Alternative data

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

How banks can benefit from collaborating with FinTechs

By Satoko Omata | 10 July, 2018

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TODAY, customers expect more from their banks – who are slow to deliver new products, services, and experiences as a result of their legacy systems and archaic processes.

However, those that truly want to meet and exceed expectations (and snatch up a bigger share of the market), there are a few lessons they can learn from fintechs.

By partnering with fintechs, banks would have access to new services that help deliver better offerings to customers, at cheaper rates.

At the Wild Digital conference on Wednesday, panelists at a discussion observed that of all the industries, those dealing with money-based investments have been the least changed by technology.

The panel featured Richard Eldridge, Co-founder and CEO of Lenddo EFL; Ashley Koh, Senior Vice President and General Manager of Send, Matchmove; Michele Ferrario, Co-Founder and CEO of StashAway; and Gan Pooi Chan (PC), Country Director GoBear. 

Read full article on Tech Wire Asia.

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

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Over years of blogging, one of our most popular ever blog posts was about the Gini coefficient. In this series of posts, we revisit the Gini and dig further into its uses and the ways we see it misused in credit scoring.

What is a GINI?

For lenders around the world, the “Gini Coefficient” is an often heard, sometimes feared, and frequently misunderstood statistical measure. Commonly used to assess things like wealth inequality, Gini Coefficients are also used to evaluate the predictive power of credit scoring models. In other words, a Gini Coefficient can help measure how good a credit score is at predicting who will repay and who will default on a loan: the better a credit score, the better it should be at giving lower scores to riskier applicants, and higher scores to safer applicants.

Though calculating a Gini Coefficient is complex, understanding it is fairly simple:

A Gini Coefficient is merely a scale of predictive power from 0 to 1, and a higher Gini means more predictive power.

However, there are a few key aspects of Gini Coefficients that are not always well understood and can lead to their misuse and wrong interpretation. Over this series of blog posts we’ll discuss four of them:

  1. People often compare Ginis when they should not. The only useful comparison across Ginis (or AUCs) is when looking at different scores over the exact same data. 

  2. People forget that Gini will vary by acceptance rate. When presented with a Gini coefficient, always keep an eye on the effect of the acceptance rate.

  3. People focus on Ginis, but are not always aware of its impact on the costs, benefits and overall economics of Credit Scoring.

  4. People do not fully understand and often overestimate the role of Gini in the business of lending.

 

About the Author:

Carlos del Carpio is Director of Risk & Analytics at LenddoEFL. He has 10+ years of experience developing credit scoring models and implementing end-to-end credit risk solutions for Banks, Retailers, and Microfinance Institutions across 27+ countries in Latin America, Asia and Africa.

About LenddoEFL

LenddoEFL’s mission is to provide one billion people access to powerful financial products at a lower cost, faster and more conveniently. We use AI and advanced analytics to bring together the best sources of digital and behavioural data to help lenders in emerging markets make data-driven decisions and confidently serve underbanked people and small businesses. To date, LenddoEFL has provided credit scoring, verification and insights products to over 50 financial institutions, serving seven million people and lending two billion USD. For inquiries about our products or services please contact us here.

Blog | Digital Identities: Learnings from GSMA’s User Research in Sri Lanka

Smallholder farmers in Sri Lanka interviewed as part of the digital identity research

Smallholder farmers in Sri Lanka interviewed as part of the digital identity research

Recently, our friends at GSMA’s Digital Identity Programme and Copasetic Research set off to research digital identities and how they could support smallholder farmers in Sri Lanka. Their key hypothesis was:

“If MNOs, financial institutions, government and other service providers had access to a smallholder farmer’s ‘economic identity’ (income, transactional histories, credit worthiness, rights to/ownership of land, geolocation, farm size, and other vital credentials), they could provide access to more and better tailored services that enhance their productivity.”

In speaking with 40 smallholder farmers in Sri Lanka, as well as 7 stakeholders and 5 agri experts, GSMA learned a lot about the need for digital identities. GSMA invited LenddoEFL’s input in advance of the field research so we were keen to review the learnings. Below are some of the key findings of the report and how it relates to our work at LenddoEFL.  If this is interesting, we recommend reviewing the full report.

Source: Digital Identity for Smallholder Farmers: Insights from Sri Lanka

Source: Digital Identity for Smallholder Farmers: Insights from Sri Lanka

Identity is valued, but farmers are unclear how it relates to additional benefits
In Sri Lanka, the government is rolling out a new smart ID card giving increasing access to official identity. But farmers do not immediately understand how new forms of identity can be used to help them get access to more services (e.g. more tailored information services). Once they make the connection, they see the value clearly.

Identity is valued as it relates to accessing credit
Farmers and banks do not connect directly in many cases and farmers tend to have informal manners of connecting to credit through their buyers and agribusinesses. Banks don’t always have the information they need to cater to farmers. And the microcredit model can be more of a burden on the farmers than it’s worth. The research found that smallholder farmers are happy for their trusted service providers to work together and share information to enable access to credit. But since many farmers receive their income informally, the thought of sharing this information too widely (particularly with the government) caused some concerns.

Source: Digital Identity for Smallholder Farmers: Insights from Sri Lanka

Source: Digital Identity for Smallholder Farmers: Insights from Sri Lanka

Digital ID must build on face to face relationships
In Sri Lanka, farmers rely on and trust institutions with whom they have built local, personal, face to face relationships and these will be the best channels to roll out new systems and technology.

Farming is changing
Climate change and globalization mean that the work of a farmer is changing. Traditional farming skills are no longer enough. Farmers need to be constantly re-considering which crops they will grow now and in the future due to changing weather conditions and fluctuations in profitability. Younger farmers in particular are looking outside of their communities to the internet for new information. This new information needs to be combined with better access to financial services, allowing farmers to finance the transition to new crops, and hedge some of the risks in experimenting with new approaches.

ID needs vary across farmer types
The research found that a farmer’s financial stability and the extent to which they are embracing change (i.e. changes to farming practices, or the use of new technologies) have the most significant influence on their digital identity needs and priorities. GSMA mapped farmers across a 2 by 2 with the axes of poorer → wealthier and embracing change → stuck/fearful of change. In each quadrant is a unique farmer with unique needs. See report for more.

All of this means there is opportunity to better serve farmers (and other small business owners).

Farmers need better access to formal financial services:
Digital financial profiles could allow farmers to access savings, credit or insurance more conveniently and cheaply. Note that farmers were concerned about sharing their income information with a lender for fear it would get to the government and increase taxation or reduce welfare support. Credit scoring using psychometric data could be a good fit for farmers as it relies on personality profile data created at the time of assessment rather than existing financial data.

Read the full report

Contact us for more info on LenddoEFL’s credit assessment

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.

NewsWav | CTOS & LenddoEFL partner to boost financial inclusion in Malaysia

KUALA LUMPUR: CTOS Data Systems Sdn Bhd (CTOS), Malaysia’s largest credit reporting agency, has entered into a partnership with LenddoEFL to enable access to financing for Malaysian consumers with little to no credit history.

Both CTOS and LenddoEFL have aided banks, lending instit…

Read more in NewsWav

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

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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

Benzinga | Here Are The Benzinga Global Fintech Award Finalists For The Best Under-Banked Or Emerging Market Solution

The finalists for the Best Under-banked or Emerging Market Solution category are:

LenddoEFL
CEO: Richard Eldridge
Description: LenddoEFL's mission is to provide 1 billion people access to powerful financial products at a lower cost, faster and more conveniently.

See full list of finalists

Forbes | Could Personality Tests One Day Replace Credit Scores?

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If someone gave you an unexpected $100, what would you do with it? Give it to charity? Save it? Splurge on something fun?

We see questions like this in personality quizzes online, and sometimes even when applying for jobs. Your answers are supposed to help others predict your behavior using what’s called psychometrics.

And companies looking to avoid hiring potential problem employees aren’t the only institutions interested in psychometrics. The financial industry might get in on it, too.

What if, instead of a lender checking your credit score, they gave you a personality test?

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

Malaysian Business Online | CTOS and LenddoEFL partner up to boost Financial Inclusion in Malaysia

CTOS Data Systems Sdn Bhd, Malaysia’s largest credit reporting agency, has entered into a partnership with LenddoEFL.

CTOS Data Systems Sdn Bhd, Malaysia’s largest credit reporting agency, has entered into a partnership with LenddoEFL.

Karangkraf | Beri peluang rakyat akses perkhidmatan kewangan

AGENSI pelaporan kredit terbesar Malaysia, CTOS Data Systems Sdn Bhd (CTOS), menjalin kerjasama dengan LenddoEFL bagi memperluaskan perangkuman kewangan pengguna Malaysia yang kurang atau tidak mempunyai sejarah kredit melalui ‘CTOS Non-Traditional Data Score’.

Ketua Pegawai Eksekutif Kumpulan CTOS Holdings Sdn Bhd, Dennis Martin berkata, walaupun markah kredit ramalan tentang tingkah laku pembayaran telah meningkat tahun demi tahun, namun sekumpulan besar peminjam yang berpotensi baik ketika ini dinafikan akses kepada kredit disebabkan kurangnya sejarah kredit.

“Disebabkan pemberian pinjaman pengguna lazimnya bergantung kepada skor kredit, individu ini mendapati diri mereka terpinggir daripada ekosistem kredit dan juga sukar menambah baik markah kredit mereka.

“Dengan memanfaatkan sepenuhnya data tingkah laku dan data digital yang diizinkan penggunaannya oleh pengguna, CTOS dan  LenddoEFL akan melancarkan platform keputusan kredit universal yang mampu menaksir kebolehpercayaan kredit mana-mana rakyat Malaysia, baik yang ada sejarah kredit mahupun kurang sejarah kredit,” katanya dalam kenyataan media.

Menurut Dennis, kini banyak individu yang dahulunya kurang dilayan oleh institusi kredit atas alasan risiko kredit tradisional mereka, akan menikmati peluang untuk akses kredit. 

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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. 
 

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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.

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.