early warning system for financial distress and earnings manipulation.

Our mission

To make financial reporting more trustworthy by giving the people who oversee it an early, explainable warning of distress and manipulation, before investors, employees and the public pay the price.

We believe powerful risk analytics shouldn’t be a black box or reserved for the largest institutions. Every FinGuard score shows the evidence behind it, so professionals can act on it with confidence.

Who uses FinGuard

Auditors

Strengthen risk assessments and audit planning.

Accountants

Monitor financial health and support decisions.

Forensic accountants

Detect earnings manipulation and financial distress.

Financial crime analysts

Identify fraud risk and suspicious financial activity.

Regulators

Identify high-risk firms that need closer scrutiny.

Lenders & financial institutions

Strengthen credit risk evaluation.

Investors

Support investment and portfolio risk decisions.

Corporate leaders & boards

Monitor organizational performance and financial risk.

Meet the founders

Dr. Sana Ramzan, DBA

Co-Founder & Chief Research Officer

Sana is an accounting researcher, AI innovator and university educator. Her doctoral research at Royal Roads University showed how explainable machine learning can improve the prediction of financial distress and earnings manipulation. Her work is published in peer-reviewed journals and presented at international conferences. At FinGuard, she leads research and model development, making sure every score is grounded in evidence.

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Dr. Mark Lokanan, PhD

Co-Founder & Chief Scientific Advisor

Mark is a Professor and Intellectual Lead in Accounting at Royal Roads University, with more than 20 years of research in fraud detection, anti-money laundering and predictive risk modelling. Before academia, he was a fraud investigator with Ontario’s Ministry of the Attorney General. He has led government- and industry-funded research projects and provides FinGuard’s scientific direction.

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The research

Built on peer-reviewed research, not a black box

FinGuard’s models were developed and tested on two decades of NYSE and NASDAQ filings, drawing on published research in accounting, auditing, forensic accounting, financial distress prediction and machine learning. Every risk score maps back to the specific ratios and disclosures that drive it, so results are traceable and defensible.

Selected publications