Abstract
The asymmetry of bank-enterprise information makes banks unable to ensure the comprehensiveness and authenticity of corporate data. Therefore, the innovation ability of the enterprise cannot be accurately assessed. The data of each bank is similar, but because each bank has a different understanding of these data, different knowledge will be formed. In this paper, the idea of internal knowledge transaction between banks is proposed. We propose The bank Knowledge Transaction Coverage Model to ensure the success of the knowledge transaction. The model includes three stages: knowledge encoding, knowledge coverage, and optimal selection. In this model, transaction bank set is selected to obtain the maximum knowledge coverage with the lowest cost. Since the number of participating banks is determined, in order to ensure that the model can be solved in polynomial time, the KCSA-GA algorithm is proposed. What’s more, the multi-objective optimization is transformed into minimization fitness function. Theoretical analysis and results demonstrate that compared with GA algorithm, KCSA-GA algorithm is superior in the distribution of fitness function and convergence in each iteration. After the knowledge transaction, the effectiveness of the model is verified by the innovation ability ranking comparing with the expert ranking and the Kendall rank correlation coefficient \(\tau \) is 91.74%.
This research is supported by the key laboratory of embedded system and service computing ministry of education (Tongji University).
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Zhu, M., Wang, Z., Feng, X., Wan, P., Shao, W., Tao, R. (2021). Research on Bank Knowledge Transaction Coverage Model Based on Innovation Capacity Analysis. In: Ning, L., Chau, V., Lau, F. (eds) Parallel Architectures, Algorithms and Programming. PAAP 2020. Communications in Computer and Information Science, vol 1362. Springer, Singapore. https://doi.org/10.1007/978-981-16-0010-4_25
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