HAFC · CA · Mid-size bank ($1B to $50B)
Total assets of FDIC-insured bank subsidiaries: $7.8B at the end of 2025
Filings on the SEC website · This bank on Bankgraph
| Report year | Using or planning AI | Explains how AI is controlled | Sees AI as a risk | Other mentions |
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| 2025 |
| In the 2025 report | This bank | Banks of its size |
|---|---|---|
| Using AI now | No | 26 of 221 (12%) |
| Explains how AI is controlled | No | 55 of 221 (25%) |
| Sees AI as a risk | Yes | 184 of 221 (83%) |
| Mentions generative AI | No | 112 of 221 (51%) |
| Mentions AI agents | No | 18 of 221 (8%) |
6 passages new in the 2025 report, 0 passages from the 2024 report no longer there. The most specific passage is more detailed than last year.
Our reliance on, and integration of, artificial intelligence ("AI") and machine learning ("ML") technologies expose us to various risks, including operational, data, regulatory, and reputational risks, which could materially affect our business and financial results.
Operational and model risk. AI/ML models may rely upon complex algorithms and vast data sets. Errors, biases, generation of false information in these models, or unexpected system failures could lead to flawed decisions, financial losses, compliance failures, or degraded customer experiences, impacting profitability and customer retention. We utilize certain AI/ML models provided by third-party vendors, including models used for credit scoring and fraud detection, and may use other AI/ML models in the future.
Data security and privacy. AI/ML systems may process sensitive customer data. Security breaches or unauthorized access to these systems could result in data theft, loss of intellectual property, and significant penalties, damaging customer trust.
Regulatory compliance risk. The regulatory landscape for AI/ML is rapidly evolving. New laws could impose costly compliance burdens, restrict AI/ML usage, or introduce liabilities, particularly concerning algorithmic bias and fair lending practices (e.g., "digital redlining"), potentially increasing operational costs and limiting service offerings.
Talent and third-party risk. Attracting and retaining skilled AI/ML professionals is crucial and competitive. We also depend upon third-party AI/ML vendors, creating dependency risks and potential issues relating to data handling, model reliability, and licensing, all of which could disrupt operations. While we do not currently develop AI or ML models internally, future expansion of our AI/ML capabilities may require specialized technical skill sets, and we could face challenges attracting and retaining qualified AI/ML talent if those needs arise.
Reputational and ethical risk. Misuse of AI/ML, biased outcomes, or privacy violations can harm our brand, erode customer confidence, and attract negative public attention, potentially affecting demand for our services.