Key challenges to digital financial services in emerging economies: the Indian context

Abstract

PurposeDigital financial services (DFS) have substantial prospect to offer a number of reasonable, appropriate and secure banking services to the underprivileged in developing countries through pioneering technologies such as mobile phone based solutions, digital platforms and electronic money models. DFS allow unbanked people to obtain access to financial services through digital technologies. However, DFS face tough challenges of adoption. Realising this, the purpose of this paper is to identify such challenges and develop a framework.Design/methodology/approachThe authors developed a framework of challenges by utilising interpretive structural modelling (ISM) and fuzzy MICMAC approach. The authors explored 18 such unique set of challenges culled from the literature and further gathered data from two sets of expert professionals. In the first phase, the authors gathered data from 29 professionals followed by 18 professionals in the second phase. All were pursuing Executive MBA programme from a metropolitan city in South India. The implementation of ISM and fuzzy MICMAC provided a precise set of driving, linkage and dependent variables that were used to derive a framework.FindingsISM model is split in eight different levels. The bottom level consists of a key driving challenge V11 (i.e. high cost and low return related problem), whereas the topmost level consists of two highly dependent challenges namely V1 (i.e. risk of using digital services) and V14 (i.e. lack of trust). The prescribed ISM model shows the involvement of “high cost and low return related problem (V11)”, which triggers further challenges of DFS.Originality/valueNone of the existing research has explored key challenges to DFS in detail nor formulated a framework for such challenges. To the best of the authors’ knowledge, this is the first paper on DFS that attempts to collate its challenges and incorporate them in a hierarchical model using ISM and further divide them into four categories of factors using fuzzy MICMAC analysis.