# Financial Data Quality Evaluation Method Based on Multiple Linear Regression
## 4 Results
### Multiple Linear Regression Quality Evaluation Model  
We created the evaluation dataset using the label generation algorithm and the quality evaluation indicator system. The descriptive statistical results of various indicators of the trust agency's individual customer table and institutional customer table are shown in Tables 11 and 12. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Index** & **Maximum Value** & **Minimum Value** & **Average** & **Standard Deviation** \\ \hline integrity & 84.75 & 78.81 & 80.12 & 0.3 \\ accuracy & 97.83 & 82.1 & 92.33 & 0.28 \\ timeliness & 100 & 100 & 100 & 0 \\ timeliness & 100 & 100 & 100 & 0 \\ timeliness & 100 & 100 & 100 & 0 \\ consistency & 9.3 & 1.5 & 3.85 & 0.29 \\ uniqueness & 100 & 89.66 & 98.1 & 0.86 \\ Compliance & 50.66 & 11.5 & 43.21 & 2.03 \\ \hline \hline \end{tabular}
\end{table} 
Table 12: Descriptive statistical table of indicators in the institutional client table. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Field Chinese Name** & **Field English** & **Is** & **Null** & **Field Chinese Name** & **Field English** & **Is** & **Null** \\  & **Name** & **Empty** & **Ratio** & **Name** & **Empty** & **Ratio** \\ \hline name & CST\_NM & 0 & 0 & type of certificate & CRDT\_TP & 0 & 0 \\ ID number & CRDT\_NO & 0 & 0 & gender & GND & 4225 & 28.72\% \\ nationality & NAT & 5145 & 34.98\% & profession & OCP & 11,549 & 78.52\% \\ address & PHY\_ADR\_INF & 2859 & 19.44\% & contact information & CTC\_PER & 3850 & 26.17\% \\ certificate validity period & CRDT\_EFDT & 5156 & 35.05\% & customer number & CST\_ID & 0 & 0 \\ contact type & CTC\_PER\_TP & 14,709 & 1 & system source & SYS\_SRC & 0 & 0 \\ address type & PHY\_ADR\_TP & 2858 & 19.43\% & sales channels & SALE\_SRC & 0 & 0 \\ updater & UPT\_NM & 8744 & 59.45\% & update time & UPT\_TM & 8744 & 59.45\% \\ founder & CRT\_NM & 0 & 0 & creation time & CRT\_TM & 0 & 0 \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Individual customer completeness statistical results. Figure 3: Integrity statistics results. To examine the presence of correlations among dependent variables and between the dependent and independent variables, we performed a Pearson correlation analysis of the data. The results were visualized through a heatmap as depicted in Figure 4, showcasing the correlations between the variables. In the heatmap, black represents a negative correlation, white represents a positive correlation, and the diagonal line represents the correlation of each independent variable with itself, resulting in a correlation coefficient of 1. We observed that the correlation between the various indicator variables was not notably high, suggesting their independence from each other and the absence of multi-collinearity. Moreover, the correlation between the independent variables and dependent variables was quite strong. Among these correlations, the compliance_score exhibited the highest correlation, with a coefficient of 0.93. This result indicated that compliance, as one of the first-layer indicators, exerted the most significant influence on the final quality score. Additionally, the integrityaccuracy_score also demonstrated notable correlations with the quality score, with coefficients of 0.64 and 0.59, respectively. The modeling approach for the MLP model in this study had the following steps: (1) The indicator data and quality score data were normalized. (2) The dataset was divided into a training set (70% of the data) and a testing set (remaining 30% of the data). (3) The MLP model was constructed using the gradient descent algorithm to derive the optimal weight values for the model. The experimental results are shown in Figures 5 and 6. Figure 4: Heat map of correlation between variables. Figure 5: True value and predicted value of quality evaluation score. The Figure 5 shows the results of the real value and predicted value of the test set. Among them, the blue is the predicted value, and the red is the real value.