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LenddoEFL offers a credit-score alternative using your character

LenddoEFL offers a credit-score alternative using your character

Financial situations change, but your personality will hold steady.

SINGAPORE, Oct. 14, 2020 /PRNewswire/ -- LenddoEFL, a pioneer of alternative data for decisioning, has shown that credit scores based on behavior and personality traits are continuing to accurately predict default risk, despite the unprecedented economic disruption caused by COVID-19 lockdowns.


Behavioral data for credit scores
Behavioral data for credit scores

LenddoEFL customer, LendMe in Nigeria, has witnessed this new credit-score reality first-hand. They have used LenddoEFL solutions for more than three years to evaluate applicants for Nano-loans. Despite the entire country going into lockdown earlier this year, LenddoEFL's models have shown strong resilience throughout the crisis and continue to successfully discriminate customers based on their risk level, enabling their lending business to continue at a critical time.

LenddoEFL VP Corporate Development, Camille O'Sullivan, said, "For us, COVID-19 has shone a light on the fact that, while your financial situation may change, your core character traits tend to be much more stable. By looking at a customer's interests (through the apps they download), their reliability, perfectionism or stability traits we are able to provide a more complete profile of an individual."

The credit-scoring difference with LenddoEFL:

  • Globally 1.7bn people don't have a bank account, meaning they are invisible to lenders using traditional credit scores.
  • Every time we pick up our phone, we leave a digital footprint. There is an enormous amount of alternative data which can be used to make a customer visible to a lender.
  • LenddoEFL uses alternative data including mobile phone, digital footprint, behavioral, and psychometric data to assess the credit risk of anyone.
  • Through LenddoEFL's data-driven decisions, financial institutions can grow their portfolios with less risk, allowing millions of people to access affordable financial services faster and more conveniently.
  • LenddoEFL models are proving resilient despite financial instability caused by lockdowns.

To learn more about this approach to scoring, click here:

Getting credit in Nigeria

Nigeria is an entrepreneurial economy with an estimated 37 million micro, small and medium-sized companies[1]. The World Bank predicts many of these businesses could grow if they had access to finance.

However, to access loans, consumers need a credit score. It is estimated that only 40% of adults in Nigeria have a transaction bank account[2]. With no account, these people are 'invisible' to banks using traditional credit scoring.

When COVID-19 arrived in Nigeria early this year, the country moved into lockdown restrictions, resulting in millions losing their income. In Nigeria, as around the world, loan default rates have dramatically increased. The Nigerian economy, Africa's largest, was hit hard. The economy contracted 6.1% in the second quarter of this year and 27% of Nigeria's labor force (over 21 million Nigerians) are unemployed.[3]

An economic shift of this magnitude results in significant changes in consumer behavior. This has impacted credit scoring models. Predictable behavior has become unpredictable. This makes it difficult for lenders to know who to lend to. How can you assess the risk of applicants when you are in uncharted waters?

The result is less money being lent, at a time when people need it the most. Alternative data decisioning can help financial institutions find a way forward.

Credit through COVID-19: Results at LendMe

While LendMe has seen a slight increase in late payments, especially with recurring customers accessing larger loans, LenddoEFL models continued to accurately predict default risk across the entire portfolio of loans. By combining a tighter credit policy and credit risk models built with a focus on long-term stability in addition to predictive power, LendMe has been able to continue lending to customers needing credit without taking unnecessary risk.

Camille O'Sullivan continues, "We believe that one of the reasons our models continue to hold up is that we only add features that are stable over time and that make business sense. We ensure that the model passes our stability algorithm checks before being selected for implementation."

LenddoEFL Executive Chairman and CFO, Paul Devine, said, "The results at LendMe are a validation of what we've been working on for a decade. We've shown that your ability to access financial services shouldn't only be determined by your bank balance."

"As the effects of the pandemic continue to play out around the world, banks and lenders will have a critical role to play as stabilizers, and they will need to rely on new solutions to do so. Our models are proving that your character is a strong indicator of your likelihood to repay. We've already assessed over 7 million people worldwide. And we're just getting started."

About LenddoEFL

At LenddoEFL we see things differently. We spend our days building products that help make the invisible, visible.

We offer data-driven decisions for financial institutions. Thanks to LenddoEFL's alternative data credit and verification tools, lenders in emerging markets are able to grow their portfolios with less risk, allowing millions of people to access affordable financial services faster and more conveniently than ever before. 

LenddoEFL offers unrivaled experience founded on more than ten years of risk modeling and four years of online lending experience. We are pioneers in using alternative data for decisioning. Our growing global team includes credit risk experts, data scientists, developers, analysts, and business development professionals. 

LenddoEFL gives you the data advantage. We create, collect, and analyze data from a wide array of consent-based alternative data sources for an accurate understanding of creditworthiness. Our unique credit decisioning tools draw from diverse, large and unstructured data sources using deep learning, AI and advanced modeling techniques.

We have global coverage. Our tools are universally and uniquely able to score anyone regardless of demographics, location, technology access or previous financial history. We serve over fifty financial institutions across twenty emerging markets.

We bring together the best sources of digital and behavioral data to help lenders in emerging markets confidently serve underbanked people and small businesses.

LenddoEFL in numbers:

  • Over $2 billion lent using LenddoEFL solutions.
  • Over 7 million people assessed
  • Over 12 billion data points analysed
  • Worked with over 50 financial institutions across more than 20 countries.


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