Focusing on regional financial services, this paper analyzes the variables affecting non-financial businesses getting loans through the region economic services function, and then proposes countermeasures to improve it. The DPC algorithm is applied to the financial analysis in this paper because of its fast speed and high accuracy. The AD-DPC approach is suggested in this study as a solution to the issue that the computation of local density relies on the choice of the truncation length parameter dc and the clustering sites must be manually chosen. This strategy lessens the subjectivity and volatility that the fictitious label dc brings. For the DPC algorithm by using a one-step assignment strategy, i.e., assigning the labels of clustering centers to all non-clustering centroids at one time, such a strategy is poorly fault-tolerant, this paper proposes the DAS-DPC algorithm on the basis of AD-DPC. Through experiments, ADAS-DPC is optimal for ARI metrics in the dataset. Among them, the ARI indexes of ADAS-DPC algorithm are 0.832, 0.895, 0.768 and 0.757 in the datasets Iris, Wine, Seed and Sonar. It shows that the ADAS-DPC algorithm can not only handle the datasets with complex shapes, large density differences between clusters and tightly connected clusters, but also improve the clustering performance of the algorithm for high-dimensional data.