Blog | Score Confidence: Boosting Predictive Power

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

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.

Blog | Our Commitment: Privacy, Responsibility, Choice and Control

By: Richard Eldridge, LenddoEFL CEO

Data privacy and security is a top priority at LenddoEFL and with the General Data Protection Regulation (GDPR) deadline coming up, we wanted to share our thoughts on this topic.

Our work toward a more financially inclusive future for one billion people brings with it important responsibilities, none more important than keeping customer data private and secure.

Our Responsibilities

Privacy is one component of a broader set of responsibilities we have as a global financial technology company.

1. Customer Protection and Privacy

We follow these five principles across our operations:

• Customer Data Ownership: Data we collect will always remain the property of the customer who shared their information with us and we will always safeguard the data as if it were our own. LenddoEFL uses world-class security standards in the transfer, storage, and processing of information to ensure that customer data is kept secure at all times. We never store data for longer than is necessary or authorized. Any information we permanently store is anonymized and encrypted. Where third party services are required, we only enlist the assistance of industry recognized players that adhere to the same or stricter standards than we do. In addition, security checks and penetration testing are conducted on a regular basis to ensure the security of our platform. See our full Security Policy here. 

• Consent-Driven Access: LenddoEFL only accesses data that customers share with us and all information gathered requires their explicit consent.

• Inclusive Use: Data shared with LenddoEFL is used with the sole purpose of enabling greater financial inclusion for each customer.

• Transparent Handling: Data shared with us is not--and will never be--shared without the consent of the person to whom it belongs. We will never share a customer’s data or sell it to another third party except their financial institution that is our client. Furthermore, we will only use the data for purposes the customer has agreed to.

• Unbiased Application: When building a credit model, no discriminatory variables—such as gender, race, and political or religious preferences— are taken into consideration.

For more details, read our full Privacy Policy.

2. Responsible Lending

When used properly, credit is a powerful tool for alleviating poverty, stabilizing income inequality, and empowering people to thrive. When used irresponsibly, credit can result in over-indebtedness, default, and economic instability. At LenddoEFL we are dedicated to building robust, proven models for our financial institution clients that enable safe, responsible data-driven decisions across the customer lifecycle with the goal of building a stable economy. 

3. Customer Choice and Control

Lastly, we believe in giving people options for financial inclusion, where they did not exist before. This involves using their own data to unlock access to savings, insurance products, and credit. With Europe’s second Payments Services Directive (PSD2) paving the way for open banking, people have increasing control over their data, and we know from experience that data can open doors to better, more affordable financial services. It makes sense to let each individual decide if and when to share their data. LenddoEFL’s credit scoring and verification tools are designed with this choice and control in mind. We allow customers to choose which data they want to share, if any, to get access to financial services from our clients. The more data someone grants us access to, the better we can understand them, and the better financial institutions can match them with appropriate offerings (pricing, terms, amount, etc).

Welcoming our New Behavioral Science Manager

In this photo, Jonathan demonstrates cultural differences in height during a field visit with loan applicants in Veracruz, Mexico.

Since our merger, we have welcomed a number of incredible new colleagues onto the LenddoEFL team. Jonathan Winkle joins us in our Boston office as our new Behavioral Science Manager. We cornered him to learn more.

Tell us about your background?

In undergrad I majored in psychology, where I developed a passion for researching the brain and behavior. To gain more experience after college, I worked in a systems neuroscience lab at MIT studying visual attention. Eventually I found my way to Duke where I earned my PhD in cognitive neuroscience. My dissertation focused on the behavioral economics of dietary choice, investigating how the mind is affected by “nudges” that can bias people towards healthy (or unhealthy) eating habits.

What brought you to LenddoEFL?

Studying behavior has always excited me because it is the ultimate endgame of our brains’ hard work, yet academic research on the topic can often be too disconnected from real-world problems. I found myself wanting to make more of an impact on society, and in this role I can leverage my experience to quickly and directly improve people’s lives around the world. As the Behavioral Science Manager for LenddoEFL, I can test a new hypothesis and apply that knowledge globally in a matter of weeks. And the better I do my job, the more people I can help get access to life-changing financial services.

What are your plans as Behavioral Science Manager?

My primary goal is to drive feature engineering. Features are the observations we collect about individuals to predict credit risk, and feature engineering is the process of discovering and creating new features to make our algorithms work better. For example, how honest a person is might be predictive of loan default, but we first need to quantify honesty as a feature to use it in a predictive model. As new features make our models more predictive and more powerful, our financial institution clients all over the world will gain a better understanding of their under-banked loan applicants.

If I am successful, we will be better at predicting if someone will repay their loans, thereby allowing our clients to make the best, most informed decisions possible. No pressure.

Across data sources, we look for ways to profile a person’s character, trying to understand how traits like honesty or conscientiousness relate to credit risk. This is a hard, but extremely important challenge.

LenddoEFL deals with both psychometric/behavioral and digital data sources. How do those differ and how do you think about each?

On the psychometric side, we engineer the form our data will take from the outset, then extract it by inserting new content (e.g., survey questions or psychometric games) into our simple, interactive assessment. We can be more hypothesis-driven when it comes to designing features in this realm.

On the digital side, we work with large, unstructured data sources where we necessarily have to be more exploratory and let the data do the talking.

Will you be working with our research advisors?

Absolutely! I am looking forward to working with leading researchers like Peter Belmi to push the envelope of our own research while also sharing the insights gained from our unique dataset with those in the field of behavioral economics. We will also be inviting more researchers to collaborate on our work.

Enough about work, what do you do for fun?

I like to rock climb, play Go, hang out with my dog Clementine (pic below), and try out new recipes in the kitchen.

What’s a fun fact about you?

I have a tattoo of Phineas Gage, a famous figure in the history of psychology and neuroscience. Gage was a railroad worker in 1848 that lost the left pre-frontal cortex of his brain when an accidental explosion sent a 3 foot iron rod rocketing through his head. Miraculously, he survived and was even able to walk himself to a doctor despite the 11⁄4 inch hole running behind his left cheek and out the top of his skull. He lived for 11 years after this event, but experienced marked changes in his personality that have been studied ever since. The story in itself is fascinating, and of particular interest to me is how Gage’s misfortune shaped theories of the mind for more than a century after the accident.

Look out for a future post from Jonathan about his field work in Mexico and learnings about group dynamics.

Lodex Blog | LodexSecurity, Privacy and Social Data - Insights from LenddoEFL

Social data empowers millions of people around the world through their transactions with financial services providers. We wanted to bring this technology to Australia and have teamed up with LenddoEFL to do this.

We spoke with Audrey Banares Reamon, Quality and Compliance Manager, and Howard Lince III, Director of Engineering, from LenddoEFL, and asked them some of the questions you have been asking to help give you a greater insight into the power behind Social Scoring and using non-traditional data. Enjoy.

See full interview

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

Medici | How BigTech Challenges Banks

The evolution of bank-FinTech narrative brought us to a logical point, when FinTech is no longer perceived to be a threat to traditional banking, but rather as an instrument in re-establishing their position in the financial services industry. The narrative, however, doesn’t end there. As Citi emphasized in its March 2018 Bank of the Future: The ABCs of Digital Disruption in Financereport, traditional banking is being challenged not by small FinTech startups, but by established tech giants because of:

Big data customer insights

"Social media has been recognized by Wharton as an important data source for credit scoringback in 2014, although the practice of judging a stranger based on his/her social environment is not really new. One of the core ideas is that “who you know matters.” Companies like Lenddo, FriendlyScore, and ModernLend use non-traditional data to provide credit scoring and verification along with basic financial services. Those companies are creating alternative ways to indicate creditworthiness. The information contained about a person in social networks can provide some sort of verification that the person exists at all and who that person is."

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

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.

Read full article in Malaysian Business Online.

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.

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. 

Read full article.

Markets Insider | CTOS & LenddoEFL Partner to Boost Financial Inclusion in Malaysia

KUALA LUMPUR, Malaysia, and SINGAPORE, CTOS Data Systems Sdn Bhd (CTOS), Malaysia's largest credit reporting agency, has entered into a partnership with LenddoEFL to achieve a joint vision of financial inclusion for Malaysian consumers with little to no credit history. Both fintech leaders have aided banks, lending institutions, utility and credit card companies to reduce risk, increase portfolio size, improve customer service and accurately verify applicants. Read full article.

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.