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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).

Published by Kaung Set Lin in Blog posts JP
Tags: Credit Scoring, Innovation, Technology

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).

Published by Kaung Set Lin in Blog posts
Tags: Credit Scoring, Innovation, Technology

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.

Published by Syam Nair in Blog posts JP
Tags: Technology

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.

Published by Syam Nair in Blog posts
Tags: Technology

October 23, 2020

Why we invested in a Supply Chain Financing company

This is the first in a series of blog posts by Sohil Shah, from our investment team, about Gojo's investment thesis and why we chose each of our partner companies.

The founding members of Loan Frame Technologies (from left to right): Rishi Arya, Shailesh Jacob, and Akshun Gulati

There are more than 63 million Micro, Small and Medium Enterprises (MSME) in India1. That is a huge number. They contribute to 30% of India’s GDP. That is significant. They employ over 100 million people. That is impactful. 

Of the 63 million MSMEs, 99.4% are micro enterprises. These are the ones that you regularly encounter in your daily life. From the ubiquitous kirana (grocery) stores, to hardware stores, stationery shops, recharge and remittance shops, they are all around us.

But did you ever wonder why these businesses often struggle and remain small, with a lot of them ultimately shutting down? Certain credible researchers in the country point to “lack of access to finance” as the single largest problem plaguing the MSME industry. It is estimated that the current unmet credit gap is roughly USD 300 billion, and is expected to increase to USD 900 billion by 2022. On further investigation, it wasn’t difficult to see that in reality, a lot of MSMEs remain excluded from the formal financial system. This gave us our next investment objective!

When we started looking at the MSME financing space, one sub-sector that stood out was Supply Chain Finance (SCF). Truth be told, SCF has been around in various forms for a long time. The most traditional forms of SCF have been trade financing and factoring, which are largely dominated by large or international banks on one side and mid-to-large corporates on the other. Moreover, banks have traditionally preferred offering simple working capital loans over supply chain financing due to lack of borrower data and difficulty in assessing the collateral provided. Until very recently, this created a large vacuum in the space.

The following diagram illustrates how Supply Chain Finance (SCF) works:

The advent of fintechs has revolutionized the SCF segment. From running analytics on transaction or trade data to providing app- or platform-based financing, technology is set to make the SCF process more efficient, flexible and transparent. With technology, lenders can now provide financing to the MSMEs further down the value chain. SCF can enable MSME suppliers, distributors and retailers to increase their working capital by availing low cost and flexible credit, which in turn opens up new business or expansion opportunities. At the same time, it also helps corporates to improve their working capital management and enables lenders to assess, measure, and manage the risks of extending financing to MSMEs more effectively.

On further investigation, there were some aspects of SCF that we really liked:

  1. Hugely underpenetrated market: Around 90% of anchor/corporates do not have a supply chain finance program for their distributors / retailers, representing a ~USD 100 billion lending opportunity
  2. Limited delinquency: The closed-loop financing built on a strong relationship between a corporate and its distributors/retailers helps in keeping delinquencies low 
  3. Limited end-use prevents diversion of funds: The biggest reason for defaults in MSMEs is lack of financial discipline. MSMEs borrow citing business reasons, but may divert funds for personal / lifestyle needs. In SCF, the payment is made directly to the Anchor, restricting the end use to financing MSME working capital, further minimizing defaults
  4. Repeat business and data: Lending is done on the basis of invoices raised by the anchor/corporate. The inherent nature of this product creates borrower stickiness and allows large-scale collection of data on distributors and retailers which eventually helps in determining market trends and managing sectoral disruptions and risks

Loan Frame Technologies, our most recent investment, is a fintech platform working in this space. We are excited by Loan Frame’s vision of leveraging cutting-edge technologies and data to build the most sophisticated next generation micro-business lending platform in India. The team is passionate about serving under-banked micro-businesses which aligns well with Gojo’s vision of extending financial inclusion across emerging markets. 

Loan Frame’s proprietary and flexible technology platform enables short term and flexible credit to distributors, dealers and retailers associated with mid- to large-sized corporates. The company primarily focuses on distribution finance, where it provides 7-90 day pay-as-you-use working capital products. Distribution finance gives small businesses credit primarily for purchasing inventory whilst helping corporates to better manage their receivables.

Unlike a lot of fintechs who jump right into action, the team spent a couple of years understanding the real challenges that micro businesses grapple with and how technology can sustainably solve these issues. The initial phase was exclusively spent on building a top-shelf technology platform – one which integrates directly with the anchor/corporate and communicates with lender and distributor/retailer via web or app on real-time basis. 

One key thing the team realized fairly quickly is that SCF cannot succeed with a “one-size-fits-all” approach, and that’s where we believe the Company has built its moat. Loan Frame has a very nuanced approach to underwriting developed through their deep understanding of the segments where they operate. This differentiated approach has allowed the Company to assess businesses in the context of the industry where they operate, thus opening up possibilities for those MSMEs with previously limited access to credit.

Finally, Loan Frame’s composite lending model combines the benefits of both on-balance sheet lending, which provides the ability to test innovative products in new markets or segments, and off-balance sheet lending, which is highly scalable and capital efficient. This gives it an edge in comparison to traditional lenders.

By the time we decided to invest in Loan Frame, it was already on track to achieve approximately USD 100 million in disbursals and we hadn’t even scratched the surface! We believe this is a timely and well-aligned partnership to build India’s largest and most preferred tech-enabled MSME lending platform.

Sohil Shah is a member of Gojo's investment team. He leads the process of building Gojo's investment pipeline and selecting our long-term partners to further our mission of extending financial inclusion to everyone.

Published by Sohil Shah in Blog posts JP
Tags: Investment, Technology

October 23, 2020

Why we invested in a Supply Chain Financing company

This is the first in a series of blog posts by Sohil Shah, from our investment team, about Gojo's investment thesis and why we chose each of our partner companies.

The founding members of Loan Frame Technologies (from left to right): Rishi Arya, Shailesh Jacob, and Akshun Gulati

There are more than 63 million Micro, Small and Medium Enterprises (MSME) in India1. That is a huge number. They contribute to 30% of India’s GDP. That is significant. They employ over 100 million people. That is impactful. 

Of the 63 million MSMEs, 99.4% are micro enterprises. These are the ones that you regularly encounter in your daily life. From the ubiquitous kirana (grocery) stores, to hardware stores, stationery shops, recharge and remittance shops, they are all around us.

But did you ever wonder why these businesses often struggle and remain small, with a lot of them ultimately shutting down? Certain credible researchers in the country point to “lack of access to finance” as the single largest problem plaguing the MSME industry. It is estimated that the current unmet credit gap is roughly USD 300 billion, and is expected to increase to USD 900 billion by 2022. On further investigation, it wasn’t difficult to see that in reality, a lot of MSMEs remain excluded from the formal financial system. This gave us our next investment objective!

When we started looking at the MSME financing space, one sub-sector that stood out was Supply Chain Finance (SCF). Truth be told, SCF has been around in various forms for a long time. The most traditional forms of SCF have been trade financing and factoring, which are largely dominated by large or international banks on one side and mid-to-large corporates on the other. Moreover, banks have traditionally preferred offering simple working capital loans over supply chain financing due to lack of borrower data and difficulty in assessing the collateral provided. Until very recently, this created a large vacuum in the space.

The following diagram illustrates how Supply Chain Finance (SCF) works:

The advent of fintechs has revolutionized the SCF segment. From running analytics on transaction or trade data to providing app- or platform-based financing, technology is set to make the SCF process more efficient, flexible and transparent. With technology, lenders can now provide financing to the MSMEs further down the value chain. SCF can enable MSME suppliers, distributors and retailers to increase their working capital by availing low cost and flexible credit, which in turn opens up new business or expansion opportunities. At the same time, it also helps corporates to improve their working capital management and enables lenders to assess, measure, and manage the risks of extending financing to MSMEs more effectively.

On further investigation, there were some aspects of SCF that we really liked:

  1. Hugely underpenetrated market: Around 90% of anchor/corporates do not have a supply chain finance program for their distributors / retailers, representing a ~USD 100 billion lending opportunity
  2. Limited delinquency: The closed-loop financing built on a strong relationship between a corporate and its distributors/retailers helps in keeping delinquencies low 
  3. Limited end-use prevents diversion of funds: The biggest reason for defaults in MSMEs is lack of financial discipline. MSMEs borrow citing business reasons, but may divert funds for personal / lifestyle needs. In SCF, the payment is made directly to the Anchor, restricting the end use to financing MSME working capital, further minimizing defaults
  4. Repeat business and data: Lending is done on the basis of invoices raised by the anchor/corporate. The inherent nature of this product creates borrower stickiness and allows large-scale collection of data on distributors and retailers which eventually helps in determining market trends and managing sectoral disruptions and risks

Loan Frame Technologies, our most recent investment, is a fintech platform working in this space. We are excited by Loan Frame’s vision of leveraging cutting-edge technologies and data to build the most sophisticated next generation micro-business lending platform in India. The team is passionate about serving under-banked micro-businesses which aligns well with Gojo’s vision of extending financial inclusion across emerging markets. 

Loan Frame’s proprietary and flexible technology platform enables short term and flexible credit to distributors, dealers and retailers associated with mid- to large-sized corporates. The company primarily focuses on distribution finance, where it provides 7-90 day pay-as-you-use working capital products. Distribution finance gives small businesses credit primarily for purchasing inventory whilst helping corporates to better manage their receivables.

Unlike a lot of fintechs who jump right into action, the team spent a couple of years understanding the real challenges that micro businesses grapple with and how technology can sustainably solve these issues. The initial phase was exclusively spent on building a top-shelf technology platform – one which integrates directly with the anchor/corporate and communicates with lender and distributor/retailer via web or app on real-time basis. 

One key thing the team realized fairly quickly is that SCF cannot succeed with a “one-size-fits-all” approach, and that’s where we believe the Company has built its moat. Loan Frame has a very nuanced approach to underwriting developed through their deep understanding of the segments where they operate. This differentiated approach has allowed the Company to assess businesses in the context of the industry where they operate, thus opening up possibilities for those MSMEs with previously limited access to credit.

Finally, Loan Frame’s composite lending model combines the benefits of both on-balance sheet lending, which provides the ability to test innovative products in new markets or segments, and off-balance sheet lending, which is highly scalable and capital efficient. This gives it an edge in comparison to traditional lenders.

By the time we decided to invest in Loan Frame, it was already on track to achieve approximately USD 100 million in disbursals and we hadn’t even scratched the surface! We believe this is a timely and well-aligned partnership to build India’s largest and most preferred tech-enabled MSME lending platform.

Sohil Shah is a member of Gojo's investment team. He leads the process of building Gojo's investment pipeline and selecting our long-term partners to further our mission of extending financial inclusion to everyone.

Published by Sohil Shah in Blog posts
Tags: Investment, Technology

September 29, 2020

Learning to innovate for impact

A customer of Microfinance Delta International (MIFIDA), Gojo’s partner company in Myanmar, who runs a small factory with several sewing machines

At Gojo, we aim to become the private sector version of the World Bank. More specifically, our mission is to extend high-quality, useful financial services to 100 million people in 50 developing countries around the world. At least 80% of our clients should be living below their national median income.1 Our services are meant to produce tangible impact in the form of financial inclusion, giving everyone, not just a few privileged people, the ability to take control of the direction of their lives. Such an ambition cannot be achieved by doing what has already been done before. We need to break new ground, discover novel angles in doing microfinance. To put it in more mundane words, we have to innovate. We're investing heavily in just that, and a key tool we are using is technology.

When it comes to doing new things with tech, Silicon Valley and startups around the world give us a well-tested (and amply written-about) framework. Simplifying greatly, this approach can be distilled into two concepts. 

The first key point is understanding the customer deeply. Listen and observe carefully. Constantly measure everything about how the product is functioning in the hands of users. That way you can be fairly confident that you're solving actual problems, rather than imaginary ones. 

Second, experiment often and fast. Product management expert Marty Cagan of Silicon Valley Product Group coined the "inconvenient truths about product": at least half of all ideas simply don't work out, and those that do work take multiple iterations to get right. Mistakes are not to be avoided but rather normalized as a useful part of a healthy creation process.

These two ideas form a powerful feedback loop for product innovation. They are at the core of popular “movements” like Lean Startup and Human-Centered Design, the power of which we believe in at Gojo. We could naively apply these lessons by the book and say that we're innovating. Unfortunately however, the Silicon Valley model on its own is not sufficient to get to where we're heading.

If we are to become the Private Sector World Bank, increased profits and growth are not enough. There's another type of "fit" here, beyond product-market fit, that we have to reach: we need to bring real change in people's lives. Selling a lot of financial products that, for instance, help clients solve temporary needs, without long-term improvements, cannot be considered a success. Even worse, there is a risk of doing harm by creating dependency and indebtedness spirals. Unlike video-game companies and social-media giants, creating addiction is definitely not something we seek.

How do we innovate around all these traps, then?

As a company, we are still new at making products. We will have a lot to learn as we go forward. However, we do already have some experience as a team and we put much thought into these problems. Our product team is already hard at work. Here is how we believe it should be done:

Work extra hard to get in the client's shoes
Because we come from very different cultures and lifestyles from our clients, it is especially important to throw away all of our assumptions and learn directly from the people we serve, in their context. All of Gojo's members, regardless of role, visit our partner companies regularly (when traveling is safe), learn from them and go with them to meet our clients. We are also blessed with the counsel of some of the most respected experts in the financial lives of the poor, such as our director Stuart Rutherford.

Own the pipeline
To be nimble with the validation of our ideas, and to keep a super-high pace of iterations, we cannot afford to depend too much on third parties when it comes to technology. Asking tech partners for permission to make even tiny changes, negotiating contracts and timelines all the time, would kill our velocity. That is why we have our own technology team with fintech and product experience, and we build the core tools and platforms ourselves. Of course, we do welcome partnerships in non-core areas, but we keep the product development and delivery pipeline firmly in our own hands.

Measure the outcomes
Numbers like app installation counts, processing times, or outstanding portfolios are only half of our KPIs. They are direct outputs, not outcomes on the lives of our clients. To see that missing half, we now have a team dedicated exclusively to impact measurement. They are now working closely with our Tech Team to build those measurements right into our solutions and make the product feedback loop truly effective.

Look at your real competition
Some other microfinance institutions provide high-quality, useful financial products that we surely want to match and surpass. But when impact is the goal, there are non-institutional players to keep in mind. For instance, often low-income households borrow from so-called informal lenders, who charge extremely high interest rates, or from wealthier relatives straining family relationships and self-esteem. Yet, these forms of borrowing are still popular even when cheaper, safer loans are available from microfinance institutions. Why do our products fail to eliminate the need for those unpleasant and risky informal debts? Clearly, it's not just the rates. Unless we treat those channels as first-class competition, we won't be able to answer such questions and change the dynamics in favor of the clients.

A group collection meeting run by an MFI in Myanmar (not MIFIDA).

While I believe that the points above may indeed help, perhaps the single most important insight that we will need to keep in mind is that large-scale success in this endeavor is more about people than it is about technology.

No one, to my knowledge, has put it better than Kentaro Toyama, professor at the University of Michigan and formerly a researcher of impactful innovation at Microsoft Research India. In his book Geek Heresy: Rescuing Social Change from the Cult of Technology, he introduced the "Law of Amplification", stating that technology merely amplifies any effects of pre-existing human intentions and abilities, whether positive or negative. Technology can't be a silver bullet; you can’t expect it to guarantee results on its own.

"Yet CIOs everywhere are asked to perform exactly that sort of wizardry. The more experienced ones are careful not to promise too much. Technology can improve systems that are already working—a kind of amplification—but it doesn’t fix systems that are broken. There is no knowledge management without management."

People, be they our clients or colleagues, will remain the drivers of our innovation. When we do finally reach the last person in the world who needs our services, perhaps somewhere in Africa or South America, it will be the fruit of the love and collaboration of humans.

Marco is part of Gojo's tech team and works on developing Gojo's digital products and strategy. He is currently leading the development of Gojo's Digital Field Application (DFA) in MIFIDA, our partner company in Myanmar.

Published by Marco Giancotti in Blog posts
Tags: Innovation, Technology

September 29, 2020

Learning to innovate for impact

A customer of Microfinance Delta International (MIFIDA), Gojo’s partner company in Myanmar, who runs a small factory with several sewing machines

At Gojo, we aim to become the private sector version of the World Bank. More specifically, our mission is to extend high-quality, useful financial services to 100 million people in 50 developing countries around the world. At least 80% of our clients should be living below their national median income.1 Our services are meant to produce tangible impact in the form of financial inclusion, giving everyone, not just a few privileged people, the ability to take control of the direction of their lives. Such an ambition cannot be achieved by doing what has already been done before. We need to break new ground, discover novel angles in doing microfinance. To put it in more mundane words, we have to innovate. We're investing heavily in just that, and a key tool we are using is technology.

When it comes to doing new things with tech, Silicon Valley and startups around the world give us a well-tested (and amply written-about) framework. Simplifying greatly, this approach can be distilled into two concepts. 

The first key point is understanding the customer deeply. Listen and observe carefully. Constantly measure everything about how the product is functioning in the hands of users. That way you can be fairly confident that you're solving actual problems, rather than imaginary ones. 

Second, experiment often and fast. Product management expert Marty Cagan of Silicon Valley Product Group coined the "inconvenient truths about product": at least half of all ideas simply don't work out, and those that do work take multiple iterations to get right. Mistakes are not to be avoided but rather normalized as a useful part of a healthy creation process.

These two ideas form a powerful feedback loop for product innovation. They are at the core of popular “movements” like Lean Startup and Human-Centered Design, the power of which we believe in at Gojo. We could naively apply these lessons by the book and say that we're innovating. Unfortunately however, the Silicon Valley model on its own is not sufficient to get to where we're heading.

If we are to become the Private Sector World Bank, increased profits and growth are not enough. There's another type of "fit" here, beyond product-market fit, that we have to reach: we need to bring real change in people's lives. Selling a lot of financial products that, for instance, help clients solve temporary needs, without long-term improvements, cannot be considered a success. Even worse, there is a risk of doing harm by creating dependency and indebtedness spirals. Unlike video-game companies and social-media giants, creating addiction is definitely not something we seek.

How do we innovate around all these traps, then?

As a company, we are still new at making products. We will have a lot to learn as we go forward. However, we do already have some experience as a team and we put much thought into these problems. Our product team is already hard at work. Here is how we believe it should be done:

Work extra hard to get in the client's shoes
Because we come from very different cultures and lifestyles from our clients, it is especially important to throw away all of our assumptions and learn directly from the people we serve, in their context. All of Gojo's members, regardless of role, visit our partner companies regularly (when traveling is safe), learn from them and go with them to meet our clients. We are also blessed with the counsel of some of the most respected experts in the financial lives of the poor, such as our director Stuart Rutherford.

Own the pipeline
To be nimble with the validation of our ideas, and to keep a super-high pace of iterations, we cannot afford to depend too much on third parties when it comes to technology. Asking tech partners for permission to make even tiny changes, negotiating contracts and timelines all the time, would kill our velocity. That is why we have our own technology team with fintech and product experience, and we build the core tools and platforms ourselves. Of course, we do welcome partnerships in non-core areas, but we keep the product development and delivery pipeline firmly in our own hands.

Measure the outcomes
Numbers like app installation counts, processing times, or outstanding portfolios are only half of our KPIs. They are direct outputs, not outcomes on the lives of our clients. To see that missing half, we now have a team dedicated exclusively to impact measurement. They are now working closely with our Tech Team to build those measurements right into our solutions and make the product feedback loop truly effective.

Look at your real competition
Some other microfinance institutions provide high-quality, useful financial products that we surely want to match and surpass. But when impact is the goal, there are non-institutional players to keep in mind. For instance, often low-income households borrow from so-called informal lenders, who charge extremely high interest rates, or from wealthier relatives straining family relationships and self-esteem. Yet, these forms of borrowing are still popular even when cheaper, safer loans are available from microfinance institutions. Why do our products fail to eliminate the need for those unpleasant and risky informal debts? Clearly, it's not just the rates. Unless we treat those channels as first-class competition, we won't be able to answer such questions and change the dynamics in favor of the clients.

A group collection meeting run by an MFI in Myanmar (not MIFIDA).

While I believe that the points above may indeed help, perhaps the single most important insight that we will need to keep in mind is that large-scale success in this endeavor is more about people than it is about technology.

No one, to my knowledge, has put it better than Kentaro Toyama, professor at the University of Michigan and formerly a researcher of impactful innovation at Microsoft Research India. In his book Geek Heresy: Rescuing Social Change from the Cult of Technology, he introduced the "Law of Amplification", stating that technology merely amplifies any effects of pre-existing human intentions and abilities, whether positive or negative. Technology can't be a silver bullet; you can’t expect it to guarantee results on its own.

"Yet CIOs everywhere are asked to perform exactly that sort of wizardry. The more experienced ones are careful not to promise too much. Technology can improve systems that are already working—a kind of amplification—but it doesn’t fix systems that are broken. There is no knowledge management without management."

People, be they our clients or colleagues, will remain the drivers of our innovation. When we do finally reach the last person in the world who needs our services, perhaps somewhere in Africa or South America, it will be the fruit of the love and collaboration of humans.

Marco is part of Gojo's tech team and works on developing Gojo's digital products and strategy. He is currently leading the development of Gojo's Digital Field Application (DFA) in MIFIDA, our partner company in Myanmar.

Published by Marco Giancotti in Blog posts JP
Tags: Innovation, Technology
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