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Data science or even artificial intelligence research in cooperatives is at infancy level. However, there are a couple of researches done. Like in other enterprises, cooperatives must aggressively adopt new technologies that will propel them towards stardom performance. Application of data science on cooperative data has been demonstrated by Elliot, Elliot & Sluis (2018) who researched on predictive analytics to demonstrate how membership heterogeneity affects sustainability of cooperatives. Chopras, Jiang, Toulis & Golab (2018) exhibited how data analytics can be used to improve cooperative education. Human resource management (HRM) field is beginning to turn attention towards data science so as to help organizations gain insights from voluminous data in their databases. Data science is a field that combines several fields-information technology, computer science, mathematics, operations research and statistics so as to generate knowledge from a pool of data for decision making and prediction. Applied in HRM it is referred to as Human Resource (HR) analytics. It uses big voluminous data that can be used for prediction. Weihs and Ickstadt (2018) notes data science is influenced by mathematics, statistics, computer science and operation research. Further, the authors note that roots of data science were firmly laid by Tukey’s work on exploratory statistics as well as knowledge discovery in databases through data mining. Research studies on data science application within HRM field seems to be at infancy. This view is supported by Marler and Bondreau (2018) who note that adoption of HR analytics is slow and evidence from studies is limited. The authors further contents that HR analytics can be referred to as HR business intelligence, workforce analytics, people analytics and people research and analytics. Weihs and Ickstadt (2018) identify statistics for data science as descriptive statistics, predictive statistics and prescriptive statistics. Kremer (2018) proposes moderating variables in HR analytics such as data infrastructure, information technology and analytical skills by HR staff. Artificial neural network (ANN) has emerged as one of the potent tools in analyzing patterns that would not have been revealed before. There are a number of studies using ANN. Several studies have been done utilizing ANN in HRM (Khanjankhan, Askari, Rafiei, Perez-Campdesunar, De Miguel-Guzman Shaln, Hahemi & Shafii, 2017; Perez-Campdesunar, De-Miguel-Guzman, Sanchez-Rodriguez, Martinez-Viva, 2018; Somers, Birnbaum & Casal, 2018; Tung, Huang, Chen, & Shih, 2005; Wang & Shun, 2016). ANN analysis has a lot of value in HRM exclusively or in addition to regression models. Thus, this paper takes the view that ANN analysis and regression model analysis as data science techniques can complement each other. The paper also demonstrates output from ANN statistics and how to report them. Apart from formulating HRM variables to link with performance, and as a divergence from previous practice in selecting independent variables, the paper investigates synergetic relationship of HRM variables as an independent variable. Most studies on HRM and performance relationship (Dimba, 2008; Huselid, 1995; Khanjankan, Askari, Rafiei, Hasheni & Shafii 2017; Ngui, Mukulu & Gachunga, 2014; Spiliotis & Stavrou, 2007) focus on HRM practices but fail to deliberately study a key factor-the synergetic relationship among HRM practices and how it influences performance as dependent outcome and its multidimensional nature. The purpose of this paper was to investigate HRM performance link using ANN and demonstrate the relationship between HRM practices and performance. Additionally, the paper seeks to identify the most important variables that have the highest impact on financial cooperatives’ performance from both financial and non-financial perspective. The paper develops a conceptual model that predicts performance of financial cooperatives as theorized to have two dimensions-financial and non-financial.