Unlocking IRL reputation in web3

TL;DR

Most blockchain use cases benefit the privileged. This research sheds light on a key existential crisis for millions of people in the Global South that could be solved by bringing DeFi into the real world. It aims to provide the reader with a clear understanding of the problem, its size, and the elements that, if put together, could provide a solution that anyone can participate in. If implemented, this will reward those solving a critical problem with the pleasure of making a meaningful positive impact while obtaining greater profits than any current DeFi protocol.

The research’s structure is as follows:

• The problem: Credits to the poor at a 1% daily interest rate,
• The map: A sea of loan sharks worth ‘DeFying’
• A $1.6T USD problem and the matchmaking opportunity it brings cryptocurrency holders,
• Why it hasn’t been solved yet
• Guidelines for solving this problem
• Actionable steps
• The positive impact of solving this problem could bring

The problem: Credits to the poor at a 1% daily interest rate,

In México, as in most parts of the Global South, a cold-hearted type of being exists known as the loan shark. Loan sharks feed from the desperate need for credit to survive. Although undesirable, they are the only ones offering credits to those micro businesses with a negative credit score or who are unable to fulfill the banks’ criteria.

While loan sharks provide credits for any type of expense, this research focuses on the productive set of credits given to micro businesses. These represent more than 97% of the total economic units of México and about 99% of Latino America (CEPAL, nd).

Within this research, a loan shark was interviewed. He shared information about the “street’s” credit offerings. Credit offerings range from $500 MXN (~$30 USD) to $100K MXN (~$6K USD), while some offer a credit line of up to $60K USD. However, the most common amount is $700 USD.

Although regulated finance institutions in Mexico offer a wide range of borrowing interest rates that go from 13% (Banxico, 2021) to 127.3% (Nu Bank México), they require a positive credit history and compliance with many requirements. However, the non-regulated ones offer interest rates that go from 180% to over 365% a year.

The most used interest rate on the streets is 1% a day. These micro-credits range from short to large cycles. The interviewed company has cycles of 20 days to 126 days, other credit providers (loan sharks) offer a maturity that goes from 4 to 120 weeks. All of the providers charge on a daily or weekly basis.

According to the field research (both interviews and observation) most loan sharks are part of the mafia. Their way of operating is a dangerous pattern: For approving  credit, the borrower needs to provide highly sensitive information that is later used against them. If a payment is delayed by more than 24 hours, the borrower gets a visit by their local loan shark. This visit is in daylight when customers are in, the loan shark comes armed, yelling, threatening, and demanding their pay. Fear is their weapon, but fear does not stop there. If a person delays a couple of days more, the reaction cannot be predicted. Some examples shared by the interviewed borrowers are setting on fire their business, kidnapping a family member, and sending a cooler with body parts and a personal message to them. These kinds of examples are easy to find in the news, a few examples by country:

• El Salvador: El País, 2023.
• Chile: Interferencia, 2024.
• Colombia: Infobae, 2024.
• México: Imagen del Golfo, 2022.
• Latino America: BBC, 2016.

Are loan sharks really the problem?

This research started from a collaboration tech perspective motivated by the hypothesis that web3 tech has the potential to solve the problem of unfair credits. To tackle this research perspective two problem statements were created, one for the borrower side and one for the lending side, which are available below.

Borrower’s Problem Statement

I am a Mexican micro business owner living in Monterrey. I’m trying to get credit with fair interest rates that are large enough to be useful for my business, BUT banks do not offer me credit because of my poor or negative credit history, so my only option is loan sharks, which besides charging a criminal interest rate, keep my credit history siloed, trapping me with them. This makes me feel abandoned by society, hopeless, and enslaved.

Lender’s Problem Statement

I am a holder of cryptocurrencies looking for high-yield options within DeFi/ReFi. I am trying to increase the economic capital I hold while doing something innovative and interesting. However, there’s no easy way to find if an investment opportunity is legit, value-aligned and will genuinely provide the high yield I’m looking for. I feel overwhelmed by the number of available options, the learning curves of experimental DeFi, and the different metrics and information I need to go through to decide if to invest or not.

The map: A sea of loan sharks worth ‘DeFying’

This section explores a three-layered detail of the context of the micro-businesses in need of credit. The first layer shows some context regarding the systemic fragility they live in. The second layer talks about the matchmaking opportunity identified. Finally, the third layer summarizes the whole context through a map.

Systemic fragility

To better understand the context of this research, it’s necessary to define a micro-business. The Mexican Ministry of Economy defines it as an economic unit with one to ten workers and an annual income of a maximum of $4M MXN or about ~$239K USD a year (Secretaría de Economía, 2009).

The systemic fragility experienced by the micro-businesses interviewed by the researcher makes them prone to fail to pay on time, tagging them with a negative credit history, and ultimately banning them from bank credits, whose interest rates from 33% to 149% (La Jornada, 2023) are already high.

Why do they fail to pay on time? Interviewees reported several reasons:

“My cashflow is not predictable nor constant” - Carmen (Borrower #1)

“You know who [the mafia] comes for their share” - Pedro (Borrower #2)

“If I get ill, I need to close my shop, and if I close it, there’s no cash coming.” - María (Borrower #3)

“I am unable to save due to the high interests, so when unexpected expenses appear I cannot do anything but fail to pay” - Ingrid (Borrower #4)

During a day of field research, the lack of financial education can be seen as another challenge. By accompanying a credit company on a day of offering and providing credits, it’s clear that people do not calculate how much they will end up paying. The borrowers’ only decision-making factor is if they are able to pay it from what their typical daily earnings are.

Asking the credit company about the default rate, the employees answered that the risk is high from the traditional bank perspective, but borrowers tend to pay, so what they do is negotiate a new set of terms and conditions that fit the borrower’s needs. In one case, the interviewed company negotiated smaller payments over a larger period, managing to obtain a default rate of only 5%.

“People tend to pay because they have few options. Either they pay us or they need to ask credit to the mafia, they do not negotiate”. -Employee #1 of financial company.

A final insight from the field research regards the requirements that non-regulated financial companies ask and the way they reach out to their clients: They offer credit without asking for a guarantee, proof of income, credit history, or years of experience running their business. There are two ways these companies offer credits: Online and Presentially. The company interviewed reported that their face-to-face strategy has performed better than the online one as they are able to “know” the borrower, and locate them both at their home and business addresses. Interestingly, the borrowers reported that they feel more compromised borrowing from someone who knows their face.

Figure 1

The above insights can be taken further when combined with INEGI’s survey findings, which is one of the most recent and largest surveys made worldwide (OECD, 2022). INEGI (2021) surveyed 280,489 micro, small, and medium enterprises, of which 52.77% were micro businesses.

The INEGI’s survey report (2021) offers deep detail on the factors that limit access to financing, this report also offers a summary in Figure 2. The researcher analyzed the data from both the summary and its detailed report and cataloged the main blockers into three clusters:

• Unfair conditions group: 62% of answers.
• Misunderstanding of reality group: Too many requirements (39%), Large and complicated onboarding (33%), Inconvenient periodicity and payment forms (30%), Requirement for a guarantee or guarantor (11%), Cannot prove income (9%), No credit history (9%), Negative credit history (7%).
• Other group: institutions determined low capacity of payment (14%), Not enough time operating (6%), No credit institutions in the locality (6%), and Other (4%).

Figure 2

By analyzing together the three above-mentioned clusters and the researcher’s findings, it’s possible to conclude that:

• The financial institutions are not offering financial products that fit the reality of micro business borrowers, leaving these borrowers with no other option than loan sharks.
• Even when the main blockers are the unfair interest rates, loan sharks thrive because they manage to cover all the other blockers. Loan sharks facilitate the borrowing process by:
  • going to the borrower and presenting a few requirements,
  • charging in the way the borrowers are able to calculate their payment capacity (daily or weekly periods),
  • not asking for a guarantee, guarantor, credit history, time operating, or proof of income.

A summary of the borrowers’ pain points and credit options is available in Figure 3.

Figure 3

Although the problem is heavier on the borrower's side, the lender is key in the equation.

Interviewed web3 lenders reported unease on three main points.

• Experimental DeFi. It offers highly attractive yields but with the requirement to understand its complexities and the high risks associated with catastrophic events like code vulnerabilities or behind-the-scenes actions that destroy everything in a dominó effect like the FTX and Luna cases.
• Lending or staking in DeFi. It provides an easy to understand way of yielding profits, at the cost of not knowing what their money is funding.
• Staking crypto for positive impact. It provides clarity on where the money is going, however, the profit (if any) is not attractive enough.

A user story to contextualize:

Megan knows there are easy investment opportunities in crypto. She has tested some like fixed lending, staking with dynamic APY, and automated investment strategies with vaults. She knows most offer high profit opportunities with a few clicks. She has both earned big rewards and lost money, however, it was not clear how either happened, She acknowledges the documentation explains it, and that there are thousands of videos and communities around these topics, she is not interested in diving down that rabbit hole. She is not attracted by being surrounded by people talking only about money, she is interested in talking about the subject only if she feels proud her money is making a difference for good, and foremost she is afraid her investments are black boxes whereas she could be funding terrorism, planetary exploitation, or political actions that are against her values and visions.

The lenders’ context summary is illustrated in Figure 4.

Figure 4

An identified opportunity: matchmaking the Web3 space with the real world

On the one hand, borrowing money on the streets is highly expensive and unsafe. On the other hand, DeFi protocols offer stablecoin-based lending rates that are several times cheaper than those offered by loan sharks and banks, even if the DeFi lending rates vary seasonally. For example during the bear market in March 2023, Aave had an average of 3% Annual Percentage Yield (APY), while in Compound was around 6% (Binance Square, 2023). Today, one year later and in a full bull market, Aave has a dynamic average interest rate for borrowing of 15.73%, and an APY for lending of 11.94% (Aave, 2024). In a similar line, Compound holds an average of 11.52% for the borrowing interest rate and an average APY -for lending, of 13.49% (Compound, 2024).

Blockchain technology has been able to remove or reduce intermediaries. Finding a reliable way to bring DeFi to micro businesses is key. Just within 2024’s current crypto’s bull run, the DeFi rates are about 31 times cheaper than local Mexican loan sharks, and within the 2023’s bear market, the difference was about 61 times.

The matchmaking opportunity comes from finding the pain points in each side of the market and how can each side support the other.

Borrower’s side

Analyzing the borrower’s context shows two main problems: Systemic fragility and siloed Credit history.

• Systemic fragility leads to a negative credit history, so they can’t access cheaper credit anymore. Also, due to this fragility, they know if they put something up as a guarantee they’ll lose it. That’s a deal breaker.
• Loan sharks Silo customers’ credit history, so even if a customer has paid back several credits at a 365% annual interest rate, they can’t show the banks or anyone with cheaper rates, because there’s no record.

Lender’s side

During the interviews, the questionnaire, and the Twitter space used to gather insights from web3 lenders, a pattern emerged: Investment opportunities should offer three main things: 1) An attractive profit; 2) Clarity on what their money is funding; and 3) Easy to understand terms and conditions.

Figure 5 shows the context of both sides and the identified opportunity

Figure 5

A $1.6T USD problem and the matchmaking opportunity it brings to cryptocurrency holders

The lending side of the market

$6 billion USD was the total allocation among the DeFi protocols, according to the latest DeFi sector brief (Messari, 2023). Currently, there are about 7.5M unique DeFi users (Statista, 2023). It’s expected that by 2028 this number will grow to 22.09M worldwide (Statista, 2023). Zooming in, 483k unique DeFi users are expected in México that same year (Statista, 2023).

The borrowing side of the market

According to the OECD (2022), 4.2 million micro businesses exist in México. In another report, the INEGI found that 43% of the surveyed SMEs had requested credit at least one time (INEGI, 2022). But most important is that 20% of the surveyed micro businesses had an active credit line when the survey was carried out (INEGI, 2022).

Therefore, if those statistics are extrapolated to the overall available market size of the Mexican micro business sector, the following numbers could serve as the basis for sizing the serviceable national micro credit market:

4.20M micro businesses x 20% that had a credit at a given moment = 840K micro businesses.

According to the micro finance institution interviewed for this research, the average credit line is about $700 USD. In a hypothetical scenario where 840K micro businesses get a credit of $700 in the same, it would mean $588M in credit lending.

Although $588M may already be an attractive market, the ENAFIN reported that 33.9% of the surveyed SMEs can’t access credit due to the high interest rates and the associated complications (INEGI, 2022). Thus, a credit solution with less friction and lower interest rates may bring at least 284.7K micro businesses to ask for credit, meaning that the size of the attainable market in a same given moment can grow $199M, i.e., a total of $787M USD.

The above serviceable credit market estimate of $787M comes from only 1.1M Mexican micro-businesses. This means that over 3.1M micro businesses were left out of the equation, but that does not mean they do not need credit. It only means that a working solution has room to grow.

What about outside of México?

A working solution could be scaled to other places in the Global South and other business sizes where there’s too much of an opportunity gap to be covered by bringing low international interest rates to compete with the high local interest rates.

A report from Dalberg (2019) points to a gap of $1.6 Trillion USD in credit demand by micro and small enterprises in Latino America and the Caribbean alone. Even if that number is already dramatically high, it only corresponds to 14% of the total demand by micro and small companies in emerging markets (Dalberg, 2019).

A visual summary of the above information is available in Figure 6.

Figure 6

The market opportunity

In a hypothetical scenario, the total estimated market of $787M could be covered alone by 10% of the current 7.5M DeFi users if each lent an average of $1049 USD. In a future scenario, the competitive power of DeFi and its 22M DeFi users could be the key to closing the credit gaps worldwide.

Why hasn’t it been solved yet?

This section stands aside from a scientific view to enable the author to provide a personal perspective on the matter.

Root cause

Dalberg (2019) points out the main reasons why financial institutions have neglected MSEs, which is summarized in Figure 7.

Figure 7

Victor Alexiev, a peer reviewer of this article, shared a more succinct description:

“There are a few reasons why lower income markets don’t have good access to credit. The price of credit (interest rate) is determined based on (1) cost of capital, largely driven by the central bank interest rate; (2) the risk or probability of default… modeled by the presence of collateral and good customer KYC; (3) cost of reaching the customer and servicing the credit… modeled by the credit provider’s operations cost; (4) Profit seeking behavior - credible credit providers will have a relatively thin margin on top, the loan sharks put predatory terms because they are aware that nobody else is servicing the market.” -Victor Alexiev

The researcher believes that Alexiev’s four reasons persist today due to the limitations that the web2 infrastructure and political-economy ontology hold. The researcher believes this because he was able to access the accounting records of a credit offering company, and interview one of its workers to better understand the numbers. The profit margins are very high and the default rates are about 5%. So, instead of providing a definitive answer of why it has been solved, a set of questions come in place with the aim to inspire the reflection and, perhaps even a future research endeavor with the aim of exploring such questions.

1. What if the cost of capital came from DeFi instead of a central bank?
2. What if the risk, or probability of default, is modeled through a web of trust based on the real world, web2, and web3 oracles? For example, the capacity of payment based on the flow of clients a business gets is identified by the Google Map data from customers attending that business, leaving reviews, uploading photos, and the time they stay in that business.
3. What if the cost of reaching the customer and servicing the credit is redesigned from a community standpoint? This could mean using the current infrastructure of local businesses to offer the credit, cash it out, and make periodic payments.
4. What if the profit seeking behavior was constructivist, collaborative, and co-owned instead of being extractivist? It could include all participants as co-owners (infrastructure providers, lenders, borrowers, and credit recovery personnel), rewarding them with access to both profits and ways of managing the company, as well as rewarding those paying back with collateral so they can progressively access cheaper credit.

As with any other research, there are limitations that one should be clear about. This research is limited by a 12 week period of research, funding for one single researcher, and the requirement to focus on the problem space rather than on a solution. However, to counterbalance these limitations, the CoLab Fellowship, created by Arbitrum DAO and RnDAO, designed this program to inspire fellow entrepreneurs and researchers to take action. In the next section, the reader will find a set of guidelines on this route.

Guidelines for solving this problem

A potential solution, first draft

Figure 8

An explanation of Figure 8. The criteria for a potential solution were designed as part of the findings in this research. It can be explained in the following way:

1. To lend to local business owners, Web3 lenders require a decentralized identity (DID) as a trust mechanism to assess risk and interest rates. In turn, local business owners require an easy and quick way to borrow, and an easy way to make payments in either crypto or fiat.
2. The borrower should open a self-custodial decentralized identity (DID). This DID will aggregate the different attributes or verifiable credentials (VCs) from its owner. The VCs come from two types of Oracles: IRL Oracle, and Digital Oracle.
3. The borrower should issue initial digital and IRL VCs.
4. The digital oracle VCs are composed of two sub domains, web2 and web3 data sources.
   1. The web2 VCs are issued by data obtained from verifiable information about the borrower, such as those coming from social media, past credits, and other web apps like Google Maps.
   2. The web3 VCs are issued by data from on-chain activity, such as on-chain credit history, the collateral the person has backing her credit line, and a wide set of on-chain verifiable credentials that support risk assessment such as education, attended events, donations made, affiliations, work-related contributions, etc.
5. The IRL oracle’s VCs are those within a traditional KYC process like national ID, phone number, home and business address, municipal license to operate, business’ act, ownership title of assets, etc.
6. Validation occurs. For the digital oracle, algorithms are used to validate. For the IRL oracle, both algorithms and a trusted agent on the ground are used. Verifying identifiers should carry some kind of personal relation. Borrowers reported that associating a face to the credit is what makes them pay, and combining it with knowing that somebody can come to their real business or home address to collect the payment will make the difference. Also, the possibility of fraud is reduced by making an agent on the ground responsible for verifying that the information provided by the client is correct.
7. The on the ground agent needs to confirm that the borrow is a legit company with:
   1. qualified personnel where the customer is located,
   2. legal resources, and
   3. available infrastructure,
8. to ensure that the borrower has ways to cash out, pay back, and in case of default, to be located.
9. The agent on the ground should be trusted. The interviewees offered two ways to trust this agent:
   1. A smart contract-based penalization that could be applied to a staked amount if the company acted wrongly or poorly;
   2. Sharing co-ownership of such a company, so as to have a stake in the ways the company operates.
10. Finally, to tackle the systemic fragility of the borrowers, a part of the profits should be used to delegate collateral to each borrower. A rewards-like program should be put in place so borrowers are incentivized to pay on time. The delegated collateral locked to each borrower has two main functions: First, allow the borrower to progressively access cheaper credit, and second, safeguard lenders when borrowers default.

Criteria to test whether the solution worked

• Did the solution manage to deliver lower interest rates than what the local markets of the borrower offer?
• Did the solution manage to deliver a higher annualized percentage yield than what DeFi protocols offer to lenders?
• Did the solution manage to offer a trust score that does not lock-in the borrower?
• Did the solution manage to provide a way for lenders to know what their money is funding?
• Did the solution manage to tackle the systemic fragility of the borrowers?

If the solution answers yes to all those questions, then it is properly addressing the problem stated in this research.

Principles to follow:

• Open source. To enable the public to enrich the work.
• Modularity. To build an interoperable and remixable solution.
• Zero Knowledge Proofs. To ensure the right to privacy while being able to count data.
• No web3 nativeness, instead web2 to web3 transition. To take into account all the data points a person has built and which will help them get better credit.
• Experimentation. To test approaches in a scientific way that can lead others to validate, replicate, and improve the ways of implementing this solution.
• Web of partnerships. To have people and protocols do what they do best working together with this new approach.
• Zero-distance building. To understand the needs, motivations, and behaviors of the participating lenders, borrowers, credit lending institution, DeFi protocol, attestation protocol, and DID provider. This will ensure that the product works and is adopted.

Potential collaborators

• Ethereum Attestation Service for attesting the trust and decentralized credit scores;
• Candide for creating smart wallets that enable two things: 1) the DID that stores the credit score and 2) an escrow account that allows setting up the smart contract architecture required to execute the collateral delegation in a trustless way, lending, profit distribution from paybacks, collateral build up, accounting, reporting, and oracle to smart contract communication.
• Reclaim Protocol for bringing web2 data on chain in a privacy-preserving way with Zero Knowledge proofs (zKp);
• Token Engineering Academy, TEC and the Regen Score initiative from the Trusted Seed could be allies in defining the right scoring mechanism and criteria;
• Arbitrum DAO could be a potential interested stakeholder via a growth experiment where $ARB tokens could be used as collateral to offer a lower interest rate for the newcomers;
• The main players within the DeFi ecosystem could be interested in providing the capital influx for lending, and if not, a fork of a fixed lending protocol like Notional could be used;

Actionable steps

To support entrepreneurial researchers into building a solution, this section provides two main contributions. First, a checklist of things to gather or build, and second, a solution agnostic description with steps narrated through the perspective of each user.

Initial checklist of things to gather or build:

• [ ] Risk profile assessment framework,
• [ ] Self-custodial DID provider,
• [ ] Attestation provider (both for web2 to web3, and web3 native one),
• [ ] Attestation schemas for an initial set of IRL credentials,
• [ ] Attestation schemas for an initial set of web2 credentials,
• [ ] Attestation schemas for an initial set of web3 credit history,
• [ ] The workflow of credential issuing, verification, and assessment from the individual point of view of the borrower, the agent, and the lender,
• [ ] The workflow from the agents on the ground that facilitate cashout, paybacks, and the recovery of defaulted credits,
• [ ] The token engineering design regarding incentives and penalizations to all actors involved,
• [ ] The lifecycle model of the web3 credit to IRL and back,
• [ ] Legal checkup that the model and workflows will comply with regulations,
• [ ] A smart contract architecture to enforce workflows, incentives, and penalizations,
• [ ] A working group with lenders, borrowers, credit lending institutions, attestation protocol, and a DID provider,
• [ ] A wider group of lenders and borrowers to test the solution,
• [ ] Interfaces that are easy to understand and use by each of the participants,
• [ ] DAOified version of the group that ensures that the micro financial solution commits to solving the challenge of unfair credits instead of just digitizing them.

Solution agnostic guide through user stories

Mary, the borrower:

1. Create DID

Mary has requested a credit that requires her to create a decentralized identity (DID). The DID has a Spanish interface that guides her by voice, clear visuals, and text.

The DID interface shows the trust score gained after each verifiable credential she uploads, in a game-like setting. The DID app shows a wide number of verifiable credentials (VCs) Mary can pick to upload.

The DID is self-custodied by Mary and has social recovery options in case she forgets how to access it.

The verifiable credentials are easy to upload to the DID via a smartphone. The process of uploading gathers metadata to support the verification of the VCs in regard. These credentials are cryptographically secure, privacy respecting, and verifiable by both humans and machines.

Mary uploads the basic ones to request a credit, which are: National ID, Phone number, proof of home and business address.

       2. Trust score validation

Algorithms and AIs verify that the data is valid, has not expired, and complies with the requirements (EX: the proof of address has the name of the requester). After this, Juan, an agent on the ground, receives a notification that a credit requester has issued VCs and that these require verification.

Juan goes to Mary’s business and validates that it is open, working, has clients, and that Mary is the owner. Juan then issues a Google Map VC associated with Mary. Juan helps Mary in issuing as many VCs as possible to increase her trust score. Juan makes sure that Mary understands the credit terms and conditions, including how to cash out their credit and how to pay it back.

Once Mary has her initial trust score validated she is ready to ask for credit from a worldwide audience of web3 lenders.

      3. Paying back

Mary decides how to pay back. She can exchange MXN pesos in cash for crypto using a local p2p solution, she can deposit funds to a centralized exchange, and even accept payments in crypto at her business.

      4. Update of trust score

From each repayment, Mary gets her self-custodied score automatically increased. This is done by a call to the web3 attestation service which monitors her credit contract. The same happens with each time Mary defaults, a penalization to her score it’s automatically applied for each day of delinquency.

Mary’s credit is an on-chain escrow. Her escrow address serves two goals: Disburse the credit to Mary’s wallet, and Receive the payment from Mary. The on-chain activity allows her to build a decentralized credit score that can be used by Mary to request credit from other DeFi protocols or people across the internet.

      5. Cyber-physical actions

The importance of the on-the-ground agent is that Juan can look for the borrower, recover the payment, start a negotiation strategy, and update the situation of the credit contract.

A series of IRL actions are triggered and their corresponding attestations are issued when the borrower defaults. This first IRL action regards requesting an on-the-ground agent to visit the person.

If Mary defaults, she will be visited by Juan. If Mary is located, Juan asks for the defaulted payment. If the defaulted payment is recovered, a minimal penalization to the trust score happens through an attestation of “delayed payment #n”. This attestation includes a note from Juan regarding the reason for default. If the recovery of the defaulted payment is not achieved, a larger score penalization happens via a “Visited but not recovered” attestation. A note is also included.

If Mary shows willingness to pay, either if she could pay back or not, but cannot do it following the agreed terms and conditions, then Juan starts a negotiation strategy. The negotiation strategy is to support Mary in paying back, such as activating a freezing period or prolonging the credit maturity with a smaller periodic payment amount. In either case, two attestations will occur: First, a “negotiation strategy” attestation which triggers a penalization to the score, and a second attestation called “close of negotiation“ which happens when Mary pays off the loan, slightly increasing the score again, or heavily penalizing it in the case Mary stops paying the negotiated agreement.

In case of the borrower’s death, the agent can upload an official act as proof of death to the borrower’s trust score. After the document is validated by the algorithms, a “death proof” attestation is issued and with it an action is triggered: the liquidation of the credit through the collateral, if any. In some cases, if an insurance mechanism is contracted, the proof of death will trigger the insurance contract to cover the collateral lender, the lender, and the family of the borrower.

In the case that Mary is not able to be located, Juan will trigger the repayment to the lender by the available collateral, if any. A “non localizable” attestation with negative impact to the trust score is issued.

      6. Progressive fairer interest rates

Mary gets rewarded with every payment she makes. Rewards are in the form of an increase in the trust score as well as in collateral build-up.

A way to increase the trust score and thus lower the interest rate, is by staking collateral at Mary’s profile. Collateral can be either delegated by another lender, owned by the borrower, or progressively built as a reward for each fully paid back credit.

To progressively lower Mary’s interest rates, collateral staking is incentivized in the following ways:

• Delegated collateral. The collateral lender earns a part of the lender’s interest rate.
• Savings in collateral. Enabling the borrower to put collateral for her credit lowers her interest rate.
• Rewarded collateral. Every credit fully paid back re-distributes some profits back to the borrower in the form of a staked collateral. This collateral is locked and can only be withdrawn by the triggering request of either: a) automated paybacks due to payment default by the borrower or b) in case of death, which remaining collateral -after paying debts, can be claimed by the family of the borrower.

      7. Trust score report

Every time Mary finishes paying her credit contract she gets a report. This report details how much rewarded collateral she earned, how her score evolved, and what new interest rates and amounts she can access with a new credit.

Juan, the micro finance worker:

The validators and micro finance workers need to be trusted somehow. Therefore, Juan has a worker DID. His DID is updated with attestations that penalize his reputation if he attempts to game the system or performs his role poorly, and increase his reputation when fulfilling his metrics. Juan earns a salary based on his reputation. His reputation is based on how many credits he allocated, how many of those credits were paid back, and the score of the borrowers he brought in or validated. Based on his salary, at the end of the year, Juan gets a share of the micro-finance treasury. His share is based on his reputation which is slashed or increased according to his metrics.

Megan, the lender:

Megan can either lend to a package of credits that will distribute its pool to several qualified borrowers or choose who to lend to by browsing among the individual profiles of credit requesters. Search is filtered by risk appetite. Profiles are cryptographically secure, privacy respecting, and both human and machine-verifiable.

Each profile shows the trust score of the person or business in regard, as well as specific information from the credit line such as what’s the credit for, how much credit is requested, for how long, number and amount of payments the borrower commits to, interest rate, the collateral available, and the opportunity to lend either in collateral or directly to the borrower. Megan can easily see how much she can earn from lending to the profile in regard.

If the borrower defaults, Megan is notified about what will happen.

Megan receives her yield automatically each time her lending contract, which is an escrow, receives a payment.

Once the credit has been paid back, Megan gets an attestation that increases her lender’s trust score, which might be beneficial if in some time in the future she wants to borrow too. Every lending attestation comes with a financial impact report.

Final recommendations

If the reader is interested in contributing to tackling this key challenge, there will be a call to participate through a set of open-source initiatives called Nouns Bank and deCredit Score. These initiatives will test this publication through a constructive research approach where:

Nouns Bank is a meme-social club that will gather crypto holders equally interested in making a positive impact in the world while also making a profit.

This social club could start as a DAO whose membership token provides three initial exclusive benefits: First, choosing which borrowers the DAO should stake its treasury on as collateral and how much of it to each. This will immediately reduce the interest rates of the borrowers. Second, enabling the members to access lending yields higher than any other DeFi protocol offer, such as 24% a year. And third, deciding how to distribute the DAO profits coming from staking collateral on borrowers, so as to make the most impact possible.

The trust score mechanism will be created using Ethereum Attestation Service and Reclaim Protocol. The meme-social club will be kickstarted using the Nouns framework. The community and smart contract governance will be deployed using Charmverse, Hats Protocol, and Tally. The escrow smart contract can be achieved through an Account Abstraction provider such as Candide or Holonym, and the automatic distribution of payments to lenders, stakers, and workers can be done using SuperFluid.

In the above mentioned constructive research, a DAO will be created and it will look to buy the traditional finance company so that the personnel on the ground, their expertise, and their clients database become part of the DAO. This has been already agreed with the company, and an initial pilot will be carried out with 100 clients. This organization will first focus on onboarding the existing clients into creating their web3 wallets and DIDs, as well as tokenizing their current IRL credit scores and their trust scores in a self-custodied fashion.

The positive impact solving this problem could bring

Dalberg (2019) in its extensive study regarding closing the credit gap in the Global South, pointed out several benefits solving this problem could bring:

• Increasing the profits from their operations by about 54%,
• Supporting their ability to meet daily expenses, specially after unexpected expenses take their account balance to zero,
• Improve their business productivity, quality of their operations, catch up with their customers’ demand, and ultimately, grow their business,
• Improve their financial literacy,

The author speculates that solving this challenge through DeFi might accelerate the number of web3 users that want to take part, either as borrowers or lenders. And that would be only their starting point, as this solution already touches on the worlds of decentralized IDs, DAOs, smart wallets, and more.

Additionally, disrupting the current niche of micro credits, might bring the attention of big players within the centralized finance world, and several outcomes are imagined:

• Banks and hedge funds could invest in this solution, bringing more money to close the gap worldwide.
• Professionalization and diversification of DeFi-based risk assessment frameworks.
• Showcase that a post-Capitalist way of financing is possible, replicable, and desirable.

The future has not been yet written, it is up to us on the decisions we take today.

Final note

I, Humberto Besso Oberto Huerta, the author, want to dedicate this last section to thank the RnDAO and the Arbitrum DAO for the opportunity to participate in the CoLab Fellowship. I am deeply grateful to Yatan, Andrea, Lino, and Daniel (the RnDAO team) who supported me throughout this research endeavor. Special thanks to my peers Milena, Rich, Artem, and Dominik who shared with me feedback, links, and dozens of hours in swarm mode. And thanks to the peer reviewers too. Big thanks to Erick Halsey for his deep editorial support.

I am grateful to the people who shared their precious time with me to participate in the interviews, surveys, Twitter spaces, and that allowed me to observe them on the field. Thanks to Gerardo Pérez and Juan for their contributions, open minds, and true willingness to shift from a traditional financial company toward a post-capitalist financial DAO. They are true heroes to me.

Thanks to Anna Kaic., Juan Giraldo, Sofi Villarreal, Irwing Durán, Johan De Jesús, Mateo, and José Luis, for engaging in active conversations about these research findings and being supportive in kickstarting an open source solution for turning this research into impact. Thanks to the people who donated to the deCredit Score initiative. I am deeply grateful for their support.

Thanks to the readers who read everything until this point. Thank you. You are the decision makers of our future. What will you do with this knowledge?

Finally, I want to dedicate this research to my parents, Carmen and Humberto. They have always encouraged me to make the most positive impact possible. Hopefully, this research contributes to making some good in the world.

Bibliography & resources

La Jornada. (2023). Bancos cobran intereses hasta de 100% en tarjetas de crédito básicas. Extracted from https://www.jornada.com.mx/notas/2023/06/18/economia/bancos-cobran-intereses-hasta-de-100-en-tarjetas-de-credito-basicas/ on 21/March/2024

Imagen del Golfo. (2022). Préstamos gota a gota de mafia de los 'colombianos', ¿deuda de muerte en Veracruz?. Extracted from https://imagendelgolfo.mx/policiaca/prestamos-gota-a-gota-de-mafia-de-los-colombianos-deuda-de-muerte-en-veracruz/50240343 on 21/March/2024

Infobae. (2024). Apareció joven bogotana que había desaparecido tras amenazas de prestamistas. Extracted from https://www.infobae.com/colombia/2024/03/11/aparecio-joven-bogotana-que-habia-desaparecido-tras-amenazas-de-prestamistas/ on 21/March/2024

Interferencia. (2024). Pagas o te matamos: la nueva forma de cobrar de créditos obtenidos por internet. Extracted from https://interferencia.cl/articulos/pagas-o-te-matamos-la-nueva-forma-de-cobrar-de-creditos-obtenidos-por-internet on 21/March/2024

El País. (2023). Gota a gota: qué son y cómo funcionan los préstamos ilegales ofrecidos por grupos criminales. Extracted from https://elpais.com/america-colombia/2023-10-03/gota-a-gota-que-son-y-como-funcionan-los-prestamos-ilegales-ofrecidos-por-grupos-criminales.html on 21/March/2024

BBC. (2016). Qué son los préstamos "gota a gota" que grupos criminales de Colombia exportan al resto de América Latina. Extracted from https://www.bbc.com/mundo/noticias-america-latina-37708989 on 21/March/2024

CEPAL. (nd). Acerca de Microempresas y Pymes. Extracted from https://www.cepal.org/es/temas/pymes/acerca-microempresas-pymes on 21/March/2024

Messari. (2023). DeFi Sector Brief: December 4, 2023. Extracted from https://messari.io/report/defi-sector-brief-december-4-2023?referrer=research-reports on 06/March/2024

Statista. (2023). DeFi - Worldwide. Extracted from https://www.statista.com/outlook/fmo/digital-assets/defi/worldwide on 04/March/2024

Binance Square. (2023). Recent statistics on the DeFi lending and borrowing market. Extracted from https://www.binance.com/en/feed/post/350273 on 04/March/2024

Secretaría de Economía. (2009). Acuerdo por el que se establece la estratificación de las micro, pequeñas y medianas empresas. Extracted from https://www.economia.gob.mx/files/marco_normativo/A539.pdf on 22/March/2024

OECD. (2022). Financing SMEs and Entrepreneurs 2022: An OECD Scoreboard. Extracted from https://www.oecd-ilibrary.org/sites/53f52411-en/index.html?itemId=/content/component/53f52411-en on 06/March/2024

INEGI. (2023). Comunicado de prensa núm. 377/23. Extracted from https://www.inegi.org.mx/contenidos/saladeprensa/aproposito/2023/EAP_DIAMIPYMES.pdf on 7/March/2024

INEGI. (2022). Comunicado de prensa conjunto CNBV-INEGI. Extracted from https://www.inegi.org.mx/contenidos/saladeprensa/boletines/2022/enafin/Enafin2021_Nal.pdf on 18/March/2024

INEGI. (2021). Encuesta Nacional de Financiamiento de las Empresas 2021: ENAFIN. Síntesis metodológica. Extracted from https://www.inegi.org.mx/contenidos/productos/prod_serv/contenidos/espanol/bvinegi/productos/nueva_estruc/889463907404.pdf on 18/March/2024

Banxico. (2021). Indicadores Básicos de Créditos a las Pequeñas y Medianas Empresas (PyMEs). Extracted from https://www.banxico.org.mx/publicaciones-y-prensa/rib-creditos-a-pymes/{D50011D6-98BB-1D8A-85C6-C635535A04DE}.pdf on 18/March/2024

Nu Bank México. (2024). Te damos transparencia y claridad. Extracted from https://nu.com.mx/credito/ on 18/March/2024 at 14:23 UTC -6

Aave. (2024). Extracted from https://app.aave.com/ on 06/March/2024

Compound. (2024). Extracted from https://app.compound.finance/markets on 06/March/2024

Dalberg. (2019). Bridging the credit gap for Micro and Small Enterprises through digitally enabled financing models. Extracted from https://www.findevgateway.org/sites/default/files/publications/files/external_190131_final_report_mses_cgap_external_final_updated-bisvb.pdf 06/March/2024

Dalberg. (2019). Bridging the credit gap for Micro and Small Enterprises through digitally enabled financing models [Graph]. Extracted from https://www.findevgateway.org/sites/default/files/publications/files/external_190131_final_report_mses_cgap_external_final_updated-bisvb.pdf 06/March/2024.

‍

‍

See Humberto's presentation at our Co.Lab Fellowship Demo Day on April 4th here

‍