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:

Which BLUE person is most like you?

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:

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

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 | On the use (and misuse) of Gini Coefficients in Credit Scoring: Gini and Acceptance Rate

This is part 3 in a series of blog posts about Ginis in Credit Scoring. Read part 1 and part 2.

The relationship between Gini Coefficients and Acceptance Rate

One of the most frequent uses of Credit Scores is to decide whether to admit or reject an applicant applying for loan. This is usually called an “Admission score” or “Origination score”. A key decision around this use case is the selection of a score cut-off that will determine a threshold for admission. This cut-off value determines the acceptance rate of the population.

If the score is working well and predictive power is good, the relationship between acceptance rate and default rate will be positive. The higher the acceptance rate, the higher the default rate of the accepted population and vice versa. The direction of this relationship also has two implications: when acceptance rate is higher, the absolute number of bad loans (i.e. non-performing loans) or “bads” will also be higher, and the proportion of these “bads” in respect to the total loans in the accepted population will be higher too.

What does this mean in practical terms?

It means that the predictive power as measured by a Gini coefficient for the exact same score at different levels of acceptance rate for the exact same population will be different. The higher the acceptance rate, the higher the Gini coefficient and vice versa.

This is something that can be easily tested. If you have a portfolio and a score with good predictive power, you can calculate the Gini coefficient for different score cutoffs or acceptance thresholds and the results should look something similar to this example of a typical credit portfolio:

So for example, if there is a change in credit policy and the acceptance rate is lowered from 60% to 40%, the Gini coefficient for the same score over the new sample may also be lower. Does that mean the model is not working anymore? Absolutely not. All the contrary, it’s probably just a good signal that the score is doing a good job. Once a change in acceptance rate is implemented, results should be assessed by the change in default rate, not in predictive power.

Bottom-line:  To judge the predictive power of a Credit Score by the means of Gini, you also need to take into account the Acceptance Rate at which the Gini coefficient is measured. Lower Acceptance Rates will tend to have lower Gini coefficients by construction, even if it is the same exact score over the same population.

The fundamental reason behind this phenomenon was discussed in the part 2, where we explained why Gini coefficients should only be directly compared over the exact same data samples, even if the two samples correspond to the same population.

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By: Carlos Del Carpio, Director of Risk and Analytics, LenddoEFL

Financial Services Veteran, Ray Ferguson, Joins LenddoEFL’s Board

Ferguson’s wealth of experience across banking, insurance and venture capital in Asia makes the appointment a strategic addition for the growing fintech company

LenddoEFL, whose credit score, verification and consumer insights help leading banks across emerging markets make data-driven decisions, is proud to welcome financial services industry veteran, Ray Ferguson, as a new member of its board. Ferguson is co-founder and Managing Partner of Caber Partners, former Regional CEO, SE Asia at Standard Chartered Bank, and currently chairs the boards of digital life insurance company, Singapore Life, and e-money software platform, Youtap. 

Read complete press release.

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

Partnership allows Ghanaians to purchase their first toilets

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.

Sina News Taiwan | How to break the credit assessment problem? (如何破解信貸評估難題?)

Bangladeshi banker and Nobel laureate Muhammad Yunus (Muhammad Yunus) the promotion of microfinance , is the poor through microcredit loans , so there is money to do a small business to support themselves, and thus get rid of poverty. However, due to the time-consuming and laborious credit evaluation of lenders, the large-scale application of microfinance is difficult to achieve once.

Nowadays, mobile banking comes. It can collect data to help people who have little formal financial records in the traditional sense to broaden their services. Labor costs are also greatly reduced. For example, Kenyan mobile telecommunications operator Safaricom and African Commercial Bank jointly launched the M-Shwari business in 2012, which can determine customers’ credit scores based on Safaricom’s user information and the trading history of its M-PESA mobile money business. Loan amount.

In addition to payment data, mobile phones (especially smart phones) can also provide more types of information for credit evaluation by borrowers . For example, a person's geographic location data can reflect whether he has a stable job and fixed residence; shopping records can even reveal whether the borrower is pregnant ; and the richness of information obtained by social media is not Yu.

The fintech start-up company Lenddo EFL also uses the Internet to conduct psychological tests on potential borrowers. The question concerns the concept of money (for example, choosing to pay $10,000 at a time, or $20,000 for six months), where your money is spent. , Evaluation of living communities, etc., to determine the reliability of testers loan repayment. To date, the company has completed more than 7 million credit assessments, helping consumers with a lack of traditional credit records to borrow 2 billion U.S. dollars from 50 financial institutions of varying sizes.

詳全文 如何破解信貸評估難題？-財經新聞-新浪新聞中心 http://news.sina.com.tw/article/20180514/26854022.html

Spore Magazine | Réduire les risques : Des systèmes innovants d’évaluation du crédit pour aider les agriculteurs

La difficulté d’emprunter, pour de nombreux petits agriculteurs ne disposant ni de garanties ni d’antécédents de crédit, a fait apparaître de nouveaux systèmes pilotes d’évaluation du crédit pour aider les banques à apprécier les risques que présentent réellement les emprunteurs et tirer parti de ce secteur potentiellement lucratif.

L’évaluation psychométrique

Pour augmenter les taux d’acceptation et réduire les délais de traitement des prêts aux agriculteurs, Juhudi Kilimo, prestataire de solutions financières pour les petits agriculteurs d’Afrique de l’Est, teste la méthode d’EFL Global, une entreprise privée qui utilise l’évaluation psychométrique pour créer les profils de risque d’emprunteurs africains, asiatiques, européens et latino-américains. Cette méthode pilote – financée par la Fondation Mastercard – mobilise les représentants de six agences kényanes de Juhudi qui visitent et incitent les demandeurs de prêts à passer des tests psychométriques sur tablette. Ces tests permettent, selon EFL, de définir leur personnalité, y compris leur self-control en matière de dépenses et budgétisation. Sur cette base, une cote de crédit à trois caractères est alors attribuée aux demandeurs. À partir de son évaluation initiale d’environ 6 000 clients réalisée à l’aide de l’outil d’EFL, Juhudi a constaté que 6 % des personnes classées dans le quintile le plus bas avaient au moins une fois des arriérés de remboursement de 60 jours pour un prêt type d’un an, contre 1,5 % dans le quintile le mieux noté.

Read full article.

Medici | What Happens at the Convergence of Machine Intelligence and Online Lending

Credit scoring and approval rates changed substantially with the arrival of alternative lenders, mainly due to the adoption of new practices in collecting and analyzing potential borrower data. Alternative data has played its role in expanding horizons for financial institutions and for creating an opportunity to enter the financial sector fir technology startups and data-rich international companies.

While social media, for example, as a source of data for creditworthiness assessment is still at a nascent stage, certain startups are already claiming to have incorporated information from social networks into their frameworks. In the quest to reinvent the way to assess consumer-related risk (as well as extend credit to unscored and questionable), startups were found more imaginative than traditional institutions.

Alternative data requires alternative approach to data analytics, which wide adoption of machine learning and artificial intelligence brought.

Read full article

Microfinance Gateway | Malaysia: Fintech Heavyweight CTOS Expands Services for A Better Financial Inclusion

CTOS has been Malaysia’s largest in terms of credit reporting, just announced a partnership with LenddoEFL to achieve a joint vision of financial inclusion for the people who had difficulties securing loans in Malaysia due to the lack of credit history. 

Read article in MicroFinance Gateway website: https://www.microfinancegateway.org/announcement/malaysia-fintech-heavyweight-ctos-expands-services-better-financial-inclusion

AstroWani | CTOS, LenddoEFL extends financial inclusion in Malaysia

30% of Malaysians with good potential is still denied access to loans. This is because they lack or directly have no credit history. In order to curb this issue, Malaysia's Largest Credit Reporting agency, CTOS Data Systems Limited, partnered with Fintech LenddoEFL company and emerged with a new solution.

Finance Digital Africa | Can big data shape financial services in East Africa?

Psychometric big data—including online quizzes to judge character or personality traits and analysis of Facebook “likes”—is garnering increased attention. Suppliers of psychometric data or psychometric tools, such as EFL, believe not only that their data and analytics are predictive but also that they have a key advantage in their applicability to everyone, even clients with limited credit history (“thin-file” clients), as a starting point. When layered with other big and traditional data sources (e.g., social media, mobile phone, bureau data, bank historical data), proponents expect psychometrics to become even more powerful. Indeed, Equity Bank conducted an experiment with EFL’s psychometric scoring model and found it both predictive and useful; they plan to integrate it into applicable models across their regional subsidiaries.

 Moreover, Juhudi Kilimo decided to partner with EFL in order to evaluate character as part of their risk assessment. This was previously carried out by loan officers, but they believed the EFL approach would be more objective.

Read full article.

World Bank | Using a PhD in development economics outside of academia: interviews with Alan de Brauw and Bailey Klinger

Today's interviews are with Alan de Brauw, a Senior Research Fellow in the Markets, Trade, and Institutions Division at the International Food Policy Research Institute; and Bailey Klinger, the founder and (until recently) CEO of the Entrepreneurial Finance Lab

Read full interview with Bailey Klinger.

Economic Times India | LenddoEFL appoints Darshan Shah as Managing Director, India & South Asia

KOLKATA: Singapore-headquartered fintech company LenddoEFL has appointed Darshan Shah as managing director, India and South Asia, effective April 16. 

In his new role, Shah will be responsible for growing LenddoEFL’s footprint in India and South Asia as well as bring more financial institutions in the region on board as clients who would be using LenddoEFL services. 

Shah comes with close to two decades of experience in the credit information industry. He has worked with large organisations like TransUnion CIBIL and Equifax (Canada) in leadership roles. His last role was as director (credit services) at Experian. 
 

Read more at:
//economictimes.indiatimes.com/articleshow/63691887.cms?utm_source=contentofinterest&utm_medium=text&utm_campaign=cppst

Media Telecom | Orange Bank comienza a ofrecer micropréstamos personales

Micropréstamos: un negocio en aumento

La posibilidad de ofrecer micropréstamos a los usuarios tienta cada vez más a la industria. No solo a la banca digital. El año pasado, Telefónica de España presentó Movistar Money. Se trata un servicio de préstamos al consumo. Asimismo, una de sus principales características es que son preconcedidos a los clientes de la operadora.

En Latinoamérica esta tendencia es todavía más importante. Así, en México, Lenddo y Entrepreneurial Finance Lab (EFL) se fusionaron para brindar productos financieros para el sector no bancarizado. Read full article.

QQ | Paolo Montessori, LenddoEFL COO, featured in an interview with Tsinghua Financial Review

Watch full video here.

The Edge Markets | Cover Story: Scoring with big data

"The exponential rise in the use of smartphones, mobile wallets and e-payment systems has given birth to a new technology that uses big data to determine credit scores. The technology has been lauded for helping the underbanked gain access to credit, representing the first step towards financial inclusion.

The use of non-traditional data to churn out credit scores is now expanding beyond the underbanked and unbanked to reach even well-banked individuals who already have a credit score. This pool of data, which is used to discover patterns of users’ repayment behaviour based on their mobile phone and social media usage, is playing an increasingly important role in Asia alongside traditional credit scores." Read the full article.

The ASEAN Post | The potential of big data for microfinancing in Southeast Asia

"Microfinance is described by the Financial Times Lexicon as a service where financial institutions will back small start-ups and would-be entrepreneurs with small loans, in the poorest parts of the world. In Southeast Asia, the biggest microfinance players currently include Asia Pacific-based LenddoEFL, Singapore's CredoLab and the Philippines’ Lendr, for example..." Read full article.

Markets and Fintech | El Big Data en la evaluaćión del riesgo de crédit

LenddoEFL, fundado por varios profesionales de perfil tecnológico en 2011, nacía con la misión de mejorar el acceso bancario a la emergente clase media de los países en vías de desarrollo. Con este objetivo en mente se acercó a las principales entidades financieras de Estados Unidos con la idea de estudiar los datos que éstas tenían sobre su población objetivo y poder elaborar un algoritmo de credit scoring alternativo. Tras la negativa de los bancos decidió emprender el viaje en solitario. 
Siete años después, Lenddo parece haber dado con algo parecido a la receta de la tarta de frutas perfecta. Analizando multitud de variables, desde el comportamiento en redes sociales, hábitos de comercio electrónico o la velocidad a la hora de rellenar los formularios de solicitud afirma reducir la mora en un 12%, aumentando las aprobaciones en un 15% y ser capaz de realizar una evaluación en menos de tres minutos. Read full article.

Quienopina | Las redes sociales factor determinante para que le aprueben un crédito

Sí, leyó bien las redes sociales. De acuerdo a su interacción y lo que hace en ellas diferentes empresas y hasta entidades financieras pueden determinar si le aceptan o no un préstamo. Oscar Torres, director de Lenddo para América Latina, empresa que analiza la información para determinar si una persona podría pagar o no, explica que lo que ellos hacen es usar  la información de redes sociales, de dispositivos móviles o de la personalidad de la gente, para decirle a la entidad en menos de un minuto si es viable o no aceptar los préstamos.

Lenddo a diferencia de Lineru, trabaja directamente con los bancos, en aplicaciones como la de Nequi de Bancolombia,  con el fin de generarles a estas una calificación del cliente, tomando los datos de la central de riesgo y haciendo un análisis conjunto con las herramientas no tradicionales. Read Full Article

Lodex Blog | The Future of Data-Driven Financial Inclusion Posted by Aisha Hillary-Morgan

"In Australia, millions of people find themselves in a chicken-or-egg-type dilemma when it comes to getting credit. Even though they have steady income, they still can’t access credit because of lack a formal credit history. Yet, most of these consumers carry a smartphone, are online and connected through social networks, leaving behind a digital footprint that can be analyzed to better understand who they are and their attitudes toward credit.

This is why we have teamed up with LenddoEFL, the leading technology platform powering data driven decisions for financial services, to help them create more of a credit story. Your Social Score will use, with your consent, your digital footprint to provide additional insights for borrowers and for lenders to more efficiently make a preliminary assessment." Read the full article

Originally posted by our partner Lodex