"Microfinance methodologies
During the last 25 years, microfinance has addressed this problem through a different credit methodology, called “Relational Methodology”. Through this methodology, the microfinance institution uses its “Credit Officers” with the purpose of approaching potential unbanked clients and establish a “Relationship” aiming to understand the economic and social aspects of these individuals. The credit officer collects directly the information associated with the sales, inventory, business cycle, and risks of the potential customer. In order to evaluate the payment capacity of the unbanked customer, the data collected is processed by the Credit Officer, thus proposed to the Credit Committee for the approval of a first financing. However, it takes a lot of time for the approval, using paper-based information for credit risk assessment, credit history, collaterals, persons to vouch, and visiting an office personally."
- it is conveniently and keep risk under control, I think so.
Topic: We can take care of the Unbanked through Blockchain and Big Data (Read 107 times)
January 20, 2018, 01:45:48 PM
By Albi Rodriguez Jaramillo, Lawyer & Senior ICO Adviser
https://cdn-images-1.medium.com/max/800/1*vcyQoDrXbXwX3H8WFBAc5g.jpeg
The financial inclusion to the unbanked population is one of the leading global challenges that must be met to achieve the millennium goals and support the fight against poverty.
The Banking Approach
Historically, traditional banking has based its credit risk assessment on the Reference Model. Through this model, the bank collects “references” that allow it to carry out its risk analysis. These references are usually, among others, financial statements, credit history, assets owned by the debtor and guarantors. Through these methodologies, it is impossible to care for the unbanked population. It lacks these referential elements due to their informality.
Microfinance methodologies
During the last 25 years, microfinance has addressed this problem through a different credit methodology, called “Relational Methodology”. Through this methodology, the microfinance institution uses its “Credit Officers” with the purpose of approaching potential unbanked clients and establish a “Relationship” aiming to understand the economic and social aspects of these individuals. The credit officer collects directly the information associated with the sales, inventory, business cycle, and risks of the potential customer. In order to evaluate the payment capacity of the unbanked customer, the data collected is processed by the Credit Officer, thus proposed to the Credit Committee for the approval of a first financing. However, it takes a lot of time for the approval, using paper-based information for credit risk assessment, credit history, collaterals, persons to vouch, and visiting an office personally.
Limitations of the Microfinance Model
Microfinance and its “Relational Methodology” have demonstrated significant advances in the banking process. However, in recent years this methodology has shown a stagnation in highly competitive markets. In markets such as Peru, India or Kenya, microfinance institutions are concentrated in newly banked clients and end up serving the same clients all over again, without assuming the risk of going to sectors that are not yet being served. This fact is due to the relevance of credit history.
The presence of the human factor in the “Relational Methodology” brings very high costs of supervision, trying to avoid underestimation or overestimation of the information collected, as well as cases of fraud by identity theft or impersonation. These supervision costs, as well as the losses due to bad loans, are accumulated at the interest rates that the new customers must pay on the system. These increasing costs lead to compounding the problem by increasing the interest rates.
As the vast majority of microfinance institutions are hunting for the best clients of the system, in many cases these clients end up over-indebted. Over-indebtedness adds another layer to the risk of default.
Because the judiciary is not an efficient mechanism for the recovery of delinquent microloans, the treatment of delinquency becomes increasingly complex, and its economic cost ends up being transferred to the new participants entering the credit system.
The consequence of all this is that higher interest rates are significant barriers to access and economic growth for low-income clients.
This real picture shows that conventional microcredit methodologies are only efficient when clients can withstand very high interest rates and markets are not mature. These escalating interest rates do not just respond to micro-entrepreneur risk levels but also to the failures of the methodology and its supervision schemes.
MicroMoney.io is a promising project looking to build a decentralized credit bureau platform. Led by Anton Dzyatkovsky and Sai Hnin Aung, this project aims to develop the credit scoring using its current app backed by blockchain. Its proposal is the optimization of the solution to reach the base of the pyramid through blockchain for its microcredit operations in Cambodia, Myanmar, Indonesia, Sir Lanka, and Thailand.
Given the landscape described above, MicroMoney proposes an alternative approach for the unbanked population that as of today has an alternative source of relevant information. For the years of more significant expansion of microfinance (15 years ago), the low-income population had neither access to the internet nor access to smart mobile technology. The penetration and adoption of smartphones in the low-income sectors are increasing every day and has an exponential tendency to reduce its costs.
The low-income population, like the rest of the people, has incorporated the cell phone not only as a communication mechanism but also as an instrument of social and commercial interrelationship. The Big Data derived from smartphones, as well as the global data generated from interaction in social networks, utility payments, and consumption behaviors, have allowed MicroMoney to conceive a credit scoring that can give a first predictive reference to those who do not have a credit history
The creation of a decentralized tool based on Big Data and Artificial Intelligence algorithms can also continue to feed the loop with performance data that clients bring, as well as integrate with conventional sources of risk information and “Relational Models” of microfinance institutions. Service providers, merchants, and retailers could give credit access to the unbanked to pay for goods and services through MicroMoney.
The ultimate goal is to apply new strategies to achieve better results. If we want different results, we cannot continue to apply the same strategies of the past.
Through blockchain, Big Data, and AI, MicroMoney is challenged to provide a credit scoring that allows evaluating more than 10,000 qualitative, quantitative, social, and behavioral parameters of the client. This approach reduces the error or bias of the intervention of credit offices as the single tool.
With the development of the platform, MicroMoney offers the possibility of taking the financial services to where they have not been able to reach. Likewise, the expansion and growth model of the MicroMoney platform will allow easy scaling of the presence in different regions where there is a high concentration of unbanked population
For this reason, MicroMoney’s mission to serve the more than 2 billion unbanked on the planet could be achieved. This ambitious but achievable mission is only possible through decentralized solutions as in blockchain.
https://cdn-images-1.medium.com/max/800/1*vcyQoDrXbXwX3H8WFBAc5g.jpeg
The financial inclusion to the unbanked population is one of the leading global challenges that must be met to achieve the millennium goals and support the fight against poverty.
The Banking Approach
Historically, traditional banking has based its credit risk assessment on the Reference Model. Through this model, the bank collects “references” that allow it to carry out its risk analysis. These references are usually, among others, financial statements, credit history, assets owned by the debtor and guarantors. Through these methodologies, it is impossible to care for the unbanked population. It lacks these referential elements due to their informality.
Microfinance methodologies
During the last 25 years, microfinance has addressed this problem through a different credit methodology, called “Relational Methodology”. Through this methodology, the microfinance institution uses its “Credit Officers” with the purpose of approaching potential unbanked clients and establish a “Relationship” aiming to understand the economic and social aspects of these individuals. The credit officer collects directly the information associated with the sales, inventory, business cycle, and risks of the potential customer. In order to evaluate the payment capacity of the unbanked customer, the data collected is processed by the Credit Officer, thus proposed to the Credit Committee for the approval of a first financing. However, it takes a lot of time for the approval, using paper-based information for credit risk assessment, credit history, collaterals, persons to vouch, and visiting an office personally.
Limitations of the Microfinance Model
Microfinance and its “Relational Methodology” have demonstrated significant advances in the banking process. However, in recent years this methodology has shown a stagnation in highly competitive markets. In markets such as Peru, India or Kenya, microfinance institutions are concentrated in newly banked clients and end up serving the same clients all over again, without assuming the risk of going to sectors that are not yet being served. This fact is due to the relevance of credit history.
The presence of the human factor in the “Relational Methodology” brings very high costs of supervision, trying to avoid underestimation or overestimation of the information collected, as well as cases of fraud by identity theft or impersonation. These supervision costs, as well as the losses due to bad loans, are accumulated at the interest rates that the new customers must pay on the system. These increasing costs lead to compounding the problem by increasing the interest rates.
As the vast majority of microfinance institutions are hunting for the best clients of the system, in many cases these clients end up over-indebted. Over-indebtedness adds another layer to the risk of default.
Because the judiciary is not an efficient mechanism for the recovery of delinquent microloans, the treatment of delinquency becomes increasingly complex, and its economic cost ends up being transferred to the new participants entering the credit system.
The consequence of all this is that higher interest rates are significant barriers to access and economic growth for low-income clients.
This real picture shows that conventional microcredit methodologies are only efficient when clients can withstand very high interest rates and markets are not mature. These escalating interest rates do not just respond to micro-entrepreneur risk levels but also to the failures of the methodology and its supervision schemes.
MicroMoney.io is a promising project looking to build a decentralized credit bureau platform. Led by Anton Dzyatkovsky and Sai Hnin Aung, this project aims to develop the credit scoring using its current app backed by blockchain. Its proposal is the optimization of the solution to reach the base of the pyramid through blockchain for its microcredit operations in Cambodia, Myanmar, Indonesia, Sir Lanka, and Thailand.
Given the landscape described above, MicroMoney proposes an alternative approach for the unbanked population that as of today has an alternative source of relevant information. For the years of more significant expansion of microfinance (15 years ago), the low-income population had neither access to the internet nor access to smart mobile technology. The penetration and adoption of smartphones in the low-income sectors are increasing every day and has an exponential tendency to reduce its costs.
The low-income population, like the rest of the people, has incorporated the cell phone not only as a communication mechanism but also as an instrument of social and commercial interrelationship. The Big Data derived from smartphones, as well as the global data generated from interaction in social networks, utility payments, and consumption behaviors, have allowed MicroMoney to conceive a credit scoring that can give a first predictive reference to those who do not have a credit history
The creation of a decentralized tool based on Big Data and Artificial Intelligence algorithms can also continue to feed the loop with performance data that clients bring, as well as integrate with conventional sources of risk information and “Relational Models” of microfinance institutions. Service providers, merchants, and retailers could give credit access to the unbanked to pay for goods and services through MicroMoney.
The ultimate goal is to apply new strategies to achieve better results. If we want different results, we cannot continue to apply the same strategies of the past.
Through blockchain, Big Data, and AI, MicroMoney is challenged to provide a credit scoring that allows evaluating more than 10,000 qualitative, quantitative, social, and behavioral parameters of the client. This approach reduces the error or bias of the intervention of credit offices as the single tool.
With the development of the platform, MicroMoney offers the possibility of taking the financial services to where they have not been able to reach. Likewise, the expansion and growth model of the MicroMoney platform will allow easy scaling of the presence in different regions where there is a high concentration of unbanked population
For this reason, MicroMoney’s mission to serve the more than 2 billion unbanked on the planet could be achieved. This ambitious but achievable mission is only possible through decentralized solutions as in blockchain.
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