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TFC

Truist

A reporting-period timeline of the company’s AI and machine-learning disclosures, grounded in official investor materials and exact source excerpts.

AI strategyRisk and governance
Reporting history

AI disclosures by reporting period

Documents with no explicit AI/ML evidence are shown as such. Silence is not interpreted as inactivity.

Quarterly report

Truist quarterly report filed 31 Jul 2026

SEC EDGAR

Reporting period ended 30 Jun 2026

Primary source

Truist includes a definition for artificial intelligence within the glossary of its quarterly report. The term is defined to encompass machine learning and other types of artificial intelligence. No further strategic, financial, or operational AI disclosures are present in the provided excerpt.

AI strategyDirection not stated

AI defined within glossary of terms

Truist defines the term AI in its glossary to encompass artificial intelligence, including machine learning and other types of artificial intelligence.

“AI Artificial intelligence, including machine learning and other types of artificial intelligence”

Context: Glossary of Defined Terms

Annual report

Truist annual report filed 24 Feb 2026

SEC EDGAR

Reporting period ended 31 Dec 2025

Primary source

Truist identifies artificial intelligence as a key element of its operational and cybersecurity risk landscapes. The bank notes that AI-enabled technologies and advancements in generative AI can expand its cyber-attack surface, enabling malicious actors to launch more sophisticated social engineering attacks. Additionally, Truist faces internal operational and regulatory risks related to AI model deployment, potential algorithmic bias, and evolving legal frameworks surrounding data and model usage rights.

Risk and governanceContinuing

Malicious actors leverage generative AI to escalate cybersecurity threats

Truist expects to continue being targeted by cybersecurity threats with increasing frequency and severity, driven in part by malicious actors utilising machine learning and generative AI to conduct advanced social engineering attacks, such as targeted phishing and smishing.

“advances in AI, such as the use of machine learning, generative AI, and quantum computing by malicious actors to develop more advanced social engineering attacks on the Company or its clients, including targeted phishing and smishing attacks”

Context: Part I, Item 1A. Risk Factors

Risk and governanceContinuing

AI models and datasets present inherent quality, transparency, and legal risks

Truist acknowledges that its internal or third-party AI implementations face risks of being flawed, biased, or lacking transparency. Furthermore, evolving regulatory and legal frameworks around data rights, intellectual property, and privacy present compliance risks if the bank lacks sufficient rights to the datasets or models utilised.

“that models, prompts, algorithms, and datasets, as well as related decisions, predictions, analysis, and other output, are flawed, inaccurate, of poor quality, insufficient, biased, or otherwise erroneous or inadequate, any of which may not be easily detectable. In addition, the models and processes relating to AI are not always transparent, which could increase the risk of unintended deficiencies.”

Context: Part I, Item 1A. Risk Factors

Quarterly report

Truist quarterly report filed 30 Oct 2025

SEC EDGAR

Reporting period ended 30 Sept 2025

Primary source
No explicit AI or machine-learning disclosure was found in this results document.
Quarterly report

Truist quarterly report filed 31 Jul 2025

SEC EDGAR

Reporting period ended 30 Jun 2025

Primary source
No explicit AI or machine-learning disclosure was found in this results document.