Artificial Neural Network and Analytical Hierarchy Process Integration: A Tool to Estimate Business Strategy of Bank

Objective – The disruption has been occurring in financial services. Thus, rethinking a new strategy for banking is needed to make a sustainable innovation in organizations. Studies mentioned that formulating strategy is a very costly, time-consuming, and comprehensive analysis. The purpose of this study is to present an integrated intelligence algorithm for estimating the bank’s strategy in Indonesia. Methodology – This study used the integration model between two modules. The algorithm has two basic modules, called Artificial Neural Network (ANN) and Analytical Hierarchy Process (AHP). AHP is capable of handling a multi-level decision-making structure with the use of five expert judgments in the pairwise comparison process. Meanwhile, ANN is utilized as an inductive algorithm in discovering the predictive strategy of the bank and used to explain the strategic factors which improved in forward. Findings and Novelty – The empirical results indicate that ANN and AHP integration was proved to predict the business strategy of the bank in five scenarios. Strategy 5 was the best choice for the bank and Innovate Like Fintechs (ILF) is the most factor consideration. The strategy choice was appropriate for the condition of the bank’s factors. This framework can be implemented to help bankers to decide on bank operations. Type of Paper: Empirical JEL Classification: M15, O32. Keywords: Bank’s strategy, ANN, AHP, BSC, Indonesia. Reference to this paper should be made as follows: Ristyawan, M.R. (2021). Artificial Neural Network and Analytical Hierarchy Process Integration: A Tool to Estimate Business Strategy of Bank, Journal of Finance and Banking Review, 5(4): 01 – 09. https://doi.org/10.35609/jfbr.2021.5.4(1)

Artificial Neural Network and Analytical Hierarchy Process Integration: A Tool to Estimate Business Strategy of Bank

The disruption has been occurring in financial services. Rethinking new strategy of banking is needed to make a sustainable competitive advantage innovation in organizations. The four types of business strategy for banks are prospector, analyzer, defender, and reactor. Studies mentioned that formulating strategy is very costly, time consuming, and comprehensive analysis. The banks have to get rid of execution time inefficiency, lack of flexibility, and lack of ability to present several scenarios in the dynamic business environment. The purpose of this study is to present an integrated intelligence algorithm for estimating strategic resources of the bank strategy in Indonesia. The algorithm has two basic modules which are artificial neural network (ANN) and analytical hierarchy process (AHP). ANN is utilized as an inductive algorithm in discovering predictive strategy of the bank and used to explain the strategic resources which improved in forward. AHP has the capability to handle multi-level decision-making structure with use of expert judgments in pairwise comparison process. AHP is used to measure the weight of the resources and the score is used to determine the strategy. The empirical results indicate that ANN and AHP integration is proved to predict the business strategy of the bank. The strategy choice appropriate with the condition of bank's resources. This framework can be implemented to help banker for the decision making in bank operation. Keywords: business strategy, ANN, AHP, resources

Inductive Decision Tree Analysis of the Validity Rank of Construction Parameters of Innovative Gear Pump after Tooth Root Undercutting

The article presents an innovative use of inductive algorithm for generating the decision tree for an analysis of the rank validity parameters of construction and maintenance of the gear pump with undercut tooth. It is preventet an alternative way of generating sets of decisions and determining the hierarchy of decision variables to existing the methods of discrete optimization.

Calibrating and Analyzing a Mathematical Model of Human Circulation and its Response to Hemorrhage

Complex physiological events such as hemorrhage are met with a continuum of responses in individual test subjects that range from complete compensation to circulatory failure. Predicting the circulatory outcome of an individual potentially affects treatment modalities, for example, by indicating that aggressive intervention is justified based on the likelihood of a negative result with a more passive therapy. We have previously determined an algorithm for calibrating and sampling parameter distributions that generate experimentally verified output distributions via an application of the Metropolis algorithm. This technique is advanced here by the addition of a three-pronged post hoc analysis. First is an inductive algorithm generating minimal parameter sets yielding efficient classification (MER). This algorithm is validated with PCA on the resulting parameter subsets. Finally, we provide an analysis on the response characteristics of clusters determined by a density dependent algorithm on the parameter/variable subspace indicated by the MER.