Determinants of Mobile Money Adoption in Pakistan

11/13/2017
by   Muhammad Raza Khan, et al.
berkeley college
0

In this work, we analyze the problem of adoption of mobile money in Pakistan by using the call detail records of a major telecom company as our input. Our results highlight the fact that different sections of the society have different patterns of adoption of digital financial services but user mobility related features are the most important one when it comes to adopting and using mobile money services.

READ FULL TEXT VIEW PDF

Authors

page 1

page 2

page 3

page 4

12/08/2018

Mobile Money: Understanding and Predicting its Adoption and Use in a Developing Economy

Access to financial institutions is difficult in developing economies an...
08/22/2021

Digital Resilience and the Continuance Use of Mobile Payment Services

The use of mobile payment services is an essential contributor to financ...
10/29/2021

Diagnosing Web Data of ICTs to Provide Focused Assistance in Agricultural Adoptions

The past decade has witnessed a rapid increase in technology ownership a...
09/18/2017

Building an Effective Data Warehousing for Financial Sector

This article presents the implementation process of a Data Warehouse and...
06/16/2016

Machine Learning Across Cultures: Modeling the Adoption of Financial Services for the Poor

Recently, mobile operators in many developing economies have launched "M...
07/10/2018

A strategic framework for identifying the critical factors of 4G technology diffusion in I.R. Iran - A Fuzzy DEMATEL approach

As the most prominent representative of 4G, Long term evolution (LTE) te...
03/01/2019

Urban Vibes and Rural Charms: Analysis of Geographic Diversity in Mobile Service Usage at National Scale

We investigate spatial patterns in mobile service consumption that emerg...
This week in AI

Get the week's most popular data science and artificial intelligence research sent straight to your inbox every Saturday.

1 Introduction

Wider penetration of the mobile phone services in the developing countries is providing alternate ways to provide financial services through mobile phones (“Mobile money services”) in these countries. Considering the lower cost of additional infrastructure government, mobile operators and development agencies all around the world have been trying to promote mobile money services. However, only a few countries have seen widespread adoption of mobile money. Most of the moible money services around the world have been unable to achieve a critical user mass. scharwatt_state_2014 . An important question thus revolves around understanding what drives customers to adopt and use mobile money. This paper is focused on the following question: How do patterns of adoption of mobile money vary across different demographics (gender, urban/rural areas; rich/ poor areas)?

We try to answer this question by applying machine learning algorithms over social networks extracted from mobile money transactions.

2 Related Work

The work on the adoption of digital financial services has been dominated by macroeconomic work related to regulatory issues around interoperability and the logistics of mobile money agents dermish_branchless_2011 ; donovan_mobile_2012 and a very few research papers have explored the adoption of mobile money from a quantitative perspective. However, mining of insights from mobile communication meta-data has been a popular area of research and some examples of work in this area include predicting the socioeconomic status blumenstock_predicting_2015 , gender frias2010gender of mobile phone subscribers, customer churn behavior khan2015behavioral and analysis of gender disparities using social networks extracted from mobile communication logs reed2016gender .

We have used deterministic finite automata (DFA) based feature engineering over CDR data which is quite similar to the approach used in khan_dfa_2016 and blumenstock_predicting_2015 . Lastly, the differences in the usage of technology across men and women, poor & rich, and the urban & rural population has been a popular theme of research in the ICTD domain jackson_race_2008 , blumenstock2010mobile ,etc. In comparison to these studies, our work employs a more comprehensive feature engineering approach over a bigger dataset to evaluate the role of the different type of features in the adoption of mobile money for people with different demographic backgrounds.

3 Context and Methods

Our input data consists of anonymized call detail records (CDR) and mobile money transaction information (MMTR) from a major mobile operator of Pakistan. Each row of the CDR typically consists of the tuple containing {callerID, recipientID, date, time, duration, callerLocation, receiverLocation}. We categorize each user either as a “Voice only user",“Registered mobile money user" or “P2P mobile money user” as explained below.

  • Voice only users: The users who do not use mobile money services at all.

  • Registered mobile money users: The users who have signed up for mobile money.

  • P2P mobile money users: The users who have either sent or received money to other users.

Instead of relying on a few handpicked features, we wanted to use a comprehensive set of features for which we have used the DFA based feature generation algorithm as shown in Figure 1. As an example, say we are interested in constructing a feature for each individual

that corresponds to, “the variance in the average duration of outgoing calls made by

on different days of the week”. This feature can be constructed through the following set of recursive rules: (a) filter outgoing calls, (b) filter transactions initiated by , (c) group by day of week, (d) focus on call duration, (e) aggregate by average (duration per day of week), (f) aggregate using variance

(over average daily durations). DFA also helps in clear categorization of the features. For the purpose of this work, we use the following categories: usage features, mobility features and network (structural properties of the networks) features. We have performed two different type of supervised learning experiments:

Voice Only Users vs. Registered Mobile Money Users & Voice Only Users vs. Peer-to-peer Mobile Money Users

For each of these experiments, we drew a stratified random balanced sample of adopters/peer-to-peer users and non-adopters for each of the six categories (Males, Females; Urban, Rural; and Rich, Poor ). Gradient boosting algorithm

friedman2001greedy

is used to classify users and determine prominent features.

Figure 1: Deterministic Finite Automatonkhan_dfa_2016
Figure 2: Male vs Female
Figure 3: Urban vs Rural Districts
Figure 4: Rich vs Poor Districts
Features Importance for Mobile Money Adoption
Figure 5: Male vs Female
Figure 6: Urban vs Rural Districts
Figure 7: Rich vs Poor Districts
Features Importance for Mobile Money - Peer to Peer Usage

4 Results

The distribution of conditional feature importance values for all the features generated through the DFA is shown in the Figures 4, 4, 4. First thing to note in the Figures 4, 4, 4 is that different category of features have different aggregate feature importance for different cases. Both network and usage related features are a more likely indicator of adoption for females while mobility related features have more predictive power for male users. Usage related features are the most important category for the female users while the mobility related features are the most important one for male users.

Similarly, mobility is the most important category of features for both urban and rural districts. However, compared to rural districts, the network, and usage related features of the users in the urban districts are not that important. The importance of the network related features for the rural users indicates that the awareness of the mobile money services and endorsement effects from other mobile money users play an important role in the adoption of mobile money services. The trends for the rich vs. poor districts are quite similar to the urban and rural districts.

The features importance for each of the experiments for voice only users vs peer to peer mobile money users is shown in the figures 7, 7 and 7. Usage related features are the top category of peer-to-peer transactions for the female users, while the mobility related features are the top determinant for the male users as shown in the Figure 7. Mobility related features are still the top features for the urban and rural districts (7) while in comparison to the Figure 7 the importance of network related features is lower while the importance of the usage features is higher for the rural district’s users. In comparison to the Figure 4, Figure 7 shows that the mobility related features are the most important one for the users in the rich districts while the usage related features are the most important determinant for peer to peer transactions in the poor districts.

Figure 8: Mobile money adoption accuracy
Figure 9: Peer-to-peer usage accuracy

Cross-validated accuracy for predicting the adoption or the peer to peer usage of mobile money obtained through gradient boosting is shown in Figure 9.

Index Feature Category
Panel A: Gender: Female
1 Variance in the number of active days with incoming SMS Usage
2 Rank Percentile of the Size of the incoming SMS contact network Network
Panel A: Gender: Male
1 Number of unique contact locations on weekdays Mobility
2 Variance in the contact locations per day Mobility
Panel C: Rural
1 Number of unique contact locations on weekend Mobility
2 Percentile of the network size on weekend sms Network
Panel D: Urban
1 Number of unique contact locations on weekdays Mobility
2 Variance in the duration of the weekend calls to each contact locations Usage
Panel E: Poor
1 Number of unique contact locations on week days Mobility
2 Size of the network per contact locations per day Network
Panel F: Rich
1 Number of unique contact locations on the weekdays Mobility
2 Number of unique contact locations on the weekend Mobility
  • Notes: Rank percentile for a feature indicate the percentile of the feature value for the subscriber as compared to all the contacts.

Table 1: Top features for mobile money adoption

5 Conclusion

Most interesting finding of this work are the differences in performance of the different type of features for different sections of the society. For example, the usage category was the top one for the female users but not for the male users. This indicates that the males who may be working remotely or have to commute to work are more likely to use the mobile money, whereas the women who are more active or have higher technology literacy are more likely to user mobile money. Similarly, mobility of the users is the prime determinant when it comes to the adoption of mobile money in both urban & rural and rich & poor districts. However, the people in rural districts with a larger network are more likely to use mobile money. These results can help the marketing and developing agencies to promote the adoption of mobile money in the developing countries.

References

  • [1] J. Blumenstock, G. Cadamuro, and R. On. Predicting poverty and wealth from mobile phone metadata. Science, 350(6264):1073–1076, 2015.
  • [2] J. Blumenstock and N. Eagle. Mobile divides: gender, socioeconomic status, and mobile phone use in rwanda. In Proceedings of the 4th ACM/IEEE International Conference on Information and Communication Technologies and Development, page 6. ACM, 2010.
  • [3] A. Dermish, C. Kneiding, P. Leishman, and I. Mas. Branchless and mobile banking solutions for the poor: A survey of the literature. Innovations: Technology, Governance, Globalization, 6(4):81–98, Oct. 2011.
  • [4] K. Donovan. Mobile money for financial inclusion. Information and Communications for Development, pages 61–73. The World Bank, 2012.
  • [5] V. Frias-Martinez, E. Frias-Martinez, and N. Oliver. A gender-centric analysis of calling behavior in a developing economy using call detail records. 2010.
  • [6] J. H. Friedman. Greedy function approximation: a gradient boosting machine. Annals of statistics, pages 1189–1232, 2001.
  • [7] L. A. Jackson, Y. Zhao, A. Kolenic III, H. E. Fitzgerald, R. Harold, and A. Von Eye. Race, gender, and information technology use: the new digital divide. CyberPsychology & Behavior, 11(4):437–442, 2008.
  • [8] M. R. Khan and J. E. Blumenstock. Predictors without borders: Behavioral modeling of product adoption in three developing countries. In Proceedings of the 22Nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, New York, NY, USA, 2016. ACM.
  • [9] M. R. Khan, J. Manoj, A. Singh, and J. Blumenstock. Behavioral modeling for churn prediction: Early indicators and accurate predictors of custom defection and loyalty. In 2015 IEEE International Congress on BigData, pages 677–680. IEEE, 2015.
  • [10] P. J. Reed, M. R. Khan, and J. Blumenstock. Observing gender dynamics and disparities with mobile phone metadata. In Proceedings of the Eighth International Conference on Information and Communication Technologies and Development, New York, NY, USA, 2016. ACM.
  • [11] C. Scharwatt, A. Katakam, J. Frydrych, A. Murphy, and N. Naghavi. State of the Industry: Mobile Financial Services for the Unbanked. Technical report, GSMA, 2014.