Predicting efficiency in Malaysian Islamic banks: A two-stage TOPSIS and neural networks approach

• This paper uses TOPSIS to assess the efficiency of Islamic Malaysian banks.
• TOPSIS is used in a two-stage approach with neural networks to predict performance.
• Results reveal that cost structure constitute the most important efficiency driver.
• Findings also indicate the inexistence of a learning curve in the Malaysian banking sector.

This paper presents an efficiency assessment of the Malaysian Islamic banks using TOPSIS. In this research, this technique is applied first in a two-stage approach to assess the relative efficiency of Malaysian Islamic banks using the most frequent indicators found in the banking literature. Besides, in the second stage, neural networks are combined with TOPSIS results as part of an attempt to produce a model for banking performance with effective predictive ability. The results reveal that variables related to cost structure have a prominent negative impact on efficiency levels, although some parsimony in equity leveraging derived from Islamic finance principles maybe helpful in achieving higher efficiency levels. Findings also indicate that the Malaysian Islamic banking market also imposes cultural and regulatory barriers to foreign banks, so that their efficiency levels are lower when compared to their national counterparts. Learning curves (trend impact) are relevant in predicting efficiency levels.

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