Include1Billion

Blog | How mobile data improve client engagement 

Written by: Lucrecia Lopez, Data Scientist and Oscar Pobre, Risk & Analytics Director

robin-worrall-FPt10LXK0cg-unsplash.jpg


For most people, the smartphone is an essential part of daily life. We carry it around wherever we go, and we spend an inordinate amount of time interacting with it throughout the day. As such, it’s no surprise that the smartphone reveals quite a lot about us. Traits associated with your social network, your communication habits, and technology use are all captured by the device.

In fact, smartphone data has, by now, established itself as one of the most effective data sources for credit scoring. This has been especially valuable for the so-called thin-file segment, where applicants have little or no credit history nor other reliable sources of financial information. How you use your smartphone can now help you get a loan or credit card.

However, as useful as smartphone data has been to the credit industry, there are many other use cases for this data source. In this article, we will explore how smartphone data was used to predict an individual’s need for health insurance. The following data was obtained through an engagement with a large insurer in Southeast Asia, who wanted to determine their mobile app users that would be responsive to a health insurance offer.

Let’s now see theory in action!

 

Your phone contacts shows your organizational skills.

How contacts are labeled on a smartphone can be quite telling of your personality. When a new contact is added, there are many details you can fill-in. At a minimum, you have to complete the contact’s name and phone number. However, you can also add a number of other details, such as their email, company, address, and birthday. Having more than just names and phone numbers on your contact list indicate a higher degree of perfectionism and organization. Those traits are represented by those with a high level of awareness and attention, who want to have order and control over all the events of their lives. They plan for their future. That means that they are the ideal customer to offer an insurance product which allow them to minimize potential risks.

The chart below shows the percentage of population split by the percentage of completed contact information that they have in their phones and each group propensity  to acquire an insurance product. If it is considered that population with less than 30% of their contacts information completed as the group with lowest probability to buy, it is possible to affirm that people who complete more than 50% of their contacts’ details are more than 1.5 times likely to buy an insurance product compared to those who belong to the first group.


Your phone calendar determines your daily schedule and priorities.

How you use your smartphone calendar is another good source of insight. For example, we can see how much time you spend in meetings versus how much time you spend in social events. The habit of scheduling upcoming activities is also an indicator of how organized you are and how well you plan. We have seen that people with these traits, as measured by calendar behavior, are in fact more likely to acquire an insurance product. This is most likely driven by their focus on planning for expected (and unexpected) events.

In the chart below, people were grouped according to the number of calendar events they scheduled.  The chart shows that there is a correlation between an individual’s propensity to buy an insurance product and the number of entries in his/ her phone calendar.

 

Your mobile apps show personal interests.

Another interesting data category relates to the types of apps that you have installed on your smartphone. This is particularly insightful since your apps directly correspond to your hobbies, tastes, interests, etc. People who are keen on games usually have a lot of gaming apps installed. People who are interested in finance have apps related to banking, investments, and even blockchain. If someone has many apps related to sports, health, and healthy lifestyle, that person is likely to be someone who takes good care of himself and is a good prospect for an insurance product.

Going back to our insurance use case, the plot below shows that people with health apps installed are 30% more likely to respond to the insurance offer compared to someone without health apps.

Statistics is the data not your personal information.

We should clarify that companies that use smartphone data are just interested in statistics and the insights you can infer from them. They are not interested in knowing the phone numbers of your family and friends nor the details of your mailing address. The focus is on statistics, predictions, and associations, as they are generated by complex machine learning algorithms. 

As a final note, mobile data should be used as a tool to reach more individuals in need of financial services while further enriching insights on clients, to be able to provide the appropriate products. Financial inclusion is lagging behind digital inclusion, where 1.7 billion individuals and SMEs are still unbanked while registered unique mobile subscribers is already at 5.1 billion. LenddoEFL has been working with mobile data as basis of scoring and predictive analytics for ten years. We have proven and deployed multiple models that help financial institutions with their credit and financial decisioning, at the same time allowing thin-file clients to use their mobile data to access life improving financial services.

Reference:

https://cybersecurityventures.com/how-many-internet-users-will-the-world-have-in-2022-and-in-2030/

https://www.statista.com/statistics/570389/philippines-mobile-phone-user-penetration/

https://www.gsma.com/r/mobileeconomy/

FintechNews.SG | 12 Companies Score SG$1.2 Mil at The Singapore Fintech Awards 2018

The 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.

singapore-fintech-festival-2018.jpg

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.

To continue reading, click here.

APAC CIO Outlook | 8 AWS Do's and Don'ts Learned from 8 Years Scaling Across 20 Countries and 300 Serviers

Posted on APAC CIO Outlook website. Refer to this link to read full article.

by Howard Lince III, Director of Enginerring, LenddoEFL

Howard v2.jpg

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.

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

By: Jonathan Winkle, Manager of Behavioral Sciences, LenddoEFL

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

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

Psychometrics for credit assessment

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

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

Psychometric example content: Financial Impulsivity

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

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

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

image11.png

Psychometric example content: Locus of Control

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

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

Conclusion

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

Press Release | LenddoEFL Launches eKYC Solution to Speed Up Verification in the Philippines

zany-jadraque-373625-unsplash.jpg

Aligned with Philippine banking regulation, technology-aided verification from LenddoEFL can help more people get faster, more convenient access to financial services.

MANILA, PHILIPPINES (PRWEB) AUGUST 28, 2018

LenddoEFL, whose verification, credit scoring, and consumer insights helps leading banks make data-driven decisions, launched an electronic Know Your Customer (eKYC) product for customers applying for credit card and bank accounts at Philippine financial institutions consistent with Bangko Sentral ng Pilipinas (BSP) regulation.

To date, Know Your Customer (KYC) regulations in the Philippines have always required a face-to-face or real-time online interview to onboard new-to-card or new-to-bank current account/savings account (CASA) customers.

Now, customers will be able to get verified as part of a CASA or credit card account application faster and more conveniently by opting-in to avail of the eKYC solution from their mobile phone. LenddoEFL’s eKYC solution offers a simpler way for banks to onboard new customers. See BSP Circular 950, Subsection X806.2 item D for details on using information and communication technology (ICT) in the conduct of face to face contact.

“This is a game-changer as we continue to adopt alternative digital verification and scoring to help push for a more secure, faster and reliable verification process to onboard more unbanked and underserved segments into the financial system, supporting BSP's mission of financial inclusion,” said Judith Dumapay, APAC Sales Director Philippines, LenddoEFL.

Each bank considering to use technology-aided verification must do so within their risk-based customer acceptance policies and anchored on the results of their risk assessment process. They also remain responsible for watchlist screening.

Read in PRWeb.com

CFI.Org | Aim. Build. Leverage. Partner. Persevere: 5 Tips to Leverage Alternative Data to Bank the Unbanked

Alternative data can help FSPs reduce loan defaults and speed up the approval process, but pitfalls exist

Written by Rodrigo SanabriaLenddoEFL

 

bernard-hermant-592731-unsplash.jpg

I have been rolling out alternative data initiatives for financial inclusion across Latin America for several years. At some point, my clients ask: “is this going to work?” My usual answer is “I’ve failed enough times to have figured this out.”

This is a fairly new and not completely mature field. LenddoEFL has been doing this for over 10 years. While there is still a lot to learn, my team and I can share some wisdom.

In response to Accelerating Financial Inclusion with New Data, I recently wrote about the promise and challenge of using alternative data to bank the unbanked. We’ve learned a lot about applying alternative data and have identified five key success factors:

 

1. Aim at the pain
2. Build on top of your current business
3. Leverage the best data source for you
4. Partner with somebody that can handle multiple data sources
5. Persevere. Capture low-hanging fruit without losing sight of the big prize

We will tackle one at a time.

1. Aim at the pain

Some financial institutions come to us interested in “trying out” alternative data. Our usual question is “what problem are you trying to solve?” Sometimes they are not clear about what they want to solve, and sometimes they want to fix too many things at the same time. The whole approach for the initiative will depend on this understanding. Choose one pain, focus on it, and build the KPIs to measure success according to this.

Keep repeating to everybody the pain you are attempting to solve to make sure everybody shares the same understanding.

These are some examples from our experience:

• An MFI wanted to increase productivity per loan officer while maintaining default rates: reduce turn-around-time, workload in the field, and complexity. Its client base was made up of unbanked and thin-file customers, so, automation based on traditional scores was not an option. Solution: Collect psychometric information for credit scoring which would allow a centralized, automated process.

• A non-traditional microlender wanted to obtain early warnings of clients that would likely fall in arrears on their next installment so that they could better focus pre-emptive collections efforts. By combining traditional repayment data with Android phone data, we are able to “rank” clients by the probability of next payment default. Now they can focus on the the one-third that will create 75 percent of the defaults.

• A traditional financial institution was turning down about one-third of applicants due to lack of credit history, and not belonging to the “right” demographics. They decided to invite “rejects” to re-apply by providing psychometric information, which allowed us to “rescue” about half of those prospects without increasing the default rate.

• A home appliances retailer providing $200 loans to consumers was losing clients due to the time required to verify their identity. By leveraging social network data, they have been able to reduce the approval turnaround time from two days to a few minutes in most cases. They have been able to approve more clients, reduce the cost of identity verification, and reduce cases of fraud.

2. Build on top of your current business

A good friend and a brilliant risk professional called me asking for help: “We are planning to launch a new product, for a new segment, in a new channel, so we need to use a new source of data to build an origination model.”

“Too many ‘news’ in the equation,” I told him. However, I joined his new venture.

You can guess how this adventure ended: slow volume uptake, lack of an actionable model after several months, and little enthusiasm to keep investing in order to capture value.

As we discussed in the first post, building models with alternative data is a numbers game. You need volume.

In the successful cases we mentioned before, we collected alternative data from a population that was already being served through a channel already established. This was to support a product with existing traction in the business. Innovation was concentrated in the data source and methodology to asses risk.

3. Leverage the best data source for you

Each source of data has advantages and drawbacks. In the front end, some sources may create more or less friction on the client onboarding, depending on origination processes. On the backend, usually the “low-friction” data is not structured. Unstructured data is not organized in a predefined way, so using it to build a risk model is more challenging than using structured data.

Once you have identified the pain point, you may work out with your partner/vendor the tradeoffs considering your population and channel. Note the following tips:

• Highly digital populations already served through an online channel may be approached using digital data, but you must make sure that you can get the volumes required to build a model based on unstructured data (unstructured data requires more volume to build a model).

• People with whom you already have an ongoing relationship may be a good population to leverage mobile phone data, as they may perceive a benefit to downloading and keeping your mobile application.

• Less digitized populations, served through traditional channels (branches or field loan officers) may be better suited for psychometrics.

Avoid the pitfall of falling in love with a specific data source and then figure out a use case within your business. Go the other way around: “given my business need, what data source better fits it?”

4. Partner with somebody that can handle multiple data sources

“When you only have a hammer, all problems look like nails,” my first boss told me a long time ago. To avoid the pitfall described on recommendation three, you must partner up with a vendor that can manage several data sources.

This will not only let you choose the right pain and business to focus on, but also give you flexibility as you roll out.

For example, we found, while working with a one client that their clients would willingly share their email data. Unfortunately, we found that they used their email so scarcely, that we couldn’t score many of them. Now we are working with psychometrics in this population.

In another situation, we started using psychometrics to approve more people at a Mexican e-lender. In the meantime—while they were approving more clients—we collected digital data from these same applicants. After several months, we have been able to combine both sources of data to approve even more people.

5. Persevere

If you are like most of us and work for an organization that needs results in a few quarters, structure your initiative to collect early results that may give you inertia while you go for the long-term prize.

We work with an institution that provides big loans. They do not have that much volume, but they invest heavily in each prospect. Big stakes, low volume is the most challenging environment to build an alternative data-based score. It took us almost 4 years, but now they are harvesting the fruits of their perseverance.

To deal with this issue, you need to be creative to identify secondary pain points that may be addressed quickly along the way.

For example, we worked for a retailer that wanted to increase approvals while keeping defaults in line by approving new-to-credit consumers. Loans had mostly 24 to 36-month terms and most 60 days defaulters tended to recover. That was a challenging situation: we would have to wait 12 months for vintages to mature, and look for 90 or 120 days in arrears for the “bads” to profile. It looked like a 2 to 3 year project.

But we found a secondary pain: “straight rollers.” These were loan recipients who didn’t pay their first two or three installments and were eventually written off. We collected data on all their clients to quickly build a “straight rollers model.” We only needed 3 installments on each vintage to identify bads.

Along the way, we are collecting data that will be used to build an admission score to address the main pain.

In summary, building credit policies based on alternative data is challenging. Fortunately, there is enough learning accumulated in our community to avoid some pitfalls and we hope you find these tips useful.

See post in CFI.Org

PRWeb | LenddoEFL Announces Hiring of Indonesia Country Director

Jefri Sormin.JPG

LenddoEFL, a fintech offering alternative credit scoring and verification solutions in emerging markets, welcomed Jefri Sormin as its new Indonesia Country Director.

“I’m joining LenddoEFL to help give more people access to credit and banking services, and to drive growth in Indonesia,” said Jefri Sormin, Indonesia Country Director, LenddoEFL. “Indonesia is home to 260 million people and the Financial Services Authority (OJK) has aggressive financial inclusion targets. Our solutions can help Indonesia achieve those goals, while helping banks serve more underbanked people with less risk.”

Jefri has over 15 years of experience in banking, including Citibank, General Electric, Sewatama and Orica. As Country Director for Indonesia at LenddoEFL, Jefri will be responsible for bringing the company’s credit scoring, verification and insights products to financial institutions in the country actively helping them to successfully achieve digital transformation.

“Indonesia is poised for continued growth in financial access and services,” said Mark Mackenzie, APAC Managing Director, LenddoEFL. “We are already seeing strong demand from Indonesian financial institutions for innovative ways to responsibly serve more people. I’m confident that Jefri’s leadership and brand-building skills will help us meet the demand in Indonesia.”

About LenddoEFL
LenddoEFL’s mission is to provide 1 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, LenddoEFL has provided credit scoring, verification and insights products to 50+ financial institutions, serving over 7 million people. Find more information at 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 | What exactly do we mean when we say financial inclusion?

LenddoEFL Partner Success Manager, Gerardo Rivero, doing field research for our financial access tools

LenddoEFL Partner Success Manager, Gerardo Rivero, doing field research for our financial access tools

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. 

  1. 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. 

  2. 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. 

  3. 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! 

 

Blog | Score Confidence: Boosting Predictive Power

image1.jpg

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

newswav.jpg

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

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.

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.

image2.jpg

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.

image1.jpg

 

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

New Strait Times | 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 institutions, utility and credit card companies to reduce risk, increase portfolio size, improve customer service and accurately verify applicants. Read full article.