This study examines whether structured explanations can overcome the negativity bias in consumer trust recovery following algorithmic errors in digital financial advisory services. Using a three-round experimental design with 294 participants, we tested a temporal trust dynamics framework comprising trust formation, single-error shock, and post-error repair stages. Results demonstrate that brief post-error explanations achieved substantial trust recovery, restoring 79-92% of initial trust damage across trust, satisfaction, and reliance measures. Financial literacy moderated these effects, with high-literacy participants showing 15-20% greater recovery levels. Round-by-explanation interactions were significant for all outcomes, confirming that explanations can quantitatively reverse negativity bias effects. Recovery Ratio analyses provide concrete benchmarks for financial service providers, suggesting a minimum restoration rate of 80% as performance targets. These findings have significant implications for consumer protection policy and financial service design, demonstrating that effective error management can maintain consumer confidence, prevent costly switching behaviors, and protect family economic well-being.

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