he Monetary Authority of Singapore (MAS) and The Association of Banks in Singapore (ABS) today awarded 12 FinTech companies a total of SG$1.2 million divided for 12 different companies at the Fintech Awards, which took place at the third Singapore FinTech Festival.
This time around, the awards featured a greater ASEAN representation, with a focus on financial inclusion, spanning different business areas like credit-scoring, mobile security, anti-money laundering, and digital investment. The Fintech Awards, supported by PwC, recognises innovative FinTech solutions that have been implemented by FinTech companies, financial institutions and technology companies.
This year, 40 finalists were shortlisted from more than 280 global submissions including the companies who participated in the ASEAN PitchFest6. The winners were selected by a panel of 17 judges who represent a cross-section of international and local experts from the private and public sectors. The entries were evaluated based on four criteria: impact, practicality, interoperability, and uniqueness and creativity.
The panel of judges includes representatives from Accenture Technology, Allianz, AMTD Group, Credit Ease, DBS, Deloitte, GIC, Grammen Foundation India, HSBC, Insignia Venture Partners, Jungle Ventures, Mastercard, The Boston Consulting Group, The Disruptive Group, True Global Ventures, UOB and Vertex Ventures.
ASEAN Open Award Top 3
First Place: LenddoEFL (Philippines)
The company wants to provide people access to powerful financial products without exorbitant costs, quickly and more conveniently by using AI and advanced analytics to bring together digital and behavioural data. This helps lenders serve the underbanked. LenddoEFL has provided credit scoring, verification and insights to 50+ financial institutions, serving over 7 million people.
Posted on APAC CIO Outlook website. Refer to this link to read full article.
by Howard Lince III, Director of Enginerring, LenddoEFL
At LenddoEFL, we work at the intersection of big data, machine learning, and financial inclusion in emerging markets. Each of these imply a level of server sophistication that would be cripplingly difficult without Amazon Web Services (AWS). Our 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 behavioral data to help lenders in emerging markets confidently serve underbanked people and small businesses. To date, we have provided credit scoring, verification and insights products to 50+ financial institutions, serving seven million people. We’ve been able to manage all of this with a team of three infrastructure engineers managing 300+ servers. Read full article.
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:
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).
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.
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).
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.
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.
Written by Rodrigo Sanabria, Director Partner Success, Latin America
On a prior post by Carlos del Carpio (“The Economics of Credit Scoring”), we discussed the business considerations to assess the merit of a risk model. In this post, I will address how a good origination model impacts the bottom line of a company’s P&L.
These principles may be adapted to look into other types of models used at later stages of a loan life, but on this post we will only address loan origination.
From a business point of view, an origination model is a tool that helps us aim at the “sweet spot”: where we maximize profits. A simple way to think about it is as a trade-off between the cost of acquisition (per loan disbursed) and cost of defaults (provisions, write-offs): The higher the approval rate, the lower the cost of acquisition, but the number of defaults go up.
How do we go about finding the sweet spot? I’ll try to explain it below.
A good model has a good Gini. A “USEFUL” model creates a steep probability of default (also known as PD) curve – we usually refer to it as a “risk split”.
Figure 1 shows the performance of a model based on psychometric information used by an MFI. The Gini (not shown in the graphic) is pretty good (0.28). The risk split is great: the people in the lower 20% of the score ranking are about 9 times more likely to default than those in the top 20%.
Knowing the probability of default for a given group, we may set a credit policy. Basically, we need to answer: “what would the default look like given an acceptance rate?”
We have re-plotted the same data in Figure 2, but now we express the probability of default in accumulated terms. Basically, the graph shows that if we were to accept 80% of this population sample, we would have a 4.5% PD, but if we were to accept 40%, the PD would go down 2 points to 2.5%.
Now, from a business point of view, we still do not have enough information to decide. Do we?
Where would the profit be maximized?
The total cost of customer acquisition is mainly fixed. Whatever we spend on marketing and sales to attract this population, will not change if we reject more or fewer applicants. So, the cost per loan disbursed would grow as we reduce the acceptance rate.
Of course, the higher the acceptance rate, the larger the portfolio, and the more interest revenue we get. BUT, the higher the provisions and write-offs. The combination of these 2 variables (cost of acquisition and net interest income) produces an inverted U-shaped curve that uncovers the “sweet spot”
The current credit policy is yielding a profit at 100% acceptance rate (see Figure 3) because the sample being analyzed corresponds to all the customers that were accepted (i.e. we have repayment data about them). So, the portfolio is profitable.
But the sweet spot seems to be shy of 60% acceptance rate. If this FI were to cut down its approval rate to that level, profits would increase by about a third, and its return on portfolio value would almost double. Of course, there are other considerations around market share and capital adequacy that may play a role in such a strategic decision, but the opportunity is clearly uncovered by the model.
In my experience, the sweet spot usually lies within 30%-70% acceptance rates, driven by marketing expenditures, interest rates, cost of capital, sales channels, and regulation.
What if the shape of the curve shows a continuous positive growth? The sweet spot is at a 100% acceptance rate! – have we reached risk karma? – Most likely, the answer is no (but almost!).
Most likely, we are leaving money on the table. Some business rule may be filtering people before they are scored. I have experienced this situation while working with lenders. For example, a traditional bank was filtering out all SMEs that had been operating for less than X years. This bias in the population was creating a great portfolio from a PD point of view, but there was clearly an opportunity to include younger businesses. As you can see in Figure 4, the maximum return on the portfolio was achieved at 60% approval rate, but they could increase profits by approving beyond the current acceptance rate. Depending on their cost of capital, it may be a good idea to expand the portfolio by approving more people.
In summary, think of your origination model as a business tool. Don’t stop at looking at Gini to assess a model’s merit. Understand how your profitability would be impacted by changes in your acceptance rate. If the PD curve is steep enough, you may capture quite a lot of value by applying the model to either reduce or increase your acceptance rate.
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.
We started LenddoEFL to solve the problem of access to credit in emerging markets, where people find themselves unable to get a loan, and unable to build their credit. This excludes good people from financial services, limiting opportunity for individual livelihoods and economic growth.
We realized that even though people may have limited financial data in a credit bureau, they have plenty of unique data that can be accessed to better understand who they are. For example, we found that analyzing the digital footprint of an individual (with full consent) helps us to get to know them and understand certain traits that relate to creditworthiness and credit risk.
Now, we are working with banks and lenders across 20+ countries to use non-traditional forms data - digital footprint, mobile behavior and psychometric to predict risk, and unlock access.
When we think about financial inclusion, there are really 3 levels, each necessary to get to the next one.
Access comes first: Can you get a credit card or open a savings account? 1.7 billion adults around the world lack an account at a financial institution according to the 2017 Global Findex. Enabling these people to take that first step towards opportunity is foundational.
Price: Often where access is scarce, the first loan can come from a payday lender or other institution at an unbearably high price/interest rate. So the next step to financial inclusion is bringing the price of a loan down to reasonable rates even without historical credit data.
Convenience: Once you have access to credit at a fair price, the third step to financial inclusion is making it convenient to get. Historically, inclusive lending such as microfinance could involve arduous, time consuming processes with multiple in-person visits and copious document collection. We want to make borrowing easier and faster for people while maintaining safety. The beauty of moving from analog loan officer-based processes to machine learning and big data-driven processes like ours is that it becomes faster and easier.
We believe that financial inclusion isn't simply about access to financial products, but about access to fast, affordable, and convenient financial products. Join us on our mission to #Include1Billion people around the world. We are hiring!
Credit is hugely important to people around the globe. You need it to obtain housing and higher education. You need it to start a business. You need it in case of emergencies and other unexpected expenses.
But in emerging economies, credit may not be accessible to many people. According to the World Bank’s 2017 Global Findex, 31% of the world’s population doesn’t have an account with a financial institution or a mobile money provider.
“We still have 1.7 billion people on the planet who don’t even have a basic bank account,” said Amie Vaccaro, Director of Marketing at LenddoEFL. “Only 11% of people around the world borrowed from a formal financial institution in the 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.
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.
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.
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.
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).
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…
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.
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?
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.
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.
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.
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."
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
“Having worked across geographies and being well-versed with the problem of credit coverage, I look forward to leveraging my experiences to work on the challenge of financial inclusion in India. The need is massive with less than 45% of Indian adults included in the credit bureau and less than 10% borrowing from a financial institution in the last year, as per the World Bank.” said Darshan Shah.