Blog | iDE Ghana increases access to sanitation with help of innovative credit assessment from LenddoEFL

Partnership allows Ghanaians to purchase their first toilets

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Globally, 32% of people lack access to a toilet in their homes (Source: WHO UNICEF JMP). In Ghana an astonishing 87% of people do not own a toilet. And in rural Northern Ghana, it is worse still. Two out of every five children in northern Ghana are stunted, compared to approximately 20% of children stunned nationally (Source: UNICEF).

iDE Ghana, a nonprofit that creates income and livelihood opportunities for poor rural households, wanted to improve sanitation in the region. They began by applying design thinking to understand the low rate of toilet use. It turned out that people didn’t know where to buy a toilet, and if they did, it was prohibitively expensive to buy.  People could not afford the full cost all at once, and there were no options to pay for a toilet over time, as there were for other large purchases.

"What we found was the criteria for borrowing towards non-income generating loans were ridiculous. So we set up a one stop shop for toilets and sanitation products, selling them door to door,” explained Valerie Labi, WASH Director at iDE Ghana. “And the beauty of the model is that we give our customers 6 to 18 months to pay the toilet off over time.”

This seemed like the perfect solution given the challenges to toilet purchasing uncovered, but it was still challenging. “We allowed people to pay over the course of 6 to 18 months but we required for the customer or a guarantor to prove their income with bank statements or payslips. And this was a big deterrent. No one wanted to give their bank statements to a toilet company. And it would take an average of 40 days to get through the process” Labi shared. “We realized these requirements were scaring away customers as they’d never had formal credit before. So we asked ourselves, how else could we assess creditworthiness in a more inclusive way?”

That’s when they came across LenddoEFL universal credit assessment. By collecting behavioral and psychometric data at the time of application, iDE’s commercial agents will be able to assess risk and make a decision in a day or less, cutting down the time to sale greatly. Previously, the commercial agent made multiple calls and visits to collect the required documents. By using the LenddoEFL score, iDE removes the need for a guarantor or proof of income for the best scoring customers. Low scorers will need to pay 50% of the cost of the toilet in monthly installments before receiving the toilets.

iDE’s goal is to provide 20,000 to 25,000 toilets to households in Northern Ghana. At an average of 11 people per household, this will provide life-saving sanitation for 275,000 people. And the plan is to sell toilets as part of a fast, convenient customer-driven process and at affordable rates. With the LenddoEFL assessment in place since February 2018, iDE is already receiving positive feedback for customers who enjoy the process. Stay tuned for updates on this exciting partnership.

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

By: Richard Eldridge, LenddoEFL CEO

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

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

 

Our Responsibilities

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

1. Customer Protection and Privacy

We follow these five principles across our operations:

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

  • 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 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).

Blog | Credit Scoring using Digital Footprints

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Evaluation of Research Findings

Author: Carlos Del Carpio

On April 30th, an exciting new piece of research was published on SSRN - “On the Rise of Fintechs – Credit Scoring Using Digital Footprints.” It was as if we had commissioned it, as this piece helps to validate what our clients around the world already know: how someone behaves online is a reflection of their character, and can be used to measure risk and boost lending. We are grateful to the authors for their work as this paper provides a great insight into the predictive power that credit scoring models can achieve by using alternative data.

However, the methodology used to build and assess the models is as important as the data used as input. In this post we share a couple of observations about the methodology used in this paper based on over 10 years of experience building and deploying alternative data credit scoring models in production environments around the world.

 

#1 Using cross-validation alone to assess credit scoring models can inflate predictive power. We recommend to use out-of-time out-of-sample hold-outs to set more realistic performance expectations.

Credit scoring’s main objective is to predict credit repayment behavior in order to make credit risk decisions. In that sense, it is a forward looking predictive modeling exercise.

As in many applications of predictive modeling, in a credit scoring setting both the context and the behavior of the population being studied tends to evolve over time. The macroeconomic environment changes, the credit policies changes, the origination and collection processes change, and therefore the population itself is changing. All these changes combined often  introduce bias and systematic differences between the training and validation sets used to build credit scoring models. Other economic and financial applications of predictive modeling face similar challenges. For example, if a model for predicting stock values is trained on data for a certain five-year period, it is unrealistic to treat the subsequent five-year period as a draw from the same population. In the same way, using cross-validation alone, where testing samples are drawn from the same time period as the training sample to validate the results of predictive models could lead to predictive power expectations that will differ greatly from the actual predictive performance that can be achieved.

To address this, using an out-of-time out-of-sample set of data that is representative of the most recent time period is preferred and can enable more realistic results. Since the model in this paper only uses out-of-sample cross validation, the results may be too optimistic compared with the actual results it may return when implemented.

 

#2 The paper omits feature selection, an important part of the model building process. This decision could lead to completely different results.

Dimensionality reduction methods, such as feature extraction (FE) and feature selection (FS) are important components of the building process of credit scoring models. However, depending on the classification technique used to estimate the functional form of the final model, FS can be done independently of model estimation, or it can be embedded in the process (i.e. built into the classifier construction occuring naturally as a part model estimation).

Logistic regression, for example, does not perform FS as part of its estimation. Therefore it has to be performed independently each time before the actual estimation. If FS is required to be done independently by the modeling procedure, this must be repeated each time for every training set, which in turn creates different optimal models with different sets of variables for each iteration of the cross-validation, which adds an additional step in order to choose the final set of variables to go into the final model.

This paper omits this problem completely by forcing all variables everytime without using any criteria for feature selection. In other words, the authors forcefully and arbitrarily “select” to include all the features, something that is rare and unrealistic in most credit scoring settings where there are hundreds or thousands of variables to choose from and are impossible to fit within a logistic regression model due to the curse of dimensionality[1]. This is an intrinsic problem for credit scoring models that include big data sources such as digital footprint and social data, and the reason why feature extraction and feature selection methods can play a key role, sometimes as important as the techniques used to estimate the final functional forms. Had the authors included a feature selection process as part of each iteration in the cross validation process, it could have yield very different results.  

In conclusion, putting these modeling observations aside, we consider this paper important because it offers a clear example of the signal that can be found in digital footprint data, and the possibilities to improve current methods and data sources. We just provide a few examples on how methodological choices can affect how results are estimated and assessed, and why are they important to assess the full potential of the predictive power in this particular type of data.

 

[1] Donoho DL. (2000). High-dimensional data analysis: The curses and blessings of dimensionality. AMS Math Challenges Lecture, 1, 32 pp.

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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Yahoo Japan | Can Japanese banks use big data with "AI loan"? (日本の銀行は「AI融資」でビッグデータを活用できるか)

Attempts to calculate the creditworthiness of individuals by AI (artificial intelligence) and to finance using it are expanding. This is called "AI score lending". 

 The meaning of AI doing loan screening, which is one of the most important tasks of banks, is quite large. 

 However, the question is whether Japanese financial institutions can handle big data. If it can not do it, it will repeat the failure of the past score lending. 

Singapore's Lenddo is a service in emerging countries such as India, Vietnam, Indonesia, which have never had a history of credit. 

Read full article

Sina News Taiwan | How to break the credit assessment problem? (如何破解信貸評估難題?)

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Bangladeshi banker and Nobel laureate Muhammad Yunus (Muhammad Yunus) the promotion of microfinance , is the poor through microcredit loans , so there is money to do a small business to support themselves, and thus get rid of poverty. However, due to the time-consuming and laborious credit evaluation of lenders, the large-scale application of microfinance is difficult to achieve once.

Nowadays, mobile banking comes. It can collect data to help people who have little formal financial records in the traditional sense to broaden their services. Labor costs are also greatly reduced. For example, Kenyan mobile telecommunications operator Safaricom and African Commercial Bank jointly launched the M-Shwari business in 2012, which can determine customers’ credit scores based on Safaricom’s user information and the trading history of its M-PESA mobile money business. Loan amount.

In addition to payment data, mobile phones (especially smart phones) can also provide more types of information for credit evaluation by borrowers . For example, a person's geographic location data can reflect whether he has a stable job and fixed residence; shopping records can even reveal whether the borrower is pregnant ; and the richness of information obtained by social media is not Yu.

The fintech start-up company Lenddo EFL also uses the Internet to conduct psychological tests on potential borrowers. The question concerns the concept of money (for example, choosing to pay $10,000 at a time, or $20,000 for six months), where your money is spent. , Evaluation of living communities, etc., to determine the reliability of testers loan repayment. To date, the company has completed more than 7 million credit assessments, helping consumers with a lack of traditional credit records to borrow 2 billion U.S. dollars from 50 financial institutions of varying sizes.



詳全文 如何破解信貸評估難題?-財經新聞-新浪新聞中心 http://news.sina.com.tw/article/20180514/26854022.html

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.

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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.

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

The Economist | Mobile financial services are cornering the market

Mobile money means more nimble financial services

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KAUSAR PARVEEN, of Chakwal district in the north of Pakistan’s Punjab province, is a star beneficiary of the work of Karandaaz, a Pakistani financial-inclusion charity. The owner of just one buffalo, she borrowed 75,000 rupees (about $650) to buy another one and started selling milk. The business has done so well she now has four buffaloes and an assistant, and has taken out another loan to install a biogas plant, saving on firewood and sparing her family the woodsmoke.

This was how microcredit, as promoted by Muhammad Yunus, a Nobel-prizewinning entrepreneur from Bangladesh who launched his Grameen bank in 1983, was supposed to work: credit would allow the poor to establish microbusinesses and improve their lives. The idea has spread across the developing world. Sadly, in many places it has not worked out that way. A big expansion of microcredit in India’s Andhra Pradesh province caused a crisis in 2010 when the lenders were blamed for an increase in suicides by farmers. A World Bank paper last November, written by Robert Cull of the bank and Jonathan Morduch of New York University, considered evidence showing that microcredit has had “only modest average impacts on customers”. It has often been used to cover the normal ups and downs of household spending, which is helpful but not transformative. Read full article.

MICROCAPITAL | Traditional Credit Bureau Ctos Tapping LenddoEFL to Add Digital, Behavioral Data to Scoring Models to Boost Financial Inclusion in Malaysia

Malaysian credit scoring firm Ctos recently partnered with LenddoEFL, an alternative credit scoring firm with offices in Singapore and the US, to increase the number of people and small businesses for which it can supply credit evaluations. The usage of alternative data can also improve the credit scores of some loan applicants.

LenddoEFL, which was created in 2017 by the merger of Singapore-based Lenddo and US-based Entrepreneurial Finance Lab (EFL), bases its evaluations on “social media activities, browsing behaviour, geolocation and other smartphone data.”

As of late 2017, the organizations had completed a total of 5 million credit evaluations facilitating USD 2 billion in lending by 50 banks, microfinance institutions, insurers, retailers and telephone companies in 20 emerging markets.

Read full article

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

Forbes | Could Personality Tests One Day Replace Credit Scores?

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If someone gave you an unexpected $100, what would you do with it? Give it to charity? Save it? Splurge on something fun?

We see questions like this in personality quizzes online, and sometimes even when applying for jobs. Your answers are supposed to help others predict your behavior using what’s called psychometrics.

And companies looking to avoid hiring potential problem employees aren’t the only institutions interested in psychometrics. The financial industry might get in on it, too.

What if, instead of a lender checking your credit score, they gave you a personality test?

Read full article.

Spore Magazine | Réduire les risques : Des systèmes innovants d’évaluation du crédit pour aider les agriculteurs

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La difficulté d’emprunter, pour de nombreux petits agriculteurs ne disposant ni de garanties ni d’antécédents de crédit, a fait apparaître de nouveaux systèmes pilotes d’évaluation du crédit pour aider les banques à apprécier les risques que présentent réellement les emprunteurs et tirer parti de ce secteur potentiellement lucratif.

L’évaluation psychométrique

Pour augmenter les taux d’acceptation et réduire les délais de traitement des prêts aux agriculteurs, Juhudi Kilimo, prestataire de solutions financières pour les petits agriculteurs d’Afrique de l’Est, teste la méthode d’EFL Global, une entreprise privée qui utilise l’évaluation psychométrique pour créer les profils de risque d’emprunteurs africains, asiatiques, européens et latino-américains. Cette méthode pilote – financée par la Fondation Mastercard – mobilise les représentants de six agences kényanes de Juhudi qui visitent et incitent les demandeurs de prêts à passer des tests psychométriques sur tablette. Ces tests permettent, selon EFL, de définir leur personnalité, y compris leur self-control en matière de dépenses et budgétisation. Sur cette base, une cote de crédit à trois caractères est alors attribuée aux demandeurs. À partir de son évaluation initiale d’environ 6 000 clients réalisée à l’aide de l’outil d’EFL, Juhudi a constaté que 6 % des personnes classées dans le quintile le plus bas avaient au moins une fois des arriérés de remboursement de 60 jours pour un prêt type d’un an, contre 1,5 % dans le quintile le mieux noté.

Read full article.

Bitcoin | 8 empresas da América Latina interrompendo finanças locais paypal bitcoin

"O que define Lenddo além de tradicionais financiadores são os critérios é usa para estabelecer os empréstimos, uma vez que depende de conexões sociais para determinar a solvabilidade. Por uma questão de fato, o seu alvo principal são os mutuários acesso bancário que são excluídos do circuito de crédito tradicional e estão acima do limiar de micro-finanças."

Read full article.

Medici | What Happens at the Convergence of Machine Intelligence and Online Lending

Credit scoring and approval rates changed substantially with the arrival of alternative lenders, mainly due to the adoption of new practices in collecting and analyzing potential borrower data. Alternative data has played its role in expanding horizons for financial institutions and for creating an opportunity to enter the financial sector fir technology startups and data-rich international companies.

While social media, for example, as a source of data for creditworthiness assessment is still at a nascent stage, certain startups are already claiming to have incorporated information from social networks into their frameworks. In the quest to reinvent the way to assess consumer-related risk (as well as extend credit to unscored and questionable), startups were found more imaginative than traditional institutions.

Alternative data requires alternative approach to data analytics, which wide adoption of machine learning and artificial intelligence brought.

Read full article

Medici | How BigTech Challenges Banks

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

Big data customer insights

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

Read full article

 

PRSync | The Future of Artificial Intelligence in Banking

 

"The Future of Artificial Intelligence in Banking", report examines the most significant uses of AI in retail banking, in both front-office and back-office implementations.

Companies Mentioned:
Admiral
Amazon
Atom Bank
Bank of America
DataVisor
Ernest
EyeVerify
Facebook
Google
IDnow
Kasisto
Lenddo
Moneyhub Enterprise
Olivia
PayPal
Personetics
Plum
POSB
Starling Bank
USAA
TrustingSocial
Wells Fargo
ZestFinance
Inquire for Report at http://www.reportsweb.com/inquiry&RW0001866700/buying

Read full article

Microfinance Gateway | Malaysia: Fintech Heavyweight CTOS Expands Services for A Better Financial Inclusion

CTOS has been Malaysia’s largest in terms of credit reporting, just announced a partnership with LenddoEFL to achieve a joint vision of financial inclusion for the people who had difficulties securing loans in Malaysia due to the lack of credit history. 

Read article in MicroFinance Gateway website: https://www.microfinancegateway.org/announcement/malaysia-fintech-heavyweight-ctos-expands-services-better-financial-inclusion

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

Enanyang MY |【独家】申请银行贷款被拒 CTOS接纳无信贷记录者

(吉隆坡12日讯)许多早前因传统信贷风险而无法获得贷款机构服务的人士,现今将可享有更包容性的金融服务。 马来西亚信贷情报服务私人有限公司(CTOS)今天与LenddoEFL...

统信贷风险而无法获得贷款机构服务的人士,现今将可享有更包容性的金融服务。 马来西亚信贷情报服务私人有限公司(CTOS)今天与LenddoEFL 达成合作伙伴关系协议,携手为甚少或几乎没有信贷记录的大马消费者,实现普惠金融的CTOS信贷记录有限评分

Read full article.