Unlike prior studies that attempted to detect audit review findings and earnings management possibilities using financial data alone, this study analyzed these issues using the discrepancies between financial information and related non-financial information. This approach is based on the premise that non-financial measures have the potential to provide powerful and independent benchmarks for evaluating the validity of financial statement data (PCAOB, 2007), and on the New International Standards on Auditing (New ISA), which stipulate that analytical review procedures should evaluate financial information by analyzing the plausible relationships between financial and non-financial data, and that considering non-financial data when assessing fraud risk can be useful. It is also grounded in the fact that non-financial measures are relatively more difficult for management to manipulate and can be more easily identified by auditors compared to financial measures (Brazel et al., 2009). Furthermore, this study was conducted as an extension of Brazel et al.'s (2009) research, which detected accounting fraud through the discrepancy between the rate of change in sales revenue (a financial measure) and the rate of change in the number of employees (a non-financial measure), and Dechow et al.'s (2011) study, which empirically demonstrated that fraud risk red flags could be detected using the discrepancy between the rate of change in total assets (a financial measure) and the rate of change in the number of employees (a non-financial measure). Therefore, this study used the rate of change in the number of employees—publicly available and validated in the aforementioned prior studies as being interrelated with financial measures such as the rate of change in sales revenue and total assets—as the non-financial measure. Based on this, to examine whether the discrepancy between the rates of change in sales revenue and number of employees, and the discrepancy between the rates of change in total assets and number of employees, could identify the possibility of earnings management and manipulation, the study reviewed the identifiability of firms flagged in audit reviews, firms suspected of loss avoidance, and firms suspected of earnings decrease avoidance after controlling for discretionary accruals (Kothari et al., 2005) and real earnings management measures (Roychowdhury, 2006). The analytical results of this study are as follows. First, the discrepancy between the rate of change in total assets and the rate of change in the number of employees was significant in detecting audit review findings, indicating the identifiability of accounting fraud risk. Second, the discrepancy between the rate of change in sales revenue and the rate of change in the number of employees was significant in identifying loss-avoiding firms and was also significant in identifying earnings decrease-avoiding firms. This indicates the possibility that discrepancies between financial and non-financial measures can be used to detect earnings management. Taken together, these findings are significant in empirically demonstrating that various stakeholders surrounding a firm can gauge the possibility of earnings management and accounting fraud by examining non-financial information that is highly related to financial information.