Credit access for underserved communities

The current credit scoring system affects almost every economic aspect of Americans' lives, especially minorities. Generally, credit reports are used to assess creditworthiness and other decisions such as potential employers, insurance companies, prospective landlords, etc. Thus, credit reports enormously influence consumers' access to employment, housing, affordable credit, and affordable auto and homeowners insurance. The expansion of the use of credit scoring has been accompanied by concerns that it may affect the availability and affordability of credit and insurance and that factors included in credit-scoring models may have adverse effects on specific populations, particularly minorities.

Ninety-two million adults (1 out of 3) that live in the U.S. cannot be scored using traditional credit reports; 69 million have an income of 50K or less. FICO is a score of 12 variables that measures the quality of people's credit history but does not measure repayment capabilities. For instance, it does not measure job stability or your ability to generate income. However, Financial Institutions and other service providers have misused traditional credit scores, trying to evaluate people's future repayment capabilities with their credit history. This situation has created a barrier to financial and social inclusion. If the credit score is a gatekeeper to good credit, disparate impact is unavoidable. There are significant disparities in the distribution of credit scores among racial groups. For instance, while 52% of White Americans have more than 720 in their credit scores, these numbers plummet to 20% for Black Americans and 29% for Hispanic Americans. 

With this lack of credit, the only financial alternative for these people is predatory lenders, who charge insanely high-interest fees.

Last year, in Texas alone, predatory lenders took home two billion in interest fees and thirty billion across the U.S.

If people haven't asked for credit, that does not mean they cannot generate income, right? Ninety-two million adult people in the U.S. do not have at least four credit accounts to be scored using the traditional credit score methodology. Dollarito is a digital lending platform that addresses this problem by helping underserved communities with no credit history or low credit score access fair credit. It uses banking, transactional, behavioral, and local economic environment data to evaluate the repayment capability more accurately than using traditional credit scores. Dollarito's unique lending approach increases financial inclusion and avoids predatory lenders in the community. 

Dollarito offers a 100% digital experience in both English and Spanish. Users complete a survey on the website regarding their income, employment, spending patterns, and more. Once they complete it, they receive a personal loan offer. Once the users accept the loan terms, they only need a government-issued I.D. and a bank account. All the information is extracted from verified sources, avoiding input mistakes and increasing the underwriting quality of the loan. Users receive instant responses and feedback after applying, and if approved, they get the money in less than 24 hrs. 

Dollarito uses banking, transactional, behavioral, and local economic environment data to evaluate repayment capabilities instead of using traditional credit scores. We leverage cloud technologies and explainable artificial intelligence to reduce operational costs and increase financial inclusion.

https://www.youtube.com/watch?v=gIPTug3x7_k

The target population is financially underserved communities that do not have access to cover a 1K unexpected expense from savings. Those with little-to-no credit history, such as:

• Youngs,

• Immigrants,

• Hispanic or African American,

• Have not used credit for a long time,

• Not use credit accounts,

• Recently widowed or divorced

• Be cash or debit card users

We estimate this population to be around 92 million adults in the U.S.

Carmen has more than 15 years of experience in human development and social policy. Carmen participated in poverty alleviation strategies that improved food access to 5 million families in Mexico. She took part in the Presidential campaign of the former President of Mexico. There, she was in charge of promoting the vote of Mexicans living in the U.S. This allowed her to be immersed in the Mexican community and build over time ties with immigrant leaders, chambers of commerce, and immigrant organizations in the U.S. Since then, she has realized how Mexicans and Hispanics are financially underserved.

Eduardo has 10+ years of experience applying A.I. and Behavioral Economics in multi-billion-dollar social projects. In Mexico, he participated in the launch of the largest social bank globally. This project was financed by the World Bank and replicated in 52 countries. Ten million bank accounts were created in less than one year, providing basic financial services as a package of savings accounts, microinsurance, and micro-loans to families in poverty. Moreover, Carmen and Eduardo experienced the problem Dollarito is tackling when arriving in the U.S., which inspired us to create Dollarito.

We are processing 200 applications per month.

Gain exposure: We, as part of the Hispanic minority community, need to make this problem visible to empower financially underserved communities in the US and make financial institutions aware of the potential of this market. For instance, Latinos´GDP economic contribution is a crucial driving force of the US Economy since it grows 57% faster than the US GDP as a whole. In 2019 Latinos GDP was 2.7T.

Join a powerful network of impact-minded leaders:  In particular, we would like to be involved with the MIT AI Policy for the World Project. Financial inclusivity can leverage AI technologies and real-time data to improve the lives of underserved communities worldwide. Our use case is relevant for the US and LATAM and can be replicated in Africa and Asia. However, this solution must be ethical and follow the best practices.

Coaching and strategic advice: We need to gain advice and expertise in the financial industry in areas such as regulations and compliance, technical innovation, marketing strategies, and how to detect and retain talent. 

Monitoring and evaluation support. 

We need to develop a monitoring and evaluation framework since, to scale our solution, we need to measure the social and economic impact on our users' lives. Additionally, we need to know how our solution impacts ESG and how it benefits our potential banking partners' financial goals and performance. 

Dollarito uses artificial intelligence(AI) and behavioral economics to determine the repayment capability using data from banking transactions, behavioral data, and economic variables related to the applicant's income source, surpassing FICO methodology limitations and opening new ways for financial inclusion at a lower cost. 

For instance, in case of emergency and with the lack of credit history, the only financial alternative the Hispanics have are predatory lenders who charge high-interest fees insanely. Last year, in Texas alone, predatory lenders took home two billion in interest fees and thirty billion across the US. For example,

• FirstCash, a pawn lender based in 2,770 pawn stores in Texas, charges up to 240% interest on a loan. They receive a 400 million credit line from local banks.

• Enova, another online payday loan, documents annual loan rates of 409% to 664% and has received credit lines for 55 million.  

• World Finance is another high-cost lender with 1200 storefront offices that provides loans that, under Texas law, carry 80% to 113% interest plus fees.

Our repayment capability methodology is based on cash flows, bank transactions, behavior, and the local economic environment where the user lives.

For instance:

• The AI considers income activities such as gardening, construction, and restaurant, detecting patterns in lower-income population income activities and expenses such as rent and utilities.

• The AI takes into account the local environment to predict job stability. Suppose the applicant is in the construction sector in the Austin metro area, where house prices are skyrocketing. In that case, we can expect job stability in the following 12 months.

• The AI accounts for up to 24 months of banking transaction history in checking, debit, and credit accounts to detect patterns and behaviors.

• Dollarito monitors the Loan Quality Profile continuously. Every time the user makes a transaction in her bank account or modifies her lending profile, Dollarito updates the Repayment Risk estimates. This way, Dollarito can act proactively if the expected risk increases.

This new methodology based on real repayment capabilities can be used in all industries, not only lending, for instance, for renting a house to get lower insurance rates. 

Generally, credit reports are used to assess creditworthiness and other decisions such as potential employers, insurance companies, prospective landlords, etc. Thus, credit reports influence consumers' access to employment, housing, affordable credit, and affordable auto and homeowners insurance. 

This year we want to 

1. Design a new methodology based on Artificial Intelligence, Behavioral Economics, Cloud Technologies, and Real-Time Data to measure repayment capabilities.

2. We want to use this data to provide loans in case of emergency, where our users currently are in the hands of predatory lenders.

Next year

1. We want to deploy our methodology on an API where other economic actors in other industries can benefit from this new methodology.

2. We want to create a cross-border loan where the loan originated in the US, and the money will be disbursed in Latin America so that the funds can have the most significant impact than the remittance but the loan faces the risk from the US, not from the Latin American country. 

Five years

1. We want to create embedded finance products based on our methodology to measure repayment capabilities.

2. We want to build a bridge of trust between financially underserved communities and traditional financial institutions so that both parties can benefit at a lower cost.

3. We want to expand our offering from lending products to face emergencies to embedded finance products for wealth creation in underserved communities leveraging the information infrastructure and our repayment capabilities models.  

1. No Poverty

Indicator: Number of loans to individuals below the poverty line

4. Quality Education

Indicator: provide financial literacy

8. Decent Work and Economic Growth

Indicator: Number of loans to self-employed individuals, and micro and small entrepreneurs.

5. Gender Equality

Indicator: Number of loans to women.

10. Reduced Inequalities

Indicator: Avg interest rate and repayment rate vs “traditional lending.”

Indicator: Model Bias by age, sex, marital status among others.

Relying only on traditional credit scores potentially excludes many Americans from accessing fair credit. Preventing people from credit diminishes their ability to thrive and grow.

However, by applying alternative underwriting models to provide inclusive financial services, we can help to reduce poverty and boost shared prosperity.

Theory of Change Steps.

Short-term: help them with an emergency or unexpected expenses

• Activities: develop a more inclusive underwriting model to assess individuals' creditworthiness.

• Outputs: provide loans.

Medium-term: Increase access to more affordable financial services they need to thrive. 

• Activities: Deploy an API with our repayment capabilities model that other financial institutions can use.

• Outputs: to increase consumers' access to employment, housing, affordable credit, and affordable auto and homeowners insurance.

Long-term: Increase access from emergency financial products to worth creation services

•  Activities: Build information and technological infrastructure based on our repayment capabilities model and real-time data.

• Outputs: Become a bridge between financially underserved markets and traditional financial services that can offer value-added services.

Dollarito uses artificial intelligence(AI) to determine the repayment capability using data from banking transactions, behavioral data, and economic variables related to the applicant's income source, surpassing FICO methodology limitations. 

The web app's user interface is simple and designed for the low-income Hispanic population. They only require a government-issued ID and a bank account. All the information is extracted from verified sources, avoiding input mistakes and increasing the underwriting quality of the loan.

Dollarito offers additional advantages to traditional banking and online lenders, such as:

• Superior user experience, transparency, convenience, and safety in both English and Spanish. 

• Instant loan response at prequalification and formal application, with disclosure on why the loan was not approved and feedback on improving their creditworthiness.

• An innovative technological solution that efficiently connects the supply and demand of capital.

• Serverless operations in the cloud allow horizontal scaling, continuous improvement, and the deployment of new services that the clients demand.

• Artificial intelligence trained with banking transactional and behavioral data specific for the lower-income population and technology that maximizes operational efficiency and helps minimize costs. 

• Our AI is explainable and simple, and we can track biases among several variances of interest in real time. 

• Data Governance, Security and Ethical AI. Our Machine Learning Operations are containerized, and a data mesh strategy is implemented. All the access to models, data, and notebooks in production or development have an audit trail and strict access control. 

The following processes have been automated:

Credit application, Processing Information, Underwriting, Credit decision, Fraud detection, Compliance with regulations, particularly those related to money laundering, and knowledge of your customer (KYC).

Dollarito embraces diversity and all the company has built with a strong human-centered approach.  We are convinced that diversity and inclusion are vital elements to get outstanding outcomes. 

Dollarito was funded by two BIPOC individuals, immigrants from Mexico. The CEO is a Hispanic woman.

Our services are tailored to underserved communities such as Hispanics and African Americans. We want to provide equal gender access to credit. 

Dollarito has a diverse cultural work policy, where every individual is respected and accepted with independence of race, gender, nationality, age or marital status.

Dollarito offers two loan products, a small-dollar loan ($500 - $1,400; 6 -12 months; APR 35%) and a microloan ($3,000 to $5,000, 18-24 months; APR 19%).

Dollarito aims to offer a loan product with an interest rate close to a credit card APR.

The application process and the repayment are 100% online, which improves coverage in areas where traditional banks do not attend and covers working hours from users that usually work in labor-intensive activities and do not have time to attend the bank.

To keep engaging our users and help them develop, we offer a weekly analysis report of their finances, offering advice on improving their expense patterns to create wealth. 

In the future, we want to offer a cross-border loan; the loan is originated in the US, and the money is disbursed in Mexico and other Latin American countries, improving the impact of the remittances and creating a cross border economy with embedded financial offerings, but with significantly less risk since the loans are exposure to the US risks. 

Our unit economics are based on our lending products

If we are considering a $5000 loan with a monthly interest rate of 2.5% and a repayment term of 24 months.

With the following three main assumptions.

1. Acceptance rate (conversion rate): 1 out of 10 loans are accepted.

2. Churn rate: 5%, which is the default rate

3. Cost structure: 10% cost of capital, 5% operation cost once economies of scale are achieved.

1. The expected Customer Acquisition Cost (CAC) per user is $20 (actual CAC is $0).

2. The lifetime revenue (LTR) is estimated at $1,810. (one thousand, eight hundred ten)

3. The lifetime value (LTV) is estimated at $868. (eight hundred sixty-eight)

4. At scale, Dollarito estimates an LTV: CAC ratio of 43X

Initially, we offer loans from our books to prove that the model works and gains traction, derisking possible alliances with banks and other financial institutions.

In the long term, we plan to get credit lines from financial institutions to fund the loan portfolio and to get venture debt from our partner banks to grow operations. 

We have raised $320K and $70K in grants to kick off operations. 

Dollarito has received its lending license in Texas and has received soft audits for its compliance and KYC/AML policies.

Dollarito partnered with Plaid to continuously improve their offering; for instance, we are about to become one of the first lenders to use Real-Time Payments operations to disburse money to our users in less than 15 minutes. 

Dollarito has been accepted in prestigious accelerator programs such as Visible Hands, in partnership with Google for Startups, Gener8tor - gBETA Houston in partnership with the Greater Houston Partnership, Adapt from VillageCapital and MetLife Foundation, Plaid Accelerate from Plaid, and IBM-MassChallenge Artificial Intelligence and Cybersecurity program. Recently, Dollarito won second place in the IBM AI and Security Mentorship Program contest, beating over 100 startups from more than eight countries. Dollarito also won a prize from the ADAPT: Social Innovation for a More Resilient Future program, which supports individual and community resilience in the United States, funded by the Metlife Foundation and the Bill and Melinda Gates Foundation.