Psychometric

Blog | The LenddoEFL Assessment Part 2: Measuring how people answer questions with metadata

By: Jonathan Winkle, Manager of Behavioral Sciences, LenddoEFL

The last post showed how our psychometric content reveals people’s personality traits, but our assessment also captures an abundance of metadata. Metadata is information about how people process the questions and exercises they complete. Here are some examples.

  • How long did an applicant take to answer a question compared to their average response time?

  • How many times did an applicant change their mind and switch their response before submitting their answer?

  • Is the applicant’s information consistent with their written request to the financial institution? (e.g., requested loan amount)

By measuring metadata, LenddoEFL’s approach goes beyond what is possible in traditional credit applications to reveal more information about applicants. Consider the following question from our test:

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For this question, we consider how long it took the applicant to slide to one answer or another and whether they changed their opinions in the middle. Someone who is confident that they are an organized person should move the slider in only one direction and relatively quickly. Quick, smooth answers belie confidence, whereas slow, wavering responses demonstrate uncertainty.

The relationship between response time and default rate can be complex. Consider another psychometric exercise:

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In this case response time was a non-linear predictor of default, where both slow and fast response times were associated with a greater credit risk!

There are many ways to interpret response time metadata. If an applicant answers a question quickly, are they confident or are they cheating? If they are taking a long time to respond, are they having difficulty understanding the question or putting extra effort into getting their answer right? By collecting metadata across all questions, we can compare a single response time to the applicant’s overall response time distribution to differentiate things like confidence and cheating (see graph below).

An example distribution of response times generated from artificial data

An example distribution of response times generated from artificial data

Conclusion

Metadata reveals another layer of behavior on top of the personality traits we target and can be used to identify features such as confidence, cheating, and confusion. These behavioral traits can be used for predicting default and ensuring that we are collecting high quality data for our models.



Blog | The LenddoEFL Assessment Part 1: Using psychometrics to quantify personality traits

By: Jonathan Winkle, Manager of Behavioral Sciences, LenddoEFL

At LenddoEFL, we collect various forms of alternative data to help lenders verify identities, analyze credit risk, and better understand an individual. One of our most important tools for financial inclusion is our psychometric assessment. While some people still lack a robust digital footprint, everyone has a psychological profile that can be characterized and used for alternative credit scoring.

In this series of posts, we shed light on the science behind the LenddoEFL psychometric assessment and how we’ve pioneered an approach to measure anyone’s creditworthiness.

Psychometrics for credit assessment

LenddoEFL employs a global research team to ensure our assessment captures the most important personality traits that predict default. We deliver innovative psychometric content by combining insights from leading academics with years of in-house research and development.

Each question in our assessment is targeted to reveal psychological attributes related to creditworthiness. We quantify behaviors and attitudes such as individual outlook, self-confidence, conscientiousness, integrity, and financial decision-making in order to build an applicant’s psychometric profile. By comparing this profile to others in the applicant pool, we can better understand and predict an individual’s likelihood of default.

Psychometric example content: Financial Impulsivity

The marshmallow test asks children whether they would you like one marshmallow now or two marshmallows later, and since its advent, psychologists have recognized that the ability to delay rewards is an important predictor of later success in life.

While adults might not long for marshmallows the same way children do, a similar test can be performed using financial rewards, and research shows that people who are better at delaying rewards are less likely to default on their loans.

Drawing from this research, we ask applicants which of two options they would prefer, a smaller sooner amount of money, or a larger later amount (see image below). Asking people for their preferences across a range of monetary values and temporal delays reveals a quantitative profile of their financial impulsivity, which is indicative of their likelihood to repay debts (If you’re curious about how we deal with people trying to cheat or game the assessment, please see this blog post on our Score Confidence algorithm).

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Psychometric example content: Locus of Control

When times get tough, some people believe they can take action to overcome hardships while others believe that the challenges they face are altogether out of their hands. Those who believe their lives are governed by outside forces, an external Locus of Control, are more risk-averse and have more difficulty managing their credit.

We ask applicants to rate their agreement with a battery of statements measuring their Locus of Control, such as “My life is mostly controlled by chance events,” and “It is mostly up to luck whether or not I have many friends.” By asking these types of questions, we can precisely quantify someone’s Locus of Control along a spectrum of internal-to-external and use this data to predict default.

Conclusion

LenddoEFL delivers an innovative psychometric assessment by combining evidence from academia with active, internal research and development.  The examples above demonstrate how we quantify certain personality traits, and the myriad exercises we use in the field allow us to produce a rich psychological profile that is predictive of credit risk. In the next post we will explore the concept of metadata, which will show that how people answer psychometric questions is just as important as the answers themselves.

CFI.Org | To Bank the Unbanked, Start Using Alternative Data

Capturing digital footprints using psychometrics can help FSPs reach the unbanked.

By Rodrigo Sanabria, Partner Success Director, Latin America, LenddoEFL

Originally posted on the Center for Financial Inclusion's Blog.

In a recent post on her report, Accelerating Financial Inclusion with New Data, Tess Johnson highlighted the huge opportunity that alternative data represents for the future of financial services. The simple fact that mobile and internet penetration have surpassed financial services penetration in most emerging markets hints at a big opportunity: many people who have had no meaningful access to formal financial services are creating digital footprints financial service providers can capture and analyze to reach them with commercially viable services that help them improve their lives. This prospect is also made possible thanks to machine learning and big data methods that were not available to us a few years ago.

Field team testing its psychometric credit assessment in Mexico. Credit: LenddoEFL

Field team testing its psychometric credit assessment in Mexico. Credit: LenddoEFL

For those of us in the world of financial inclusion, these are very exciting times: the simultaneous emergence of online penetration and data analysis methods is generating an opportunity that our predecessors in this field couldn’t even have imagined.

The bad news is that harnessing digital footprint data using machine learning is not easy; it requires time, commitment and skills that are in short supply. However, the good news is that those with the vision and  endurance to leverage this opportunity will build a competitive advantage that will be sustainable for years to come.

When developing an alternative credit score based on traditional information (e.g., demographics, repayment data), analysts usually have historical data to design and train models. Through back testing, the credit scoring model is applied to historical data to see how accurately it would have predicted the actual results (i.e., loan repayment). We can get a pretty good sense of how the model will perform in the future and set up a credit policy accordingly. Yet, when we cannot use such traditional data sources, we are entering into uncharted territory.

Lacking prior information about our current customers’ psychometric profile or digital footprint, we must build those data sets from scratch. Depending on the data source, we may need very large data sets to compensate for the lack of data structure (unstructured data is simply data that is not easily accessible in a format or structure, like an Excel spreadsheet, that is optimal for generating insights). Just as with all other artificial intelligence applications, the more data you collect, the more predictive and stable your algorithms become. LenddoEFL is an example of an organization that gathers data for these profiles and footprints. It is an alternative credit scoring and verification provider that uses psychometric and other data about a loan applicant to determine a credit score and verify identity.

Furthermore, even state-of-the-art alternative data sources do not necessarily allow you to build models that are stable and reliable across multiple segments of the market. Therefore, you need to build algorithms that are specific to your target population.

One of the most challenging issues when implementing alternative data scoring initiatives is showing the results that can be achieved within a given set of time and budgetary constraints. In the long run, after the portfolio has matured, you can show whether using alternative data allowed you to approve more applicants within your target default levels, controlling by business cycle. But if you are working with 24- to 36-month loans, it may take three or four years before you can fully assess the impact of using alternative data, by which time internal attention spans may have already run short.

To account for that, LenddoEFL uses early indicators of model performance. We set a target maturity and days in arrears according to a financial institution’s portfolio’s profile, for example, 60 days in arrears within the first 9 months. Then we calculate a Gini coefficient—a scale of predictive power that can help lenders understand how good its credit score is at assessing who will repay and who will default on a loan (not to be confused with the Gini coefficient that measures income inequality) for the model as applied to that portfolio. (For more details on how to use the Gini, check out our blog series from our risk and analytics team: Part 1Part 2Part 3).

Is it too late to pursue an alternative credit scoring initiative? I would say yes, there are plenty of companies already doing this—Te Creemos in MexicoMynt in the Philippines and Business Partners in South Africa—but only a few lenders are utilizing alternative data in each market. You could be the first institution in your segment and country to implement such an initiative, and you can still take advantage of others’ experiences and learning.

The sooner you start collecting data and building models, the sooner you will be able to underwrite the unbanked segment better than your competition, and the longer the window of advantage will be. For those who start late, catching up with the early adopters will be a great challenge.

Read article on cfi-blog.org

Caja Sullana provee a jóvenes emprendedores acceso a crédito en alianza con LenddoEFL, en el marco de proyecto con Fundación CITI y COPEME

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Lima, Peru, 16 de julio del 2018 – Organizaciones se han unido para financiar una alternativa innovadora de evaluación crediticia para incrementar el acceso a financiamiento a jóvenes que no cuentan con posibilidades de acceder a financiamiento que recién están comenzando un negocio.

Cuatro instituciones se han unido para invertir y avanzar en la innovación liderada por jóvenes en el Perú. Los jóvenes emprendedores que tienen dificultades para acceder a crédito debido a la falta de historial crediticio, ahora pueden solicitar un préstamo de negocios de la institución financiera peruana Caja Sullana usando la evaluación de crédito psicométrica de LenddoEFL, una fintech que potencia las decisiones basadas en datos alternativos para promover la inclusión financiera.

Fundación CITI financia esta iniciativa como parte de sus esfuerzos para impulsar las iniciativas empresariales de los jóvenes en los mercados emergentes. COPEME, una organización peruana que promueve la inclusión financiera, gestiona este proyecto como potencial para expandir esta tecnología a otras instituciones financieras del país.

Caja Sullana, que actualmente usa la evaluación psicométrica de LenddoEFL tanto en agencias como online, buscaba una forma de aprobar más personas con poca información crediticia de manera más sencilla. El proceso previo de evaluación crediticia implica visitas y análisis por los oficiales de crédito que consumen tiempo, que muchas veces tienen como resultado el rechazo del préstamo. Con la evaluación de LenddoEFL, Caja Sullana puede agilizar su proceso de solicitud de crédito, reducir la carga de trabajo para los oficiales de crédito, y tomar decisiones más informadas sobre los solicitantes con poca información.

“Citi Perú está comprometido con el empoderamiento económico de las comunidades donde vivimos y trabajamos, por eso promovemos este programa que fortalece a las microempresas y promueve las micro finanzas. Este proyecto constituye una excelente iniciativa para estimular el uso de tecnologías y para promover la inclusión financiera en las comunidades más alejadas”, señaló Camila Sardi, Head de Asuntos Públicos de Citibank del Perú.

"Dos de nuestros ejes estratégicos son el apoyo a la inclusión financiera de más peruanas y peruanos, en particular de zonas rurales y peri-urbanas, y la implementación de soluciones innovadoras que mejoren la eficiencia de las instituciones de micro finanzas: En ese sentido, el proyecto ejecutado con el apoyo de Fundación Citi, se suma a las acciones que en el marco de estos dos ejes desarrollamos en el país, habiendo encontrado en Caja Sullana y LenddoEFL, dos organizaciones cuyo alcance, experiencia y objetivos facilitan la consecución del propósito de su diseño y puesta en marcha: la incorporación al sistema financiero de jóvenes emprendedores a través del empleo de una herramienta disruptiva que estamos seguros tendrá un impacto significativo." afirma Carlos Ríos Henckell, Gerente General de COPEME.

“Tenemos como objetivo atender a los segmentos más jóvenes y ofrecerles esta nueva opción para ingresar al sistema financiero, considerando su perfil como emprendedores en potencia. La falta de historial crediticio dificulta el acceso a herramientas de desarrollo, por lo que nos esforzamos en promover la inclusión financiera y ser el soporte económico que ellos  necesitan”, expresó el presidente del Directorio de Caja Sullana, Joel Siancas Ramírez.

Además, agregó “nuestro sentir como institución siempre ha sido acompañar a los ‘peruanos guerreros’ en el crecimiento de sus proyectos y ser parte importante en la historia de su éxito”.

“Trabajamos con algunas de las mayores instituciones financieras de Perú y América Latina, esta es una oportunidad única de servir a la inclusión de jóvenes emprendedores. La evaluación de LenddoEFL ofrece una forma poderosa de incluir a más personas en el sistema financiero, y estamos entusiasmados de asociarnos con COPEME, Fundación Citi y Caja Sullana para servir mejor a los jóvenes emprendedores de todo el país”, señaló Rodrigo Sanabria, Director Partner Success, América Latina, LenddoEFL.

Acerca de Citi
Citi, el banco líder global, tiene aproximadamente 200 millones de cuentas de clientes y realiza negocios en más de 160 países y jurisdicciones. En el Perú, Citi ofrece a corporaciones, gobiernos e instituciones una amplia gama de productos y servicios financieros, incluyendo servicios bancarios y de crédito, servicios bancarios corporativos y de inversión, corretaje de valores, servicios de transacción y administración patrimonial. Por información adicional, visite: www.citigroup.com 

Acerca de COPEME
Somos una organización que desarrolla actividades y provee servicios para el fortalecimiento del sector microfinanzas, el desarrollo de la Mype, y el fomento de la inclusión financiera. Trabaja en Perú desde 1991, alcanzando sus acciones a microfinancieras de todo el país, empresas privadas, organismos públicos, proveedores de fondos, inversionistas y otros actores relacionados al segmento Mype y de microfinanzas. http://www.copeme.org.pe/

Acerca de Caja Sullana
Somos la Caja Municipal de los emprendedores con norte, tenemos ya más de 30 años en el Sistema Financiero regulados por la Superintendencia Nacional de Banca y Seguros. Actuamos bajo la forma de Sociedad Anónima, con el objetivo de captar recursos y utilizarlos para brindar diferentes servicios financieros, preferentemente a las pequeñas y micro empresas, contribuyendo así al desarrollo económico en las diferentes regiones donde operamos, siempre comprometidos en ofrecer estos servicios con alto sentido de Responsabilidad y Calidad. Más información sobre nosotros o nuestros servicios: http://www.cajasullana.pe.

Acerca de LenddoEFL
Nuestra misión es proveer a mil millones de personas acceso a poderosos productos financieros a un menor costo, más rápido y conveniente. Usamos Inteligencia Artificial y Análisis Avanzado para traer las mejores fuentes de digital y psicometría para ayudar a las instituciones financieras en países en desarrollo para atender en confianza a las personas que no están bancarizadas y pequeños negocios. A la fecha, LenddoEFL ha proporcionado productos como puntajes crediticios, verificación e Insights a más de 50 instituciones financieras, ayudando a siete millones de personas e impulsando el préstamo de dos mil millones de USD. Para mayor información, visite https://include1billion.com/.

Blog | Lessons from the field: How we created new group psychometrics to increase financial inclusion in Mexico

While Jonathan takes notes, Gerardo helps an applicant navigate our psychometric assessment on a mobile device. An essential component of our field work was to get direct usability feedback from applicants as they completed new psychometric content.

While Jonathan takes notes, Gerardo helps an applicant navigate our psychometric assessment on a mobile device. An essential component of our field work was to get direct usability feedback from applicants as they completed new psychometric content.

By Jonathan Winkle, Behavioral Sciences R&D Manager, LenddoEFL

An experimental psychologist by training, I am relatively new to the world of financial technology. Since joining LenddoEFL, I have embraced terms like information asymmetry, alternative data credit scoring, and financial inclusion. Yet it was only during a recent trip to the field that I was able to meet the people behind the FinTech jargon we use in our day-to-day, the small business owners whose lives we help improve in our mission to #include1billion.

In April of this year, I traveled with colleagues to Veracruz, Mexico to test new psychometric content for one of the top 3 microfinance institutions (MFI) in the country. Their group loan product extends a line of credit to a collection of business owners, but liability for payments is joint: if one person misses a payment, the group must still make that payment in full. Since many of those applying for these loans lack traditional credit histories, this MFI asked LenddoEFL to develop psychometric exercises that could quickly and reliably assess group traits that predict creditworthiness.  

There are traits that define a strong social group which are nonexistent for individual borrowers. A successful group has strong internal relationships that ensure they will help each other in times of need. A tenacious group can generate creative ideas to solve problems that arise when life presents hardships, as it is wont to do. And a cohesive group exhibits decision making abilities that allow it to act deliberately and with confidence. We designed new psychometric exercises to measure these core traits, and tested them in the field with groups of small business owners applying for loans.

Hiding from the Veracruz heat underneath a family’s palapa, Gerardo leads a collection of applicants through our group psychometric exercises while Jonathan makes observations about their behavior.

Hiding from the Veracruz heat underneath a family’s palapa, Gerardo leads a collection of applicants through our group psychometric exercises while Jonathan makes observations about their behavior.

Measuring interpersonal relationships through social pressure
To measure the strength of a group’s interpersonal relationships, we examined the social pressure that exists among group members. Do individuals feel that they can answer sensitive questions honestly? Or do they feel pressure to conform to the opinions of the group majority? While the group was sitting together in one room, we asked them to raise their hands if they agreed with statements about the trustworthiness, fairness, and helpfulness of their local communities. We then asked individuals to answer these questions privately. The discrepancy between how the questions were answered in each setting could reveal how much social pressure exists, and thus how comfortable group members are being honest with each other. We expect that less social conformity means the group’s interpersonal relationships are stronger, an important factor for predicting whether the group will cover individuals who may miss payments throughout the loan cycle.

Measuring creativity through brainstorming
To measure a group’s creativity, we created a set of generative exercises. For both an easy and a hard problem, we had groups brainstorm as many solutions as they could in 60 seconds. The number of solutions generated was recorded as a creativity metric, and, as predicted, groups generated many fewer ideas for the harder exercise. We were also interested in the group’s dynamic as they performed these tasks. Were they apathetic or engaged? Was there a dominant member of the group? Ultimately, when a loan payment is due and some individuals are short on money, can the group come up with ideas for how to get the extra money? We hope that these generative exercises will shed light on this critical group trait.

Gerardo snags a picture with one of the applicants we met and her business, a stand selling eggs, candy, and other sundries. The small scale of some businesses we encountered, such as the one pictured above, reinforces their need for access to financial products. This woman’s entrepreneurial endeavors are only limited by the capital she can acquire.

Gerardo snags a picture with one of the applicants we met and her business, a stand selling eggs, candy, and other sundries. The small scale of some businesses we encountered, such as the one pictured above, reinforces their need for access to financial products. This woman’s entrepreneurial endeavors are only limited by the capital she can acquire.

Measuring decision making abilities through consensus
To measure a group’s decision making abilities, we created a time-to-consensus task. This exercise asks the group to solve a problem where all members must agree on the answer they provide. While we asked the groups to estimate the population of the state they live in, we actually don’t care how accurate their answer is! What’s more important in this exercise is how the group reaches consensus. Are they indifferent and accept the first estimate suggested? Or do they take their time and argue intensely while deliberating over possible solutions? What kind of strategies did they use to reach their estimate? Importantly, this task provides loan officers with a window into the group dynamic that might not otherwise be seen if the assessment merely collected static information such as sociodemographics and business revenues.

Financial inclusion is the mission of LenddoEFL, but working directly with the people we want to include allowed me to better understand how our assessments must be tailored to their cultures and experiences. The better we can measure group dynamics that predict creditworthiness, the more successfully we can extend financial services to those in need. As we continue to expand our credit scoring offerings across the world, looking past the business jargon we use and maintaining empathy for the humans we touch is essential on our path to #include1billion.

 

Blog | Score Confidence: Boosting Predictive Power

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Note: This is a new and improved version of a popular post from last year.

Our unique platform has a big reason to live: we provide fast, affordable and convenient financial products for more than 1 billion people worldwide. And there is only one way to accomplish that: by facilitating more actionable, predictive, robust and transparent information to our clients to enable them to make the best possible lending decisions. However, data quality pose the most challenging problem we have faced along this journey as it threatens the predictive power we are delivering to our clients. Therefore, through the years we have developed and perfected a one-of-its-kind way to assess the quality of the data applicants are supplying: Score Confidence.

What exactly is Score Confidence?

Score Confidence is a tailored algorithm that scans and analyzes psychometric information gathered through LenddoEFL's Credit Assessment to generate a Green or Red flag which reflects how confident we are on our score’s ability to represent an applicant’s risk profile:

  • The result will be Green if LenddoEFL is confident in the data quality such that we will generate and share a score based on it.
  • Conversely, the outcome will be Red when LenddoEFL’s confidence in the gathered information has been undermined.

What does Score Confidence measure?

Once the applicant has taken our psychometric assessment, we put the data through our Score Confidence algorithm to find out whether we can be confident in a score generated using this data or not. We will return a Green Score Confidence flag if we believe the score accurately predicts risk, and also be transparent about the reasons behind a Red Score Confidence flag to empower our partners with increased visibility and actionable information.

LenddoEFL's Score Confidence system is comprised of five Confidence Indicators of key behaviors, each generated from a combination of different data sources. If we identify evidence of any of the following behaviours, the assessment will be rated as Red and no risk score will be returned in order to protect our partners:

  • Independence – the assessment has not been completed independently, and LenddoEFL detects attempts to improve one’s responses with either the help of a third party or other supporting resources.
  • Effort – the applicant has not put forth adequate effort and attention in completing the assessment.
  • Completion – the applicant has not responded to a sufficient portion of the timed elements of the assessment.
  • Scoring error – a connection issue or system error occurred and LenddoEFL is unable to generate a score.

What information feeds Score Confidence?

Our data quality indicators are constantly reviewed and updated and, over the years, we have added new and different data sources to our Score Confidence algorithms:

  • Browser and device metadata surrounding the completion of the application
  • User interaction information with LenddoEFL’s behavioural modules
  • Self-reported demographic data

Our Score Confidence system flexibly combines all the available data in order to return a Red or Green status for each application.

How does Score Confidence help our partners make the best possible lending decisions?

To boost the predictive power we can deliver for our clients, LenddoEFL does not share a LenddoEFL score for applicants with a Red Score Confidence flag as we have learned that Red applications tend to have very limited predictive power whereas data coming from Green flagged assessments can effectively sort risk amongst applicants. Therefore, not lending against a score for Red flagged applications boosts the predictive benefit for our clients.

Blog | iDE Ghana increases access to sanitation with help of innovative credit assessment from LenddoEFL

Partnership allows Ghanaians to purchase their first toilets

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Globally, 32% of people lack access to a toilet in their homes (Source: WHO UNICEF JMP). In Ghana an astonishing 87% of people do not own a toilet. And in rural Northern Ghana, it is worse still. Two out of every five children in northern Ghana are stunted, compared to approximately 20% of children stunned nationally (Source: UNICEF).

iDE Ghana, a nonprofit that creates income and livelihood opportunities for poor rural households, wanted to improve sanitation in the region. They began by applying design thinking to understand the low rate of toilet use. It turned out that people didn’t know where to buy a toilet, and if they did, it was prohibitively expensive to buy.  People could not afford the full cost all at once, and there were no options to pay for a toilet over time, as there were for other large purchases.

"What we found was the criteria for borrowing towards non-income generating loans were ridiculous. So we set up a one stop shop for toilets and sanitation products, selling them door to door,” explained Valerie Labi, WASH Director at iDE Ghana. “And the beauty of the model is that we give our customers 6 to 18 months to pay the toilet off over time.”

This seemed like the perfect solution given the challenges to toilet purchasing uncovered, but it was still challenging. “We allowed people to pay over the course of 6 to 18 months but we required for the customer or a guarantor to prove their income with bank statements or payslips. And this was a big deterrent. No one wanted to give their bank statements to a toilet company. And it would take an average of 40 days to get through the process” Labi shared. “We realized these requirements were scaring away customers as they’d never had formal credit before. So we asked ourselves, how else could we assess creditworthiness in a more inclusive way?”

That’s when they came across LenddoEFL universal credit assessment. By collecting behavioral and psychometric data at the time of application, iDE’s commercial agents will be able to assess risk and make a decision in a day or less, cutting down the time to sale greatly. Previously, the commercial agent made multiple calls and visits to collect the required documents. By using the LenddoEFL score, iDE removes the need for a guarantor or proof of income for the best scoring customers. Low scorers will need to pay 50% of the cost of the toilet in monthly installments before receiving the toilets.

iDE’s goal is to provide 20,000 to 25,000 toilets to households in Northern Ghana. At an average of 11 people per household, this will provide life-saving sanitation for 275,000 people. And the plan is to sell toilets as part of a fast, convenient customer-driven process and at affordable rates. With the LenddoEFL assessment in place since February 2018, iDE is already receiving positive feedback for customers who enjoy the process. Stay tuned for updates on this exciting partnership.