Operational risk management of using electronic and mobile money

The banking sector in India has contributed to economic growth, parity and equity while equally keeping focus on profit and social objectives. The successive prudential and regulatory reforms introduced in the banking sector have made it more robust and stronger to withstand the bubbles and external shocks. Still, the Indian banking sector in general and public-sector banks (PSBs) in particular have been suffering from the bank frauds. This study endeavors to cover the increasing incidences of banking frauds in PSBs and probes the weaknesses and chinks in the operational risk architecture at the PSBs in India. This study selects Punjab National Bank as a true representative of PSBs and treats it as a critical case study to apply the learning and findings to the PSBs in India. This qualitative analysis of the study revealed that the chinks in the operational risk control mechanism and lax corporate governance are the main reasons behind the increasing incidences of frauds at PSBs. The findings of the study showed that a strong corporate governance and compliance framework, robust risk management architecture, investment in people, technology and systems will go a long way in achieving tighter control and supervision, streamlining processes and, most of all, adhering to a culture of checks and balances.

Understanding marketing channel performance is a crucial and complex task for the mobile financial technology segment of the mobile industry in emerging markets. However, poor techniques and capabilities for channel optimization of the mobile money users across available channels by the service providers often undermine the performance of these channels. The research aims to develop a target selection and campaign optimization framework for mobile money customers along two channels of transactions. It is complemented by mapping the appropriate campaign techniques across digital and non-digital channels of mobile money transactions. The key analytical method is the combination of fuzzy c-means clustering and RFM algorithm for the target selection development through the usage logs of customers (n = 300) of a mobile service provider. The results indicated that fuzzy c-means clustering and RFM algorithm are efficient for target selection. Also, the mapping of clusters with the appropriate channel of transactions revealed that mobile money users’ transactions could be optimized along the digital channel. The analytic model’s output enables appropriate cross-selling and up-selling campaigns that optimize the service provider revenue from existing and new mobile money users within the customer base. The channel evaluation revealed mobile application channels to be a promising and future channel for mobile money transactions as smartphone penetration continues to grow in emerging mobile markets. That is a positive sign of the digital channel’s future potential for mobile money transactions in developing markets.

Acknowledgment
The authors wish to thank the University of Pecs under the Higher Education Institution Excellence Program of the Ministry of Innovation and Technology in Hungary within the framework of the 4th Thematic Program – “Enhancing the Role of Domestic Companies in the Re-industrialization of Hungary.”

The purpose of this study is to determine an index for loan loss provision as a new measurement and examine its effect on bank risk. The study also compared the results with a commonly used measurement, which is the ratio of loan loss provision (LLP). The population of this study is all conventional banks, including foreign banks with branch offices in Indonesia. The period of observation is from 2015 to 2018. The sample selection based on the purposive sampling method resulted in 86 banks. This study used panel data analysis. The data were collected from the annual reports of each bank and the website of the Financial Services Authority. The research findings show that the index of loan loss provision can decrease credit risk, liquidity risk, and operational risk. Meanwhile, the ratio of the loan loss provision only affects operational risk and does not affect credit risk and liquidity risk. The findings of this study suggest that the index for loan loss provision is more suitable to be used as an alternative measurement in the research design related to loan loss provision because the implementation of IFRS 9 requires more detailed disclosure of how banks estimate the amount of loan loss provision.