Financial Inclusion


25 NOVEMBER 2018

In 2017, Indonesia scored 49.8% on the Global Financial Inclusion Index, which means that more than half of the Indonesian adult population does not access formal financial services. Factors related to geographic coverage and infrastructure are often cited as the main challenges to financial inclusion, but knowledge gaps remain. At the same time, the digital revolution continues to generate data that provide more opportunities to gain insights on the progress of financial inclusion in Indonesia.  

PLJ invited applicants specifically who can work on the following four research tasks:

  1. Measuring financial literacy and financial inclusion through social media (Main data source: Twitter and Susenas Data)
  2. Measuring financial access through different channels (Main data source : ATM location, Alfamart, Indomaret, and Post Office)
  3. Modelling gender-based differences in financial inclusion (Main data source : household survey data, Susenas data)
  4. Measuring the effect of digital opportunity to financial inclusion (Main data sources: Twitter and Susenas Data)

The information package of Research Dive Financial Inclusion can be read here:


Research Dive Advisors and Participants


Adityo Dwijananto

Chaikal Nuryakin

Edi Winarko

Rahma Fitriani


Universitas Indonesia

Universitas Gadjah Mada

Universitas Brawijaya



Task 1 - Measuring Financial Literacy and Financial Inclusion Through Social Media

Ira Puspitasari

Universitas Airlangga

Rahmi Yuwan

Institut Teknologi Bandung

Vera Dewi

Universitas Padjadjaran


Pulse Lab Jakarta

M. Rizal Khaefi

Pulse Lab Jakarta


Task 2 - Supply-Side Measurement of Financial Inclusion

Dedy Dwi Prastyo

Institut Teknologi Sepuluh Nopember


Universitas Wahid Hasyim

Yustinus Soelistio

Universitas Multimedia Nusantara

Rajius Idzalika

Pulse Lab Jakarta

M. Rheza Muztahid

Pulse Lab Jakarta


Task 3 - Modelling Gender-Based Differences in Financial Inclusion

Jamilatuzzahro Abdul Fatah

Data Science Indonesia


Universitas Pertamina

Riki Herliansyah

Institut Teknologi Kalimantan

Pamungkas Jutta Prahara

Pulse Lab Jakarta

Annissa Zahara

Pulse Lab Jakarta


Task 4 - Assessing the Impact of Digital Opportunity on Financial Inclusion

Ana Uluwiyah

Badan Pusat Statistik

Anita Sindar Sinaga

STMIK Pelita Nusantara Medan

Nika Pranata

Lembaga Ilmu Pengetahuan Indonesia

Anthony Mockler

Pulse Lab Jakarta

Kunch Ringrod

Pulse Lab Jakarta

Group 1: Measuring Financial Literacy and Financial Inclusion through Social Media


The aim of this study is to measure nancial literacy and inclusion of 7 cities in Indonesia by using social media data. This study used data cleaning, data processing, and keyword scoring measured by nancial tweet and user in each city based on attributes criteria. Financial attributes are categorized into 7 group, which are banking, insurance, pension fund, nancial institutions, pawn shop, capital market, and others. Results show that Jakarta achieved highest nancial tweets ratio followed by Bandung, Banten, Makassar, and Medan. On the other hand, in terms of nancial users, Makassar shows the highest results, followed by Jakarta and Medan. Majority of cities showed increasing nancial tweets and users trend. In con trast to the majority, DIY Yogyakarta showed stationary trend and ranked as the lowest on both measures. We also showed that each city had dierent characteristics using word cloud visualization. Our results give insights are potential to be used by authorities to formulate relevant policies in order to increase nancial literacy and inclusion in Indonesia.


The result shows that social media analysis can reveal hidden insights of nancial literacy and inclusions. Compared than traditional survey and eld observation approaches, social media analysis oers higher spatial and temporal resolutions makes more accurate and real-time information. Social media insights about the nancial inclusion and nancial literacy are promising to be used by nancial authorities to formulate relevant policies in order to increase the local and national nancial inclusion. Despite the promises, validity of the keywords and model needs to be more evaluated to better capture nancial literacy and inclusion insights.

Group 2: Supply-Side Measurement of Financial Inclusion


This paper proposes a novel model on measuring nancial inclusion from the supply side, by harnessing the combination of location data of nancial channel and population density. We extend the channel of formal nancial service provider such as bank to formal non-nancial institutions that provide partial nancial services, such as post oce and two most Indonesian popular chain con venient stores, Alfamart and Indomaret. The proposed model is implemented and evaluated in Pontianak city, and can be general ized to other cities in Indonesia. It measures the score of nancial inclusion index in population within predened area by taking the ratio between the number of nancial channels over the popula tion aged 15 years old and older. Moreover, the nancial inclusion index is weighted by the geographic distance between the center of area and the channels. The nancial inclusion index based on the proposed approach seems to relatively follow the pattern of local economics, where the majority of channels are located on the areas known to be traditionally wealthier or growing business dis tricts. This evidence is asupporting proof that the proposed model is decent as proxy of nancial inclusion index generated by ocial survey.


The team concludes the discussion with some suggestions as follows:

  1. Two essential factors for the existing spatial distribution of nancial services channels in Pontianak city are population density and local economies.
  2. Related to the rst point, location of the formal nancial institution like banks need to be arranged not only by the proposal of each bank, but also need intervention from regu lator in order to make the spatial distribution of banks and other nancial institutions becomes more balance, relatively to the population, over the region such that they can give nancial service for broader area and more population. The can be expected to increase the number banked people such that the nancial inclusion index also increase.
  3. The proposed formula to calculate the supply-side nancial inclusion index, that also consider the geographical prox imity of nancial channel, provides the make-sense results compared with the real condition of each corresponding village. 
  4. The formal non-nancial institution can provide nancial services that can not be reached by formal nancial institu tion. This fact can be used to extend the calculation of the nancial inclusion index particularly from the supply-side.

Group 3: Modelling Gender-Based Differences in Financial Inclusion


At the end of 2019, National Strategy of Financial Inclusion - SNKI (2016) commanded that nancial inclusion index still needs to be more highly targeted by 75 percent. It means that 75 percent of adult population will have access to nancial services oered by for mal institutions. Dozens of national stakeholders have convinced a positive eect of nancial inclusion to solve poverty-alleviation and inequality gap-issue especially in rural area. The objective of this research is to examine the extent of inclusiveness on nance based on gender dierences. To dene this model, we use Generalized Linear Latent Variable Models (GLLVM). GLLVM is an extended version of ordinary regression model where multivariate-correlated responses are modeled through a single-joint model and are a pow erful class of models for understanding the relationships among multiple, correlated responses. The result is rural-based female adults are more likely to be formally served, and specically more likely to be banked through the facilities of non-bank credit and bank saving than males, with a dierence of 5% to 10%.


The study conclude that rural-based female adults are more likely to be formally served, and specically more likely to be banked through the facilities of non-bank credit and bank saving than males, with a dierence of 5% to 10%. Surprisingly, no major dierence between males and females in terms of the probability of being nancially inclusive in rural area in general except for having access to non-credits. We also found that for females, non-bank credit is positive-signicantly determined by age, marital status, and income. The most varied nding is on higher level of education where it becomes the signicant factor to boost the usage of saving account both males and females in rural area. Finally, many correlations are strongly positive in females while in males a few indicators have negative relationship in nancial inclusion. Both male and female ownership insurance have the highest correlation with others of indicators in nancial inclusion; on the other hand, saving at the bank has the lowest correlation.

The recommendation from this study includes: for policymak ers review and explore ways to strengthen nancial literacy pro grammes and developing programmes for dierentiated target groups based on age, marital status, income and educational level. And for nancial service providers, strengthen linkages with formal non-bank providers of credit, to capitalise on the specic strengths and advantages of both bank and non-bank institutions and to im prove the provision of credit services without excessively increasing the transaction costs for the end-users.

Group 4: Assessing the Impact of Digital Opportunity on Financial Inclusion 


Indonesian government has targeted the nancial inclusion index in 2019 reaches 75 percent. Meanwhile, in 2017, according to Indone sia Financial Services Authority (OJK), the index is at 69 percent. There is quite wide gap to fulll 2019 target with only about 1 year left. On the other side, the adoption of technology in Indonesia is growing rapidly. Based on data released by Statistics Indonesia, the percentage of people having mobile phone is 58.3 percent. There fore, this research aims to measure the eect of digital opportunity to nancial inclusion. This research analyzes data from two dif ferent sources which are 2017 National Socio-Economic Survey (SUSENAS) and 2014 Twitter data. The main methodology used in the research is logistics regression. In addition, descriptive statistics with visualization is utilized to provide further analysis. This study found that digital opportunity has positive impact to nancial inclu sion. The ownership of computer and phone is expected to improve nancial inclusion. Moreover, high intensity of social media activity correlates with nancial inclusion.


In general, digital opportunity has a signicant and positive impact to nancial inclusion. The use of computer, phone, and access to internet is expected to improve nancial inclusion. However, in terms of Twitter data high intensity of social media activity (tweet) does not correlate directly with nancial inclusion that is likely because Twitter data is not as comprehensive as SUSENAS data which covers almost all of cities and provinces in Indonesia. Based on the result, in order to accelerate nancial inclusion in Indonesia we propose two recommendations which are:

  • Reduce digital divide between eastern and western part of Indonesia as well as urban and rural area by improving in ternet and telecommunication infrastructure and access in those disadvantaged area as well as introducing the adoption of technology for those who are technology-illiterate
  • It is better for the government to distribute social fund like Program Keluarga Harapan through payment system ntech that provide e-wallet using cellphone particularly to the largest cellular provider in Indonesia, Telkomsel, by using their digital wallet which is T-Cash. By using their services, the use of nancial services can be accelerated considering that they have the largest subscribers and cellular coverages in almost all of Indonesia geographic location.

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