August 27, 2021

Designing technologies for financial inclusion

In today's digital world, physical cash is rapidly becoming a relic of traditional financial systems that have disadvantaged the unbanked. By combining mobile digital financial tools (such as mobile remittances and loan disbursal) with other money management tools (such as financial education), we believe unbanked people can access financial services and break out of the poverty cycle. At Gojo, we wish to include financially excluded people and enable them to achieve financial goals and self-sufficiency.

I joined Gojo as a Software Engineer in August 2020 to enable this mission and solve our mobile engineering challenges, of which there are many. I’m going to talk about two of these today.

Challenge #1:

How do you create a Digital Field Application system that works even if the cell tower is down?

Most of the time, we take internet access for granted. That’s not the case for our customers in many countries, where the internet can be patchy and a precious resource. In order to ensure that our DFA didn’t stop working when the internet did, we had to architect our technology to be offline-first.

First, we loaded our app onto an Android tablet with 32GB of storage. We were able to store all the relevant data like names, photos, and loans locally on each device. The user, typically someone like a loan agent, doesn’t even need to know whether the tablet is online or offline, because the app behaves the same either way.

In the event of the internet dropping off, as soon as the tablet is reconnected to the internet, the data automatically syncs with our servers. That enables us to maintain data quality despite patchy internet.

Field officer taking photos of new M-Lady client using Gojo's DFA / Koh Terai

Challenge #2:

How do you enable field agents to update their Customer Forms on the fly to capture desired and customisable client data?

A critical piece of our Digital Field Application is to collect client information on tablets. This information is then used to make a decision on whether a client is eligible for a loan or not. These forms can vary widely depending on the data capture requirements of the partner. Keeping the fact in mind that the KYC forms could change on a regular basis, it did not make sense for us to go for a conventional route, i.e., to hardcode forms on the tablet itself.

We solved this problem by implementing the SDUI (Server Driven User Interface) architecture for our form screens. It works in conjunction with the offline architecture I mentioned above to render the latest version of the form to the client when the internet connectivity is present, or the latest cached version in the offline scenario. 

It provided us the following benefits: 

  1. Partners no longer need to depend on mobile developers to update the app to show specific changes in forms or to change the order of the UI. An agent can now use a web portal to make any changes he/she wants in the forms and it would be reflected in the app instantly.
  2. It’s easier for loan agents to introduce new form fields, like images and map views from their office computers and have it reflect on the mobile app.
  3. It enables the engineering team to create more reusable form components and scale across partners, because we do not have to hardcode the forms for each of our partners.

As we continue to scale our Digital Field Application across Gojo partner companies, these solutions will evolve, but we're confident that our approach of immersive design and innovative development is the best way to yield technology that is as resilient and adaptive as the people in the places where it is meant to be used.


Jeet Dholakia works as part of Gojo's technology team as a software engineer, focusing particularly on mobile engineering. He is passionate about solving complex engineering problems and good mobile design, and is currently working on Gojo's Digital Field Application and Customer Mobile Application.

August 27, 2021

Designing technologies for financial inclusion

In today's digital world, physical cash is rapidly becoming a relic of traditional financial systems that have disadvantaged the unbanked. By combining mobile digital financial tools (such as mobile remittances and loan disbursal) with other money management tools (such as financial education), we believe unbanked people can access financial services and break out of the poverty cycle. At Gojo, we wish to include financially excluded people and enable them to achieve financial goals and self-sufficiency.

I joined Gojo as a Software Engineer in August 2020 to enable this mission and solve our mobile engineering challenges, of which there are many. I’m going to talk about two of these today.

Challenge #1:

How do you create a Digital Field Application system that works even if the cell tower is down?

Most of the time, we take internet access for granted. That’s not the case for our customers in many countries, where the internet can be patchy and a precious resource. In order to ensure that our DFA didn’t stop working when the internet did, we had to architect our technology to be offline-first.

First, we loaded our app onto an Android tablet with 32GB of storage. We were able to store all the relevant data like names, photos, and loans locally on each device. The user, typically someone like a loan agent, doesn’t even need to know whether the tablet is online or offline, because the app behaves the same either way.

In the event of the internet dropping off, as soon as the tablet is reconnected to the internet, the data automatically syncs with our servers. That enables us to maintain data quality despite patchy internet.

Field officer taking photos of new M-Lady client using Gojo's DFA / Koh Terai

Challenge #2:

How do you enable field agents to update their Customer Forms on the fly to capture desired and customisable client data?

A critical piece of our Digital Field Application is to collect client information on tablets. This information is then used to make a decision on whether a client is eligible for a loan or not. These forms can vary widely depending on the data capture requirements of the partner. Keeping the fact in mind that the KYC forms could change on a regular basis, it did not make sense for us to go for a conventional route, i.e., to hardcode forms on the tablet itself.

We solved this problem by implementing the SDUI (Server Driven User Interface) architecture for our form screens. It works in conjunction with the offline architecture I mentioned above to render the latest version of the form to the client when the internet connectivity is present, or the latest cached version in the offline scenario. 

It provided us the following benefits: 

  1. Partners no longer need to depend on mobile developers to update the app to show specific changes in forms or to change the order of the UI. An agent can now use a web portal to make any changes he/she wants in the forms and it would be reflected in the app instantly.
  2. It’s easier for loan agents to introduce new form fields, like images and map views from their office computers and have it reflect on the mobile app.
  3. It enables the engineering team to create more reusable form components and scale across partners, because we do not have to hardcode the forms for each of our partners.

As we continue to scale our Digital Field Application across Gojo partner companies, these solutions will evolve, but we're confident that our approach of immersive design and innovative development is the best way to yield technology that is as resilient and adaptive as the people in the places where it is meant to be used.


Jeet Dholakia works as part of Gojo's technology team as a software engineer, focusing particularly on mobile engineering. He is passionate about solving complex engineering problems and good mobile design, and is currently working on Gojo's Digital Field Application and Customer Mobile Application.

June 4, 2021

Data as (s)oil in microfinance

Farmer in the field in Anad, India. Photo by Nandhu Kumar on Unsplash

Data is the new oil?

In the past decade, the phrase “data is the new oil” has become hugely popular, with hundreds of articles and talks using this metaphor. And there is good reason for this: many see data as the “fuel” which is giving energy to the 21st century economy. 

Data on its own has no, or little value - similar to crude oil, it needs refining to become a useful and valuable resource. Only after we process our data, put it into the right context, and use it for decision making, do we get the real benefits (in the same way that oil is much more useful when turned into petrol, asphalt or plastic). To do so, we need infrastructure for collecting, storing and processing the data - which is another similarity with the oil industry.

But is this metaphor really fitting?

First, oil is a finite resource, consumed over time, and rarely reusable. This is very different from the nature of data- data  is (almost) unlimited, reusable and multiplies whenever we cross it with other data (we create information, rather than using up data). This gives us unlimited opportunities without having to worry about running out of “fuel”. 

Second, with oil you need to start big - the infrastructure is very complex and it requires a huge investment. Again, data is very different - you can start from simple data analytic functions, gradually developing your capabilities and outreach. It is also possible to test solutions and pivot if the chosen path does not fit your business. 

Third, you can store crude oil and it will still keep its value, whereas data very often loses its value over time. For example, records of some events age very quickly, and can only bring benefits if used immediately.

Finally, in case of leaks, oil can be cleaned up (although the damage to the environment is done and not always fully reversible), while in case of data it is impossible - leaked/stolen data can damage businesses and people’s lives for many, many years.

(S)oil

While listening to The Data Strategy Show1 podcast, I encountered for the first time the idea of “data as a new soil”. In the episode they mention the soil metaphor in passing, in contrast to oil. I found the soil metaphor to be much more accurate and decided to extend this thinking further.

First of all - you need to work patiently with data/soil to bring value. To grow crops, you need to know the quality of your soil well (explore your data), understand what crops you can expect to grow on this type of land (understand the business context), prepare the soil for agriculture (prepare data), sow seeds (run analytics), water crops and look after them (enrich your data, observe the results, improve analytics), protect from pests (ensure data security), harvest crops (make use of ready information), and… iterate or improve on the process.2

Moreover, the soil metaphor is useful to show that without previous experience it might be better to turn your enterprise into a data-driven one gradually. As with soil or land - if you are new to agriculture, you can start with a small plot, learn, experiment, pivot, and progress with time. Unlike with oil, you don’t need to build the whole operation from day one, but can start small and keep gradually improving.

You also need to be patient - careful preparation, good understanding of data and business context are key to obtaining the best outcome and should not be hurried. Data projects you start now might bring value after a few years - crops you planted today will not grow in a few days.

Of course, the examples above do not exhaust the similarities between data and soil, but they demonstrate the usefulness of the soil metaphor.

Woman working on a rice field in Chiang Mai, Thailand. Photo by Eduardo Prim on Unsplash

In the context of microfinance

In the microfinance context this metaphor is even more appropriate- and it is not only because the low income households we serve very often make a living from agriculture or animal husbandry. 

The microfinance sector has not usually been associated with being “data-driven”. Access to data has historically been limited, the need for data analysis has not been recognised, and the lack of proper infrastructure for data was very common. With growing usage of smartphones and tablets, however, the situation has slowly started to change, this change has rapidly accelerated under COVID-19 - more and more microfinance institutions are starting to implement better data collection methods, build (or outsource) data analytics, and use data more often in decision making or product development. But (almost) everyone proceeds in the same way as a farmer starting to cultivate a plot of land - start with a small project, learn, experiment, and then pivot or scale. And just as with growing crops: for some outcomes we will need to wait a little while.

Finally, one important point: data belongs to the people, to our clients. They give us access to their personal information and in exchange we improve our operation, pricing, and product fit. Together, we are cultivating the soil and sharing the fruits of our labor. Sometimes literally.3


Tomasz Ociepka works on data analytics at Gojo. He is currently working on setting up Gojo's data lake for the secure storage and easy analysis of data from Gojo's partner companies.

June 4, 2021

Data as (s)oil in microfinance

Farmer in the field in Anad, India. Photo by Nandhu Kumar on Unsplash

Data is the new oil?

In the past decade, the phrase “data is the new oil” has become hugely popular, with hundreds of articles and talks using this metaphor. And there is good reason for this: many see data as the “fuel” which is giving energy to the 21st century economy. 

Data on its own has no, or little value - similar to crude oil, it needs refining to become a useful and valuable resource. Only after we process our data, put it into the right context, and use it for decision making, do we get the real benefits (in the same way that oil is much more useful when turned into petrol, asphalt or plastic). To do so, we need infrastructure for collecting, storing and processing the data - which is another similarity with the oil industry.

But is this metaphor really fitting?

First, oil is a finite resource, consumed over time, and rarely reusable. This is very different from the nature of data- data  is (almost) unlimited, reusable and multiplies whenever we cross it with other data (we create information, rather than using up data). This gives us unlimited opportunities without having to worry about running out of “fuel”. 

Second, with oil you need to start big - the infrastructure is very complex and it requires a huge investment. Again, data is very different - you can start from simple data analytic functions, gradually developing your capabilities and outreach. It is also possible to test solutions and pivot if the chosen path does not fit your business. 

Third, you can store crude oil and it will still keep its value, whereas data very often loses its value over time. For example, records of some events age very quickly, and can only bring benefits if used immediately.

Finally, in case of leaks, oil can be cleaned up (although the damage to the environment is done and not always fully reversible), while in case of data it is impossible - leaked/stolen data can damage businesses and people’s lives for many, many years.

(S)oil

While listening to The Data Strategy Show1 podcast, I encountered for the first time the idea of “data as a new soil”. In the episode they mention the soil metaphor in passing, in contrast to oil. I found the soil metaphor to be much more accurate and decided to extend this thinking further.

First of all - you need to work patiently with data/soil to bring value. To grow crops, you need to know the quality of your soil well (explore your data), understand what crops you can expect to grow on this type of land (understand the business context), prepare the soil for agriculture (prepare data), sow seeds (run analytics), water crops and look after them (enrich your data, observe the results, improve analytics), protect from pests (ensure data security), harvest crops (make use of ready information), and… iterate or improve on the process.2

Moreover, the soil metaphor is useful to show that without previous experience it might be better to turn your enterprise into a data-driven one gradually. As with soil or land - if you are new to agriculture, you can start with a small plot, learn, experiment, pivot, and progress with time. Unlike with oil, you don’t need to build the whole operation from day one, but can start small and keep gradually improving.

You also need to be patient - careful preparation, good understanding of data and business context are key to obtaining the best outcome and should not be hurried. Data projects you start now might bring value after a few years - crops you planted today will not grow in a few days.

Of course, the examples above do not exhaust the similarities between data and soil, but they demonstrate the usefulness of the soil metaphor.

Woman working on a rice field in Chiang Mai, Thailand. Photo by Eduardo Prim on Unsplash

In the context of microfinance

In the microfinance context this metaphor is even more appropriate- and it is not only because the low income households we serve very often make a living from agriculture or animal husbandry. 

The microfinance sector has not usually been associated with being “data-driven”. Access to data has historically been limited, the need for data analysis has not been recognised, and the lack of proper infrastructure for data was very common. With growing usage of smartphones and tablets, however, the situation has slowly started to change, this change has rapidly accelerated under COVID-19 - more and more microfinance institutions are starting to implement better data collection methods, build (or outsource) data analytics, and use data more often in decision making or product development. But (almost) everyone proceeds in the same way as a farmer starting to cultivate a plot of land - start with a small project, learn, experiment, and then pivot or scale. And just as with growing crops: for some outcomes we will need to wait a little while.

Finally, one important point: data belongs to the people, to our clients. They give us access to their personal information and in exchange we improve our operation, pricing, and product fit. Together, we are cultivating the soil and sharing the fruits of our labor. Sometimes literally.3


Tomasz Ociepka works on data analytics at Gojo. He is currently working on setting up Gojo's data lake for the secure storage and easy analysis of data from Gojo's partner companies.

March 30, 2021

Familiar Objects Used in Unfamiliar Ways – Smartphones in Rural Cambodia

Last March, I conducted two weeks of ethnographic field research in rural Cambodia with the local staff at our partner company Maxima.

Aside from learning about the villagers and their behaviours around money, I also tried to understand their relationship with technology.

During my research, I observed one phenomenon that surprised me.

Many of the homes I visited had mysterious numbers written on their ceilings. They were written with permanent marker, or etched into the wood. I was baffled by what they were.

Mysterious numbers written and scratched into the ceiling of a home in Banteay Meas, Cambodia / Koh Terai

Can you guess what they are?

It turns out that they are phone numbers of contacts that are important to them — doctors, police, their family members, and relatives.

The baffling part is that these people all owned feature phones, and some of them even owned smartphones.

So naturally I asked them, “why don’t you put these numbers into your phone?

Their responses made me smile.

I don’t know how to register numbers into my phone, I only know how to receive calls”.

My phone is in English and besides, I can’t read

If I lose my phone, I would lose my data.”

“If the numbers stay in the phone, they sometimes get deleted. They never move if they are on the ceiling.

My phone is in English and besides, I can’t read

I felt enlightened after hearing their responses. It became clear to me how their relationships to their mobile devices are quite different from the relationship I have with my smartphone.

For me, this leads to other interesting questions we could ask like…
- What are their relationships with their mobile devices like?
- How would that influence their relationship to mobile apps?
- Does this behaviour tell us anything about the strengths of social ties in these communities?
- Are there clues we can derive from the way people use spatial memory to organize information?

“I got this phone 2 years ago. I don’t know how to use the phone. I only receive calls. I only remember if the last two numbers are 27 it’s my first daughter, if its 20, it’s my second daughter.”

How many people do you know that store phone numbers on their ceilings at home?


Koh designs products and services for Gojo. He spends time listening to clients and potential customers to deliver well-intentioned financial and digital products for low-income households.

March 30, 2021

Familiar Objects Used in Unfamiliar Ways  –  Smartphones in Rural Cambodia

Last March, I conducted two weeks of ethnographic field research in rural Cambodia with the local staff at our partner company Maxima.

Aside from learning about the villagers and their behaviours around money, I also tried to understand their relationship with technology.

During my research, I observed one phenomenon that surprised me.

Many of the homes I visited had mysterious numbers written on their ceilings. They were written with permanent marker, or etched into the wood. I was baffled by what they were.

Mysterious numbers written and scratched into the ceiling of a home in Banteay Meas, Cambodia / Koh Terai

Can you guess what they are?

It turns out that they are phone numbers of contacts that are important to them — doctors, police, their family members, and relatives.

The baffling part is that these people all owned feature phones, and some of them even owned smartphones.

So naturally I asked them, “why don’t you put these numbers into your phone?

Their responses made me smile.

I don’t know how to register numbers into my phone, I only know how to receive calls”.

My phone is in English and besides, I can’t read

If I lose my phone, I would lose my data.”

“If the numbers stay in the phone, they sometimes get deleted. They never move if they are on the ceiling.

My phone is in English and besides, I can’t read

I felt enlightened after hearing their responses. It became clear to me how their relationships to their mobile devices are quite different from the relationship I have with my smartphone.

For me, this leads to other interesting questions we could ask like…
- What are their relationships with their mobile devices like?
- How would that influence their relationship to mobile apps?
- Does this behaviour tell us anything about the strengths of social ties in these communities?
- Are there clues we can derive from the way people use spatial memory to organize information?

“I got this phone 2 years ago. I don’t know how to use the phone. I only receive calls. I only remember if the last two numbers are 27 it’s my first daughter, if its 20, it’s my second daughter.”

How many people do you know that store phone numbers on their ceilings at home?


Koh designs products and services for Gojo. He spends time listening to clients and potential customers to deliver well-intentioned financial and digital products for low-income households.

January 14, 2021

Building a credit scorecard in Myanmar

The landscape of Hpa-an, Kayin State, Myanmar. / Taejun Shin

Myanmar’s financial services industry is nascent compared to the rest of the world, since the country only started to open up after the transition in 2011 from military rule to a civilian government. With the transition came liberalization of the financial services industry, with the Central Bank of Myanmar becoming an autonomous entity, and the enactment of the Microfinance business law in 2012. Since then, the industry has been playing catch up with the rest of the world, specifically in the area of mass market consumer lending.

Banks in Myanmar have traditionally served the corporate sector with credit, and have only recently started to slowly expand their reach into the SME sector, with a couple of non-traditional banks dipping their toes into consumer lending. The biggest obstacle banks face is the majority of the population’s lack of credit history. This creates a catch-22 for the risk-averse banking sector, who will not lend to consumers without credit history, but cannot build credit histories for consumers without taking the risk of lending in the first place. Microfinance institutions have been left to pick up where banks fell short in providing lending services to consumers, taking high risk, and building credit histories.

Microfinance in Myanmar started with the mission of getting people out of poverty and extending financial inclusion. The gap in the provision of mainstream financial services has led to the popularity of microfinance among the un/underserved credit-hungry populace. As a result, while maintaining its social mission, the microfinance sector has also grown to be a provider of mass market retail lending, ranging from consumer lending to micro/small business lending. Such rapid expansion in the lending scene has brought the need for credit scoring to the forefront, especially among the no/thin file segment of the population. This is where the sector’s years of trial and error in building the credit history of no/thin file clients can begin to bear fruit, as the sector starts to address the need for stronger credit scoring and risk management by building credit scorecards.

A lady selling flowers to visitors of Bagan, the most popular tourist destination in Myanmar. / Taejun Shin

Credit scorecards: An introduction

So, what is a credit scorecard?

It is the heart of credit scoring. It is a checklist of data points that are collected and weighted to spit out a score that we call a credit score, and financial institutions use this score to measure the risk level of a consumer. Consumers who have high credit scores are usually considered low-risk, while consumers on the other end of the spectrum, who have low credit scores, are considered high-risk.

The credit score and its associated risk level can decide whether a consumer gets approved for a loan, the pricing on the loan (risk-weighted pricing), and in some cases, even the loan amount and term. With credit scoring playing an important role in the decision-making process, the need to understand how the credit scorecard is made becomes critical.

A credit scorecard is created by looking at data on past loans that the institution has made so that it can extrapolate its experience of past loans to future consumers. To do this, they first need to classify consumers as either “good” or “bad”, and an analysis is carried out to explore and extract a set of characteristics that makes a borrower “good” or “bad”. In this scenario, the definition of a “bad” consumer, in hindsight, is any consumer to whom the institution would choose not to offer a loan again. There are two main types of scorecards for making such an analysis: an expert scorecard and a statistical scorecard.

Let us begin with the expert scorecard. It is the most basic credit scorecard and the most commonly used scorecard. As its name suggests, it is a scorecard made with inputs from an expert. People with years of experience in lending and credit appraisal make a list of characteristics to check and score for any consumers applying for the loan. This is a very manual process that relies on the personal experience of seasoned loan officers and credit managers in the case of microfinance, and of the underwriting team, in the case of banks.

The statistical scorecard does not draw on any personal experience but instead on statistics. The scorecard is built by using regression analysis to find correlations between data points collected from consumers and the performance of their past loans. This often means that an institution has collected hundreds, if not thousands, of data points from consumers and their past loans to find the correlations.

There is a midway approach, aptly called a hybrid scorecard. This is the combination of the two scorecards where the statistical scorecard is evaluated by experts to create a final version of the scorecard.

Creating a credit scorecard

Financial institutions that are looking to build a scorecard need to evaluate whether they have sufficient data points covering:

  • Transaction history (volume and amounts of deposits, withdrawals, cash ins, cash outs, and payments)
  • Saving history (balances in individual account or across all deposit accounts)
  • Demographics (age, gender, location, etc.)
  • Loan performance (number of times a consumer is late for previous loan instalments, number of days late for previous instalments, history of delinquency)
  • Income data (individual / consolidated debt to income ratio)
  • Relationship with the institution (how long the consumer has been with the institution, other products of the institution used by the consumer)
  • Alternative sources of data such as the credit bureau, call/text data, social media usage, etc.

The more data points, the better the statistical scorecard is. If the institution does not have access to or has not accumulated sufficient relevant data points, they can create an initial scorecard by using expert team members who have the experience to make judgement calls in lending, while gradually transitioning towards a statistical scorecard. 

A restaurant owner providing buffet lunch for local people in Yangon City. / Taejun Shin

Transitioning to a statistical scorecard: The example of MIFIDA

The following is an example of one of Gojo’s partner companies, Microfinance Delta International (MIFIDA), and its journey to create a scorecard.

MIFIDA is a microfinance institution in Myanmar with around 150,000 customers and a portfolio of around $40 million. It was incorporated in 2013 but hit its stride in 2017, when it grew from a handful of branches to 60+ branches today. With such growth, the need to reevaluate its risk management policies and credit assessment became apparent. This in turn highlighted the need for a scorecard for its customers.

MIFIDA already had a scorecard for its MSME customers, but it was a basic expert scorecard that covered the usual characteristics such as: debt coverage ratio, the ratio of repayment amount to income, number of outstanding loans, age, years in the business receiving the loan, etc. But it did not have a scorecard for its mass market lending products, such as its group loans.

MIFIDA therefore set out to reevaluate its current MSME scorecard and to create a new scorecard from scratch for its group loans. Below, I will cover the re-evaluation and update of the MSME scorecard, and the challenges we encountered in the process. I hope to cover our journey toward creating a new scorecard for the group loans in a later post.

Relevant data is paramount for making a statistical scorecard, and this is exactly what MIFIDA did not have. It had only implemented its core banking system in recent months and even then, it only had transactional data going back as far as the data that had been migrated into the system. Despite being around seven years old, MIFIDA did not have digitised historical data on clients. There was also no guarantee that the digitised data was reliable.

This ruled out immediate creation of the statistical scorecard for MIFIDA, but as they had experts who have been making loan decisions for years now, they decided to create an expert scorecard based on the experience of their staff. They listed down everything that made a consumer “good” and “bad”. From that listing, the team trimmed it down to 14 specific characteristics that would be most telling of the customer’s behavior and provided the weightings on each characteristic to be scored. A new application form was then drafted so that the data needed for scoring could be captured.

Market-wide challenges in credit scoring

MIFIDA is using this new expert scorecard and application form as stepping stones toward a future statistical scorecard of its own. Apart from the lack of data points mentioned above, the current challenges that MIFIDA is facing in creating the statistical scorecard are:

  1. A lack of data analysts and data scientists in Myanmar. Even if you have the data, there are few people in Myanmar with the skills to do the necessary analytics to build and produce the scorecard. It would require a person well versed in R or Python to handle large datasets, do exploratory data analysis, find correlations using regression or one of a few other methods, and then make a production-level scorecard that could be used in the field.
  2. The lack of a credit bureau. Anyone who wants to double check a customer’s self-reported credit history will simply have to trust the consumer as there is no centralized database to check against. In recent years, MCIX (Myanmar Credit Info Exchange) has started to provide such a service to the microfinance sector, but it is still a nascent endeavour, as it currently only shows some of the loans that the customer has taken from other microfinance institutions, and sharing of delinquency data is still a work in progress. Until MCIX or the national credit bureau are fully-fledged,  with the majority of financial institutions onboard, MIFIDA will have to check credit histories either by building these histories itself, or through traditional means such as asking family, relatives or local authorities.
  3. Tying into the institutional lack of data is that most customers are no/thin file customers who are only just beginning to be financially included. This means that they are at the start of their journey to build a credit history with a formal financial institution. Building such histories takes time. On the other hand, it also presents an opportunity to financial service providers to get the data they want to collect from customers right, so that it can be processed and used for scoring in the future.

Financial institutions in Myanmar, MIFIDA included, are currently working on overcoming those challenges of building a statistical scorecard and transitioning from expert scorecards, as there is a whole world of new opportunities if the transition is successful. 

An artisan who makes umbrellas in Pathein City. The town is known for umbrellas. / Taejun Shin

The rewards of better credit scoring

Myanmar has seen one example of an institution that is inching closer to a full statistical scorecard, and the opportunity this has provided to that institution.

The institution is Yoma Bank. Their digital lending product, called SMART Credit, is made for the mass market with a hybrid scorecard in the backend that is recalibrated every year with the help of Experian, one of the biggest providers of credit scoring and analytics in the world. This has helped Yoma Bank to expand its lending portfolio to everyday consumers and to a new market segment that it would not normally lend to due to the associated risk.

MIFIDA hopes to replicate that success by building its own customers’ credit history, while using an expert scorecard to mitigate the current risks until sufficient data is collected for a statistical scorecard. MIFIDA will also look to move onto digital lending and digitizing much of its operations so that its loan officers can focus more on building relationships with customers instead of focusing on application forms and transactions. Such digitization would allow for the collection of well-structured data points that could be used to move onto a statistical model, enabling MIFIDA to expand more easily to new customer segments with reduced risk in future by providing a comparable baseline for the new segment’s credit scoring.


Kaung Set Lin is Gojo's Country Officer for Myanmar, and has over 6 years of experience in Myanmar's financial sector, primarily focusing on developing and implementing digital financial products. His work includes managing the rollout of Gojo's digital products, including our Digital Field Application (DFA).

January 14, 2021

Building a credit scorecard in Myanmar

The landscape of Hpa-an, Kayin State, Myanmar. / Taejun Shin

Myanmar’s financial services industry is nascent compared to the rest of the world, since the country only started to open up after the transition in 2011 from military rule to a civilian government. With the transition came liberalization of the financial services industry, with the Central Bank of Myanmar becoming an autonomous entity, and the enactment of the Microfinance business law in 2012. Since then, the industry has been playing catch up with the rest of the world, specifically in the area of mass market consumer lending.

Banks in Myanmar have traditionally served the corporate sector with credit, and have only recently started to slowly expand their reach into the SME sector, with a couple of non-traditional banks dipping their toes into consumer lending. The biggest obstacle banks face is the majority of the population’s lack of credit history. This creates a catch-22 for the risk-averse banking sector, who will not lend to consumers without credit history, but cannot build credit histories for consumers without taking the risk of lending in the first place. Microfinance institutions have been left to pick up where banks fell short in providing lending services to consumers, taking high risk, and building credit histories.

Microfinance in Myanmar started with the mission of getting people out of poverty and extending financial inclusion. The gap in the provision of mainstream financial services has led to the popularity of microfinance among the un/underserved credit-hungry populace. As a result, while maintaining its social mission, the microfinance sector has also grown to be a provider of mass market retail lending, ranging from consumer lending to micro/small business lending. Such rapid expansion in the lending scene has brought the need for credit scoring to the forefront, especially among the no/thin file segment of the population. This is where the sector’s years of trial and error in building the credit history of no/thin file clients can begin to bear fruit, as the sector starts to address the need for stronger credit scoring and risk management by building credit scorecards.

A lady selling flowers to visitors of Bagan, the most popular tourist destination in Myanmar. / Taejun Shin

Credit scorecards: An introduction

So, what is a credit scorecard?

It is the heart of credit scoring. It is a checklist of data points that are collected and weighted to spit out a score that we call a credit score, and financial institutions use this score to measure the risk level of a consumer. Consumers who have high credit scores are usually considered low-risk, while consumers on the other end of the spectrum, who have low credit scores, are considered high-risk.

The credit score and its associated risk level can decide whether a consumer gets approved for a loan, the pricing on the loan (risk-weighted pricing), and in some cases, even the loan amount and term. With credit scoring playing an important role in the decision-making process, the need to understand how the credit scorecard is made becomes critical.

A credit scorecard is created by looking at data on past loans that the institution has made so that it can extrapolate its experience of past loans to future consumers. To do this, they first need to classify consumers as either “good” or “bad”, and an analysis is carried out to explore and extract a set of characteristics that makes a borrower “good” or “bad”. In this scenario, the definition of a “bad” consumer, in hindsight, is any consumer to whom the institution would choose not to offer a loan again. There are two main types of scorecards for making such an analysis: an expert scorecard and a statistical scorecard.

Let us begin with the expert scorecard. It is the most basic credit scorecard and the most commonly used scorecard. As its name suggests, it is a scorecard made with inputs from an expert. People with years of experience in lending and credit appraisal make a list of characteristics to check and score for any consumers applying for the loan. This is a very manual process that relies on the personal experience of seasoned loan officers and credit managers in the case of microfinance, and of the underwriting team, in the case of banks.

The statistical scorecard does not draw on any personal experience but instead on statistics. The scorecard is built by using regression analysis to find correlations between data points collected from consumers and the performance of their past loans. This often means that an institution has collected hundreds, if not thousands, of data points from consumers and their past loans to find the correlations.

There is a midway approach, aptly called a hybrid scorecard. This is the combination of the two scorecards where the statistical scorecard is evaluated by experts to create a final version of the scorecard.

Creating a credit scorecard

Financial institutions that are looking to build a scorecard need to evaluate whether they have sufficient data points covering:

  • Transaction history (volume and amounts of deposits, withdrawals, cash ins, cash outs, and payments)
  • Saving history (balances in individual account or across all deposit accounts)
  • Demographics (age, gender, location, etc.)
  • Loan performance (number of times a consumer is late for previous loan instalments, number of days late for previous instalments, history of delinquency)
  • Income data (individual / consolidated debt to income ratio)
  • Relationship with the institution (how long the consumer has been with the institution, other products of the institution used by the consumer)
  • Alternative sources of data such as the credit bureau, call/text data, social media usage, etc.

The more data points, the better the statistical scorecard is. If the institution does not have access to or has not accumulated sufficient relevant data points, they can create an initial scorecard by using expert team members who have the experience to make judgement calls in lending, while gradually transitioning towards a statistical scorecard. 

A restaurant owner providing buffet lunch for local people in Yangon City. / Taejun Shin

Transitioning to a statistical scorecard: The example of MIFIDA

The following is an example of one of Gojo’s partner companies, Microfinance Delta International (MIFIDA), and its journey to create a scorecard.

MIFIDA is a microfinance institution in Myanmar with around 150,000 customers and a portfolio of around $40 million. It was incorporated in 2013 but hit its stride in 2017, when it grew from a handful of branches to 60+ branches today. With such growth, the need to reevaluate its risk management policies and credit assessment became apparent. This in turn highlighted the need for a scorecard for its customers.

MIFIDA already had a scorecard for its MSME customers, but it was a basic expert scorecard that covered the usual characteristics such as: debt coverage ratio, the ratio of repayment amount to income, number of outstanding loans, age, years in the business receiving the loan, etc. But it did not have a scorecard for its mass market lending products, such as its group loans.

MIFIDA therefore set out to reevaluate its current MSME scorecard and to create a new scorecard from scratch for its group loans. Below, I will cover the re-evaluation and update of the MSME scorecard, and the challenges we encountered in the process. I hope to cover our journey toward creating a new scorecard for the group loans in a later post.

Relevant data is paramount for making a statistical scorecard, and this is exactly what MIFIDA did not have. It had only implemented its core banking system in recent months and even then, it only had transactional data going back as far as the data that had been migrated into the system. Despite being around seven years old, MIFIDA did not have digitised historical data on clients. There was also no guarantee that the digitised data was reliable.

This ruled out immediate creation of the statistical scorecard for MIFIDA, but as they had experts who have been making loan decisions for years now, they decided to create an expert scorecard based on the experience of their staff. They listed down everything that made a consumer “good” and “bad”. From that listing, the team trimmed it down to 14 specific characteristics that would be most telling of the customer’s behavior and provided the weightings on each characteristic to be scored. A new application form was then drafted so that the data needed for scoring could be captured.

Market-wide challenges in credit scoring

MIFIDA is using this new expert scorecard and application form as stepping stones toward a future statistical scorecard of its own. Apart from the lack of data points mentioned above, the current challenges that MIFIDA is facing in creating the statistical scorecard are:

  1. A lack of data analysts and data scientists in Myanmar. Even if you have the data, there are few people in Myanmar with the skills to do the necessary analytics to build and produce the scorecard. It would require a person well versed in R or Python to handle large datasets, do exploratory data analysis, find correlations using regression or one of a few other methods, and then make a production-level scorecard that could be used in the field.
  2. The lack of a credit bureau. Anyone who wants to double check a customer’s self-reported credit history will simply have to trust the consumer as there is no centralized database to check against. In recent years, MCIX (Myanmar Credit Info Exchange) has started to provide such a service to the microfinance sector, but it is still a nascent endeavour, as it currently only shows some of the loans that the customer has taken from other microfinance institutions, and sharing of delinquency data is still a work in progress. Until MCIX or the national credit bureau are fully-fledged,  with the majority of financial institutions onboard, MIFIDA will have to check credit histories either by building these histories itself, or through traditional means such as asking family, relatives or local authorities.
  3. Tying into the institutional lack of data is that most customers are no/thin file customers who are only just beginning to be financially included. This means that they are at the start of their journey to build a credit history with a formal financial institution. Building such histories takes time. On the other hand, it also presents an opportunity to financial service providers to get the data they want to collect from customers right, so that it can be processed and used for scoring in the future.

Financial institutions in Myanmar, MIFIDA included, are currently working on overcoming those challenges of building a statistical scorecard and transitioning from expert scorecards, as there is a whole world of new opportunities if the transition is successful. 

An artisan who makes umbrellas in Pathein City. The town is known for umbrellas. / Taejun Shin

The rewards of better credit scoring

Myanmar has seen one example of an institution that is inching closer to a full statistical scorecard, and the opportunity this has provided to that institution.

The institution is Yoma Bank. Their digital lending product, called SMART Credit, is made for the mass market with a hybrid scorecard in the backend that is recalibrated every year with the help of Experian, one of the biggest providers of credit scoring and analytics in the world. This has helped Yoma Bank to expand its lending portfolio to everyday consumers and to a new market segment that it would not normally lend to due to the associated risk.

MIFIDA hopes to replicate that success by building its own customers’ credit history, while using an expert scorecard to mitigate the current risks until sufficient data is collected for a statistical scorecard. MIFIDA will also look to move onto digital lending and digitizing much of its operations so that its loan officers can focus more on building relationships with customers instead of focusing on application forms and transactions. Such digitization would allow for the collection of well-structured data points that could be used to move onto a statistical model, enabling MIFIDA to expand more easily to new customer segments with reduced risk in future by providing a comparable baseline for the new segment’s credit scoring.


Kaung Set Lin is Gojo's Country Officer for Myanmar, and has over 6 years of experience in Myanmar's financial sector, primarily focusing on developing and implementing digital financial products. His work includes managing the rollout of Gojo's digital products, including our Digital Field Application (DFA).

December 18, 2020

Why we are developing a digital financial infrastructure for the less privileged

Ladies in Gujarat, India. They are all neighbors and formed a group to borrow money from an MFI backed by Ananya. / Taejun Shin

In February, I joined Gojo as CTO to use technology to support and accelerate our mission to extend financial inclusion to everyone. In this blog post, I would like to share one of the key pillars of our technology strategy: developing digital financial infrastructure for the less privileged.

We are all well aware that there is a well designed financial system and infrastructure which enables us to transact money safely and securely.

Financial infrastructure plays a critical role in any country’s economic and societal development. Today’s long evolved financial infrastructure (which includes central banks, banking systems, payment networks, and identity or credit scoring agencies) has perfected services for the most common use cases.

We may take its robustness and efficiency (or sometimes inefficiency) for granted in our daily financial interactions, for example:

  • You can easily get a new bank account with a preferential interest rate in return for parking your money. Granted, if you are not used to digital banking, then it’s a bit of a hassle. But if you are used to it, then opening a bank account only takes a few clicks on your smartphone.
  • Your monthly salary arrives instantly to your bank account and you are notified.
  • You can get immediate credit if you face a sudden liquidity problem. You can even shop around different banks/fintechs to get a favourable lending agreement.
  • You can use your credit/debit card when you shop and avoid having to carry cash. You can even use Apple/Google/Samsung Pay or a QR code if you wish.
  • If you happen to need cash, you can walk to a nearby ATM and use your debit card to withdraw money from your account.
  • You can send money to your friends or loved ones in a few clicks from your browser or mobile app and the recipient gets it as quickly as a text  message (though of course, for cross-border transactions it's not quite that fast).

All the above daily scenarios are made possible by a financial infrastructure made up of at least one or more entities. In short, a financial infrastructure enables money to move throughout an economy, functioning as a platform for transactions, whether these are payments, financing, or the transfer of bonds and stocks.

The strength and weakness of our present financial infrastructure is its over-reliance on the customer's ability to open an account in a regulated financial institution, such as a bank or non-banking financial institution or a regulated fintech.Unfortunately, this excludes a significant minority and represents a major hurdle for a lot of people who could otherwise benefit from accessing the financial infrastructure. Many organizations in the world focus on bringing this un/underserved population to the formal financial system but have not met with great success.

In some of the countries where we work, governments have recently taken concrete steps to improve the digital financial infrastructure and have brought a lot of people into the formal system as a result. In these countries, we leverage the infrastructure or work with them. But in the vast majority of places where we operate, we still face this problem where many are excluded from the infrastructure of the formal financial system.

At Gojo, we are on a mission. We believe that everyone in this world should have an equal opportunity to access quality financial services. Gojo’s Tech team is using technology to solve these critical problems for financial inclusion. So we have started to develop our own digital financial infrastructure. Our digital financial infrastructure consists of a few key building blocks, as given below:

1. Identity

This is the foundation for everything. In order to serve our customers, we have to establish their identity and our level of confidence in their capacity to use the financial service they are requesting.

Traditionally this has been done using a formal process of KYC (Know Your Customer) by submitting government issued verifiable identification, such as a national identity or voters card or shop ownership license. The next step would be the analysis of a customer’s past financial transactions to understand their creditworthiness. Traditionally financial institutions use credit bureaus to evaluate their customers’ liabilities and financial standing.

But in our case, customers seldom come with any verifiable, government issued ID. Moreover, they have zero traces of past financial transactions with which we might assess their financial status. Instead of rejecting these customers, we are planning to use machine learning algorithms to identify and assess our level of confidence in clients for each financial service. We have started putting together our big data infrastructure and plan to integrate multiple alternate data sources such as mobile network operators (for billing, data, and call details), leading ecommerce platforms for past transactions, and behavioural analysis such as psychometric evaluations.

In addition, there are many organizations working to onboard and provide a verifiable digital identity. We would like to join together with organizations such as ID4D or other ID as a service (IDaaS) providers to provide a secure digital ID to our customers.

2. Digital Accounts

Once we provide or recognize a customer’s unique ID, we can begin to offer financial services. But here we plan to follow a fully digital/paperless approach. We are in the process of developing our own mobile application for customers. This is a logical step since a considerable percentage of our target  population uses mobile internet and smartphones. For example, as of January 2020 in Myanmar, there were approximately 68 million mobile connections and internet penetration stood at 41%1.

So if a customer wants to request a loan or deposit some money for  their savings, they will be able to access their digital accounts through the mobile app and see real-time updates of their activities, such as daily interest accruing from a loan, their financial goals, and more.

In order to offer digital accounts to customers, we need sophisticated backend infrastructure such as a cloud-based core banking system and its associated tools. We are currently investing heavily in building our common digital platform in the public cloud to achieve scalability and growth.

3. Financial rails

The next building block in our digital financial infrastructure is financial rails. There should be a simple and transparent mechanism to move money to wherever the customer wants. The most common scenarios are payments, P2P (person to person) money transfers, and remittances. We are partnering with local  real-time payment schemes where available, such as UPI in India, or leading payment schemes and alternate real-time payment services such as Mojaloop2. Mojaloop is an exciting project and we are already in the experimentation stage with it.

4. Personalised products and services

We believe in data-backed product creation and know that there will not be one single product that works for all customers. We will use data to identify customer pain points and introduce products to address them. All of our new product development goes through a human-centered design process where we ensure that the product we are putting in the market is genuinely useful for our customers. We carry out constant experimentation and prototyping to identify what makes our customers happy. This constant experimentation requires a lean and agile culture with flexible technology capabilities. At Gojo, we are putting each of these building blocks in place one by one.

We will be deploying our digital financial infrastructure stack in countries such as Myanmar and Cambodia. We hope that as a result, our customers will be able to get an account without any of the usual hassle, start saving daily, withdraw money whenever they want, apply for and obtain credit within minutes, and transact confidently through their digital wallet - and all of this will be possible without needing to register for a bank account.


Syam Nair is Gojo's Chief Technology Officer. He joined in February 2020, having previously worked for Visa and Mastercard. He leads the development of Gojo's technology strategy.

December 18, 2020

Why we are developing a digital financial infrastructure for the less privileged

Ladies in Gujarat, India. They are all neighbors and formed a group to borrow money from an MFI backed by Ananya. / Taejun Shin

In February, I joined Gojo as CTO to use technology to support and accelerate our mission to extend financial inclusion to everyone. In this blog post, I would like to share one of the key pillars of our technology strategy: developing digital financial infrastructure for the less privileged.

We are all well aware that there is a well designed financial system and infrastructure which enables us to transact money safely and securely.

Financial infrastructure plays a critical role in any country’s economic and societal development. Today’s long evolved financial infrastructure (which includes central banks, banking systems, payment networks, and identity or credit scoring agencies) has perfected services for the most common use cases.

We may take its robustness and efficiency (or sometimes inefficiency) for granted in our daily financial interactions, for example:

  • You can easily get a new bank account with a preferential interest rate in return for parking your money. Granted, if you are not used to digital banking, then it’s a bit of a hassle. But if you are used to it, then opening a bank account only takes a few clicks on your smartphone.
  • Your monthly salary arrives instantly to your bank account and you are notified.
  • You can get immediate credit if you face a sudden liquidity problem. You can even shop around different banks/fintechs to get a favourable lending agreement.
  • You can use your credit/debit card when you shop and avoid having to carry cash. You can even use Apple/Google/Samsung Pay or a QR code if you wish.
  • If you happen to need cash, you can walk to a nearby ATM and use your debit card to withdraw money from your account.
  • You can send money to your friends or loved ones in a few clicks from your browser or mobile app and the recipient gets it as quickly as a text  message (though of course, for cross-border transactions it's not quite that fast).

All the above daily scenarios are made possible by a financial infrastructure made up of at least one or more entities. In short, a financial infrastructure enables money to move throughout an economy, functioning as a platform for transactions, whether these are payments, financing, or the transfer of bonds and stocks.

The strength and weakness of our present financial infrastructure is its over-reliance on the customer's ability to open an account in a regulated financial institution, such as a bank or non-banking financial institution or a regulated fintech.Unfortunately, this excludes a significant minority and represents a major hurdle for a lot of people who could otherwise benefit from accessing the financial infrastructure. Many organizations in the world focus on bringing this un/underserved population to the formal financial system but have not met with great success.

In some of the countries where we work, governments have recently taken concrete steps to improve the digital financial infrastructure and have brought a lot of people into the formal system as a result. In these countries, we leverage the infrastructure or work with them. But in the vast majority of places where we operate, we still face this problem where many are excluded from the infrastructure of the formal financial system.

At Gojo, we are on a mission. We believe that everyone in this world should have an equal opportunity to access quality financial services. Gojo’s Tech team is using technology to solve these critical problems for financial inclusion. So we have started to develop our own digital financial infrastructure. Our digital financial infrastructure consists of a few key building blocks, as given below:

1. Identity

This is the foundation for everything. In order to serve our customers, we have to establish their identity and our level of confidence in their capacity to use the financial service they are requesting.

Traditionally this has been done using a formal process of KYC (Know Your Customer) by submitting government issued verifiable identification, such as a national identity or voters card or shop ownership license. The next step would be the analysis of a customer’s past financial transactions to understand their creditworthiness. Traditionally financial institutions use credit bureaus to evaluate their customers’ liabilities and financial standing.

But in our case, customers seldom come with any verifiable, government issued ID. Moreover, they have zero traces of past financial transactions with which we might assess their financial status. Instead of rejecting these customers, we are planning to use machine learning algorithms to identify and assess our level of confidence in clients for each financial service. We have started putting together our big data infrastructure and plan to integrate multiple alternate data sources such as mobile network operators (for billing, data, and call details), leading ecommerce platforms for past transactions, and behavioural analysis such as psychometric evaluations.

In addition, there are many organizations working to onboard and provide a verifiable digital identity. We would like to join together with organizations such as ID4D or other ID as a service (IDaaS) providers to provide a secure digital ID to our customers.

2. Digital Accounts

Once we provide or recognize a customer’s unique ID, we can begin to offer financial services. But here we plan to follow a fully digital/paperless approach. We are in the process of developing our own mobile application for customers. This is a logical step since a considerable percentage of our target  population uses mobile internet and smartphones. For example, as of January 2020 in Myanmar, there were approximately 68 million mobile connections and internet penetration stood at 41%1.

So if a customer wants to request a loan or deposit some money for  their savings, they will be able to access their digital accounts through the mobile app and see real-time updates of their activities, such as daily interest accruing from a loan, their financial goals, and more.

In order to offer digital accounts to customers, we need sophisticated backend infrastructure such as a cloud-based core banking system and its associated tools. We are currently investing heavily in building our common digital platform in the public cloud to achieve scalability and growth.

3. Financial rails

The next building block in our digital financial infrastructure is financial rails. There should be a simple and transparent mechanism to move money to wherever the customer wants. The most common scenarios are payments, P2P (person to person) money transfers, and remittances. We are partnering with local  real-time payment schemes where available, such as UPI in India, or leading payment schemes and alternate real-time payment services such as Mojaloop2. Mojaloop is an exciting project and we are already in the experimentation stage with it.

4. Personalised products and services

We believe in data-backed product creation and know that there will not be one single product that works for all customers. We will use data to identify customer pain points and introduce products to address them. All of our new product development goes through a human-centered design process where we ensure that the product we are putting in the market is genuinely useful for our customers. We carry out constant experimentation and prototyping to identify what makes our customers happy. This constant experimentation requires a lean and agile culture with flexible technology capabilities. At Gojo, we are putting each of these building blocks in place one by one.

We will be deploying our digital financial infrastructure stack in countries such as Myanmar and Cambodia. We hope that as a result, our customers will be able to get an account without any of the usual hassle, start saving daily, withdraw money whenever they want, apply for and obtain credit within minutes, and transact confidently through their digital wallet - and all of this will be possible without needing to register for a bank account.


Syam Nair is Gojo's Chief Technology Officer. He joined in February 2020, having previously worked for Visa and Mastercard. He leads the development of Gojo's technology strategy.

Newsletter

Sign up for our semi-regular round-up of our latest news and blog posts here.