Abstract
The present study aims at understanding and analyzing the COVID-19-induced behavioral change spurting artificial intelligence (AI) adoption in Indian banking industry. The study has further identified and analyzed the usage pattern of Indian customers for mobile banking/online banking services in the pre-pandemic phase and progression of Indian customers for mobile banking/online banking services during the pandemic. Secondary data has been used for deep understanding of the AI adoption in Indian banking industry, with reports from McKinsey, PWC, RBI, NPCI, BIS, etc., to form the base. The period of study was taken from 2016 to 20, and this was taken keeping in mind the timing of another unprecedented event of demonetization. Behavioral change of Indian banking industry customer was assessed on three broad parameters change in value and volume of mobile banking transactions on year on year basis. COVID-19-induced behavioral change translating in massive jump of 178% in volume of mobile transactions between March 2019 and 2021. The increase in number of smart phone users and access to connectivity and desired technology has helped the cause. With 2020–21 punctuated by several nationwide as well as localized lockdowns adoption of AI for customer engagement has been crucial for Indian banking industry, which has further translated in to designing and customizing products and risk profiling of customers further resulting in increased operational efficiency and intuitive decision making. The behavioral change induced by COVID-19 in the Indian baking industry achieves competitive advantage by truly responding to huge customer data base which has been utilized by other financial industries as now it can have systems which understand and are responsive to behavior of varied customers. From responses feeded chatbots to intuitively responsive AI bots, the customer engagement is going to be a whole new experience which will help in customer acquisition and retention. Further, with falling data storage costs, increasing processing speeds and capabilities and improved connectivity and access for all has helped the rapid automation and AI adoption. Enterprise level adoption of AI has led to revenue generation and optimization of functional resources this reducing the cost at functional level. The AI adoption has been continuous from the banks over the years though banks have started to harness its potential in the recent years with customer’s adoption of smart hand-held devices.
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References
Lee Y-K, Park J-H, Chung N, Blakeney A (2012) A unified perspective on the factors influencing usage intention toward mobile financial services. J Bus Res 65(11):1590–1599
Bruner GC II, Kumar A (2005) Explaining consumer acceptance of handheld internet devices. J Bus Res 58(5):553–558
Kuisma T, Laukkanen T, Hiltunen M (2007) Mapping the reasons for resistance to internet banking: a means-end approach. Int J Inf Manage 27(2):75–85
Chen C (2013) Perceived risk, usage frequency of mobile banking services. Manag Serv Qual 23(5):410–436
Martins C, Oliveira T, Popovic A (2014) Understanding the internet banking adoption: a unified theory of acceptance and use of technology and perceived risk application. Int J Inf Manage 34(1):1–13
Laukkanen T, Lauronen J (2005) Consumer value creation in mobile banking services. Int J Mobile Commun 3(4):325–338
Yiu CS, Grant K, Edgar D (2007) Factors affecting the adoption of internet banking in Hong Kong: implications for the banking sector. Int J Inf Manage 27(5):336–351
Marr NE, Prendergast GP (1993) Consumer adoption of self-service technologies in retail banking: is expert opinion supported by consumer research. Int J Bank Mark 11(1):3–10
Dabholkar P (1996) Consumer evaluations of new technology-based self-service options: an investigation of alternative models of service quality. Int J Res Mark 13(1):29–51
Al-Somali SA, Gholami R, Clegg B (2009) An investigation into the acceptance of online banking in Saudi Arabia. Technovation 29(2):130–141
Casu B, Girardone C, Molyneux P (2016) Introduction to banking, 2nd edn. Pearson Education Limited, Harlow
Harasim J (2016) Europe: the shift from cash to non-cash transactions. In: Transforming payment systems in Europe. Springer, pp 28–69
Seese D, Weinhardt C, Schlottmann F (2008) Handbook on information technology in finance. Springer Science & Business Media
Halevy A, Norvig P, Pereira F (2009) The unreasonable effectiveness of data. IEEE Intell Syst 24(2):8–12
Koufaris M (2002) Applying the technology acceptance model and flow theory to online consumer behavior. Inf Syst Res 13(2):205–223
Lin JCC, Lu H (2000) Towards an understanding of the behavioural intention to use a web site. Int J Inf Manage 20(3):197–208
Bina M, Giaglis GM (2007) Perceived value and usage patterns of mobile data services: a cross-cultural study. Electron Mark 17:241–252
DeLone WH, McLean ER (2003) The DeLone and McLean model of information systems success: a ten-year update. J Manag Inf Syst 19(4):9–30
Venkatesh V, Morris MG, Davis GB, Davis FD (2003) User acceptance of information technology: toward a unified view. MIS Q 27(3):425–478
Reserve Bank of India (2021) Settlement data of payment systems. Retrieved from https://rbi.org.in/Scripts/Statistics.aspx
Telecom Regulatory Authority of India (2021) Performance on key quality of service parameters—comparative performance report. Retrieved from https://www.trai.gov.in/release-publication/reports/performance-reports
Telecom Regulatory Authority of India (2016) Yearly performance indicators of Indian telecom sector. Retrieved from https://www.trai.gov.in/sites/default/files/Yearly_PI_Reports_2016.pdf
Telecom Regulatory Authority of India (2017) The Indian telecom services performance indicators. Retrieved from https://www.trai.gov.in/sites/default/files/Performance_Indicator_Reports_28Sep2017.pdf
Telecom Regulatory Authority of India (2018) Yearly performance indicators of Indian telecom sector. Retrieved from https://www.trai.gov.in/sites/default/files/PIR_25092019.pdf
The World Bank (2017) The Global Findex database 2017. Retrieved from https://globalfindex.worldbank.org
The World Bank (2021) COVID-19 related shocks survey in rural India 2020. Retrieved from https://microdata.worldbank.org/index.php/catalog/3769
Suhel SF, Shukla VK, Vyas S, Mishra VP (2020) Conversation to automation in banking through chatbot using artificial machine intelligence language. In: 2020 8th international conference on reliability, infocom technologies and optimization (trends and future directions) (ICRITO), pp 611–618. https://doi.org/10.1109/ICRITO48877.2020.9197825
Thekkethil MS, Shukla VK, Beena F, Chopra A (2021) Robotic process automation in banking and finance sector for loan processing and fraud detection. In: 2021 9th international conference on reliability, infocom technologies and optimization (trends and future directions) (ICRITO), pp 1–6. https://doi.org/10.1109/ICRITO51393.2021.9596076
Beena F, Mearaj I, Shukla VK, Anwar S (2021) Mitigating financial fraud using data science—“a case study on credit card frauds”. In: 2021 international conference on innovative practices in technology and management (ICIPTM), pp 38–43. https://doi.org/10.1109/ICIPTM52218.2021.9388345
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Shankar, N., Moid, S., Beena, F., Shukla, V.K. (2023). Pandemic-Induced Behavioral Change in Mobile Banking Adoption: An Opportunity for Indian Banking Industry for Embracing Artificial Intelligence. In: Shukla, A., Murthy, B.K., Hasteer, N., Van Belle, JP. (eds) Computational Intelligence. Lecture Notes in Electrical Engineering, vol 968. Springer, Singapore. https://doi.org/10.1007/978-981-19-7346-8_15
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