Financial Institutions

Blog | The LenddoEFL Assessment Part 2: Measuring how people answer questions with metadata

By: Jonathan Winkle, Manager of Behavioral Sciences, LenddoEFL

The last post showed how our psychometric content reveals people’s personality traits, but our assessment also captures an abundance of metadata. Metadata is information about how people process the questions and exercises they complete. Here are some examples.

  • How long did an applicant take to answer a question compared to their average response time?

  • How many times did an applicant change their mind and switch their response before submitting their answer?

  • Is the applicant’s information consistent with their written request to the financial institution? (e.g., requested loan amount)

By measuring metadata, LenddoEFL’s approach goes beyond what is possible in traditional credit applications to reveal more information about applicants. Consider the following question from our test:

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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:

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In this case response time was a non-linear predictor of default, where both slow and fast response times were associated with a greater credit risk!

There are many ways to interpret response time metadata. If an applicant answers a question quickly, are they confident or are they cheating? If they are taking a long time to respond, are they having difficulty understanding the question or putting extra effort into getting their answer right? By collecting metadata across all questions, we can compare a single response time to the applicant’s overall response time distribution to differentiate things like confidence and cheating (see graph below).

An example distribution of response times generated from artificial data

An example distribution of response times generated from artificial data

Conclusion

Metadata reveals another layer of behavior on top of the personality traits we target and can be used to identify features such as confidence, cheating, and confusion. These behavioral traits can be used for predicting default and ensuring that we are collecting high quality data for our models.



Blog | 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).

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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

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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

 

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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

Blog | Winning Agent Incentivization Strategies

By Brett Elliot, Director of Product, LenddoEFL
An agent helps a microentrepreneur apply for a loan in India

An agent helps a microentrepreneur apply for a loan in India

Lenders often use loan officers, promoters or other types of agents to sell loans, financing, and credit cards. This can be an effective way to acquire new customers but there is a big risk in this approach. Agent incentivisation plans will make or break your bottom line. Good incentive plans lead to large, healthy portfolios, but bad ones lead to fraud, default and chargebacks. We’ve worked with lenders all around the globe and have seen both good and bad strategies. Here are some of the best and worst.

Worst agent incentivisation strategies

Paying per applicant

When your goal is to get new customers, a very simple approach is to pay agents for each customer that applies. Obviously, the more people into the top of the funnel, the more customers you will get right? Not exactly. Agents will optimize for their commissions. First they will go after good borrowers, then they will go after anyone. And some of them will realize they can make up fake applicants so they don’t have to go after anyone at all. If you simply try to optimize the number of applications, you will get a lot of applications, few disbursements, and even fewer good customers. This may seem obvious but we know of a large commercial bank in South America that did this and they had a huge problem finding good borrowers.

Paying per approval

Since paying per applicant does not work, the next logical step is to pay per approval. This way your agents can still acquire as many people as possible but you keep the portfolio healthy with controls in your approval process. This is the most common approach to incentivizing agents but it does create a perverse incentive for the agent to game the system in order to get more approvals. For example, one lender in Mexico was offering credit cards to college students. At some point the agents discovered that the only criteria for approval was that the applicant be under 26 years old. This caused a huge spike in fraud as the agents signed up anyone they could find that met the age criteria. If you are going to pay per approval, the controls you set must be strong and difficult to game. See below for recommendations about how using technology can help.

Paying a commission higher than the loan interest

A subtle but often overlooked problem with any incentive plan is paying an agent a higher commission than the loan interest costs. We know of a digital bank in South America that does this and they are experiencing a type of perpetual fraud. In this scheme, the agent fills out an application using a stolen identity. Then he takes out another loan using a different stolen identity and pays back the first one with it. He repeats this process over and over, collecting his commission. This is only possible if the commission is higher than the interest rate so keep that in mind when making your commission schedule.

Good agent incentivisation strategies

Paying a percentage of the money collected

One approach that aligns the agents and lenders is to pay the agent a percentage of the money collected. This incentivizes the agent to sign credit worthy new customers but also to follow up with the customer and make sure they make their payments. We sometimes call this the “MFI model” because of how effective some MFIs have become by using it.

Withholding payment for a certain period of time

Paying a percentage of the money collected doesn’t work for credit cards, but a strategy that does is to withhold payment for a certain amount of time. For example, one financial institution we know of pays the agent a little when the credit card is activated and then the remainder in 4 months so long as the card is not in arrears.

Recommendations

Combine several incentives

There is no one size fits all for any company and often combining different incentives works really well. The companies with the healthiest portfolios that we have seen do this. For example, a large MFI in Mexico pays their agents a very low fixed salary, plus a percentage of the amount disbursed and a percentage of the money collected.

Use technology to strengthen the controls

If you are certain that your controls and risk models are effective at weeding out the bad borrowers, then paying per approval is a reasonable approach. But how certain are you especially if your customers do not have much credit history? Technology can definitely help in this area. Using highly predictive alternative credit scores is a great start. Be sure to ask the vendor what controls are in place to detect agent fraud since they will try to game the system. At LenddoEFL, our scores are backed by our Score Confidence system which looks out for suspicious loan officer behavior and coaching as well as other signs of fraud.

Final thoughts

The end goal is to reward agents for portfolio growth and portfolio quality.  Lenders that limit the number of new clients added per month, maintain a solid portfolio of recurring clients, and combine different incentives for compensating agents, seem to have the largest and healthiest portfolios.

Blog | Turning Gini into Profits

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.

Figure 1

Figure 1

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?”

 

Figure 2

Figure 2

 

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”

Figure 3

Figure 3

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!).

Figure 4

Figure 4

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.

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

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

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

Read complete press release.

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Blog | Score Confidence: Boosting Predictive Power

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Note: This is a new and improved version of a popular post from last year.

Our unique platform has a big reason to live: we provide fast, affordable and convenient financial products for more than 1 billion people worldwide. And there is only one way to accomplish that: by facilitating more actionable, predictive, robust and transparent information to our clients to enable them to make the best possible lending decisions. However, data quality pose the most challenging problem we have faced along this journey as it threatens the predictive power we are delivering to our clients. Therefore, through the years we have developed and perfected a one-of-its-kind way to assess the quality of the data applicants are supplying: Score Confidence.

What exactly is Score Confidence?

Score Confidence is a tailored algorithm that scans and analyzes psychometric information gathered through LenddoEFL's Credit Assessment to generate a Green or Red flag which reflects how confident we are on our score’s ability to represent an applicant’s risk profile:

  • The result will be Green if LenddoEFL is confident in the data quality such that we will generate and share a score based on it.
  • Conversely, the outcome will be Red when LenddoEFL’s confidence in the gathered information has been undermined.

What does Score Confidence measure?

Once the applicant has taken our psychometric assessment, we put the data through our Score Confidence algorithm to find out whether we can be confident in a score generated using this data or not. We will return a Green Score Confidence flag if we believe the score accurately predicts risk, and also be transparent about the reasons behind a Red Score Confidence flag to empower our partners with increased visibility and actionable information.

LenddoEFL's Score Confidence system is comprised of five Confidence Indicators of key behaviors, each generated from a combination of different data sources. If we identify evidence of any of the following behaviours, the assessment will be rated as Red and no risk score will be returned in order to protect our partners:

  • Independence – the assessment has not been completed independently, and LenddoEFL detects attempts to improve one’s responses with either the help of a third party or other supporting resources.
  • Effort – the applicant has not put forth adequate effort and attention in completing the assessment.
  • Completion – the applicant has not responded to a sufficient portion of the timed elements of the assessment.
  • Scoring error – a connection issue or system error occurred and LenddoEFL is unable to generate a score.

What information feeds Score Confidence?

Our data quality indicators are constantly reviewed and updated and, over the years, we have added new and different data sources to our Score Confidence algorithms:

  • Browser and device metadata surrounding the completion of the application
  • User interaction information with LenddoEFL’s behavioural modules
  • Self-reported demographic data

Our Score Confidence system flexibly combines all the available data in order to return a Red or Green status for each application.

How does Score Confidence help our partners make the best possible lending decisions?

To boost the predictive power we can deliver for our clients, LenddoEFL does not share a LenddoEFL score for applicants with a Red Score Confidence flag as we have learned that Red applications tend to have very limited predictive power whereas data coming from Green flagged assessments can effectively sort risk amongst applicants. Therefore, not lending against a score for Red flagged applications boosts the predictive benefit for our clients.

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).

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

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

Both CTOS and LenddoEFL have aided banks, lending instit…

Read more in NewsWav

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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

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

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

Financial Express | Credit bureau veteran Darshan Shah joins LenddoEFL as Managing Director

“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.

Read full article in Financial Express

SME Finance Forum | 10+ Innovative Fintechs to Demo the Africa SME Finance Forum 2018

The Africa SME Finance Forum 2018 will examine the key challenges faced by MSMEs in Africa, and explore innovative local and global best practice solutions to promote youth entrepreneurship and enhance access to financing for MSMEs.

The TechPitch will be held in the afternoon of May 15, and will provide an opportunity for innovative fintechs to demo their product to global and emerging market investment firms, financial institutions and other Forum participants. This exclusive opportunity is one of the key features of the conference and is open to a limited number of financial technology innovators working in the SME space.  Approximately 12 fintech companies are selected to demo their products to Forum participants during this 90-minute session.

We are pleased to already have a number of fintech innovators such as LendableTALASME Credit ProMobbisuranceN-Frnds,LendEnableAlternative CircleLenddoQ-LanaTopicusUber and many others joining us during the TechPitch. 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.

Can behavioral traits help financial institutions assess creditworthiness?

The Problem: Financial Exclusion

Financial inclusion is a defining challenge for this generation. Many governments and supranational agencies are investing to solve this problem. Even fintech companies are trying to help, but what is the real problem and how could it be solved?

The World Bank states “Around two billion people don’t use formal financial services and more than 50 percent of adults in the poorest households are unbanked. Financial inclusion is a key enabler to reducing poverty and boosting prosperity.”

You might ask, why isn’t this population going to financial institutions to improve their living conditions, and why haven’t financial institutions served them? From a business perspective the opportunity at a global scale is massive.