Topic: ⚡️⚡️⚡️[ANN] [PRE-ICO] LendsBay | Credit ecosystem - page 81. (Read 7899 times)
When does presale and bonus end?
Lendsbay is normalising this informal lending market would reduce the risks for lenders and improve the terms for borrowers.
LendsBay Credit ecosystem is a system for trusted financial transactions between people based on transparency and blockchain technology.
Reminder, if you post low quality posts, like "Good project," or "It has many investor!." The mods will delete it.
This project looks very promising. Good luck with the journey!
Amazing project.. best of luck to the entire team...
Reallry interesting project I've ever seen guys ! Cheers!
For this to work it needs mass promotion in the mainstream. If you want to lend money to say a friend, chances are the friend will need to know about the app.
Thank you very much for your comment. Both lenders and borrowers benefit from recording their loans via the app than otherwise. Lenders feel more secure because the loan is not only recorded, but that there is an enforceable contract in place. The borrowers benefit because if they repay the loan, which good borrowers tend to do, the the fact of the repayment will be added to their credit history. The potential borrowers will be able to request loans from potential lenders via the app and the lenders will find out via such a request about the Lendsbay app. We intend on making the app very easy and intuitive to use and rely on high word of mouth popularity of the app
Oh ok, now I see. Concepts are just sometimes difficult to comprehend for new things. I was thinking that this was something that one uses among people they already had contact with. But people can borrow from other people who use the app, no matter who they are.
Am in love with this project, the best so far
⚡️ LendsBay ⚡️
Credit ecosystem
System for trusted financial transactions
between people based on transparency
and blockchain technology
between people based on transparency
and blockchain technology
Presentation | Whitepaper | One Pager
https://d.radikal.ru/d09/1806/15/86f9c1fdec8a.png
https://d.radikal.ru/d29/1806/17/f7310c041f68.png
https://d.radikal.ru/d09/1806/15/86f9c1fdec8a.png
BACKGROUND
When it comes to the global lending market, there is one segment in particular that lacks transparency, i.e. lending between individuals (usually relatives, friends and acquaintances).
Although similar in size to the formal lending market (banks, credit card issuers, mortgage lenders, leasing providers), there are significant differences between the two.
Normalising this informal lending market would reduce the risks for lenders and improve the terms for borrowers.
The LendsBay system creates a new approach to loans between people, solving the problems such as:
⠀
❌ No formal loan records: disputes arise about repayment dates, terms and conditions; no reminders are sent
❌ No contract: there is no mechanism for judicial enforcement of debt repayment
❌ No credit history, which can have an impact on a borrower’s ability to obtain subsequent loans
❌ No integration with the formal lending segment: behaviour in one segment does not affect financing conditions in the other
❌ No market-based mechanism for determining interest rates: the market is either larger or smaller than its potential
❌ No tools for risk management: no credit rating, diversification, insurance⠀
Although similar in size to the formal lending market (banks, credit card issuers, mortgage lenders, leasing providers), there are significant differences between the two.
Normalising this informal lending market would reduce the risks for lenders and improve the terms for borrowers.
The LendsBay system creates a new approach to loans between people, solving the problems such as:
⠀
❌ No formal loan records: disputes arise about repayment dates, terms and conditions; no reminders are sent
❌ No contract: there is no mechanism for judicial enforcement of debt repayment
❌ No credit history, which can have an impact on a borrower’s ability to obtain subsequent loans
❌ No integration with the formal lending segment: behaviour in one segment does not affect financing conditions in the other
❌ No market-based mechanism for determining interest rates: the market is either larger or smaller than its potential
❌ No tools for risk management: no credit rating, diversification, insurance⠀
WE OFFER
We offer the following step-by-step solution to the above-mentioned problems:
1️⃣ Creating an app to manage loans between relatives, friends and acquaintances: the app is used to find a lender and then to record, document and administer (e.g. send out reminders) the loan.
2️⃣ Creating social groups (so-called bays) to pool financial resources through mutual lending, e.g. there are social groups centred around work, universities and social clubs that already have a certain level of trust, common values and a high degree of social control, not to mention little tolerance for irresponsible behaviour.
3️⃣ Creating a blockchain to store individuals' credit-rating information (LBR)
4️⃣ Using the blockchain for any financial transactions between individuals (non-credit assessment) (LBU)
5️⃣ Developing the elements of an ecosystem for financial relations: mutual insurance; purchasing, selling or leasing items; and decision-making systems
Every action taken by a Lendsbay ecosystem user is reflected in their rating, which is based on reliable and trustworthy behaviour within a social group. Since the ecosystem promotes users with high ratings, the amount of reliable and trustworthy behaviour will continue to increase.
1️⃣ Creating an app to manage loans between relatives, friends and acquaintances: the app is used to find a lender and then to record, document and administer (e.g. send out reminders) the loan.
2️⃣ Creating social groups (so-called bays) to pool financial resources through mutual lending, e.g. there are social groups centred around work, universities and social clubs that already have a certain level of trust, common values and a high degree of social control, not to mention little tolerance for irresponsible behaviour.
3️⃣ Creating a blockchain to store individuals' credit-rating information (LBR)
4️⃣ Using the blockchain for any financial transactions between individuals (non-credit assessment) (LBU)
5️⃣ Developing the elements of an ecosystem for financial relations: mutual insurance; purchasing, selling or leasing items; and decision-making systems
Every action taken by a Lendsbay ecosystem user is reflected in their rating, which is based on reliable and trustworthy behaviour within a social group. Since the ecosystem promotes users with high ratings, the amount of reliable and trustworthy behaviour will continue to increase.
LOANS BETWEEN FRIENDS AND ACQUAINTANCES
The system creates a new approach to loans between friends and acquaintances:
✅ A new approach to lending based on mutual assistance, trust and transparency that is always accessible through your phone
✅ The ability to quickly and easily record a loan to or from a friend or an acquaintance with confirmation from the other party
✅ Flexible loan terms at any time anywhere in the world
✅ A rating that combines all the advantages of a bank rating (based on data from credit bureaus and Big Data sources) and a proximity rating
✅ Recommended interest rates for loans
✅ Borrowers create social ratings and a transparent blockchain of their personal international credit history
✅ Loan documentation motivates compliance with the terms of the agreement and enables court action if necessary
✅ Simple and convenient analytics
✅ Artificial intelligence is used to improve the algorithms for social ratings
✅ A new approach to lending based on mutual assistance, trust and transparency that is always accessible through your phone
✅ The ability to quickly and easily record a loan to or from a friend or an acquaintance with confirmation from the other party
✅ Flexible loan terms at any time anywhere in the world
✅ A rating that combines all the advantages of a bank rating (based on data from credit bureaus and Big Data sources) and a proximity rating
✅ Recommended interest rates for loans
✅ Borrowers create social ratings and a transparent blockchain of their personal international credit history
✅ Loan documentation motivates compliance with the terms of the agreement and enables court action if necessary
✅ Simple and convenient analytics
✅ Artificial intelligence is used to improve the algorithms for social ratings
BENEFITS FOR THE LENDER
+ A recommended interest rate for loans
+ More effective use of free cash
+ Relations are documented (loan agreements)
+ Repayment notification and reminders
+ Analysis of how free cash is being used
+ Use of social control within groups
+ Preparation of a statement of claim in case of default
+ Possibility of using debt collectors
+ Potential to use insurance to reduce risks
+ A recommended interest rate for loans
+ More effective use of free cash
+ Relations are documented (loan agreements)
+ Repayment notification and reminders
+ Analysis of how free cash is being used
+ Use of social control within groups
+ Preparation of a statement of claim in case of default
+ Possibility of using debt collectors
+ Potential to use insurance to reduce risks
BENEFITS FOR THE BORROWER
+ Quick decision-making
+ Lower interest rates on loans than those from banks
+ No mandatory insurance
+ Remote loans through the app anywhere in the world
+ Flexible loan terms
+ Possibility of debt restructuring
+ Possibility to obtain a loan without having a bank credit history
+ Transparent international credit history and social rating in the blockchain
+ Quick decision-making
+ Lower interest rates on loans than those from banks
+ No mandatory insurance
+ Remote loans through the app anywhere in the world
+ Flexible loan terms
+ Possibility of debt restructuring
+ Possibility to obtain a loan without having a bank credit history
+ Transparent international credit history and social rating in the blockchain
SOCIAL GROUPS (BAYS)
Ninety-seven per cent of all borrowers who borrow from within a group return the amount in full (HeadHunter poll, 2017). In the case of the remaining 3%, they either forget to pay back the loan or are removed from the group.
Based on a balance between transparency and the degree of social control, we plan to emphasise the following main social groups in the app:
FRIENDS (relatives, friends and acquaintances). Given the high degree of trust within this group, the Lendsbay ecosystem adds a degree of responsibility (through loan documentation) and a convenient mechanism for accounting, management and oversight.
WORK (networks of co-workers). This social group is characterised by an ample level of trust and social control while Lendsbay's user rating and legal arrangements further reduce risks.
UNIVERSITY (a group of people associated with one institution). This group is characterised by a sense of belonging and responsibility, along with the pluses and minuses of the above-mentioned groups.
Based on a balance between transparency and the degree of social control, we plan to emphasise the following main social groups in the app:
FRIENDS (relatives, friends and acquaintances). Given the high degree of trust within this group, the Lendsbay ecosystem adds a degree of responsibility (through loan documentation) and a convenient mechanism for accounting, management and oversight.
WORK (networks of co-workers). This social group is characterised by an ample level of trust and social control while Lendsbay's user rating and legal arrangements further reduce risks.
UNIVERSITY (a group of people associated with one institution). This group is characterised by a sense of belonging and responsibility, along with the pluses and minuses of the above-mentioned groups.
BLOCKCHAIN - LBR
The development of a unique system for rating users that combines all the latest developments in the banking sector, social scoring (proximity), the benefits of blockchain technology and artificial intelligence.
An important advantage of our rating is that it reflects a user's entire history of financial relationships in a format that the parties can easily understand and that is necessary to make the right decision when granting a loan. The rating will be used by players in the formal sector (banks, IFIs).
Financial companies from all over the world will be able to create products using the LBR ecosystem, thereby improving the quality of information and expanding the geography of usage to a global scale.
An important advantage of our rating is that it reflects a user's entire history of financial relationships in a format that the parties can easily understand and that is necessary to make the right decision when granting a loan. The rating will be used by players in the formal sector (banks, IFIs).
Financial companies from all over the world will be able to create products using the LBR ecosystem, thereby improving the quality of information and expanding the geography of usage to a global scale.
THE ECOSYSTEM AND THE OUTLOOK FOR PROJECT DEVELOPMENT (LBU)
Blockchain (LBU) and the ecosystem
As the Lendsbay project takes off and the number of users increases, new elements of the ecosystem that extend beyond formal boundaries will be developed:
> financial services
> ratings of users and of suppliers of goods and services
> mutual Insurance
> formalising relations for leasing various items
> co-financing
> decision-making systems
As the Lendsbay project takes off and the number of users increases, new elements of the ecosystem that extend beyond formal boundaries will be developed:
> financial services
> ratings of users and of suppliers of goods and services
> mutual Insurance
> formalising relations for leasing various items
> co-financing
> decision-making systems
ROAD-MAP
2016-2017
Createding a web prototype
Market research
UX testing
Builtding a financial model
Developed the server and user part of the application
JUNE 2018
Conducting a pre-ICO
JULY 2018
Release of the beta version of the app for Android/iOS
Developing the legal component (loan agreements, lawsuits, debt collectors)
Establishing ratings and pricing mechanisms
SEPTEMBER 2018
Carrying out an ICO
Connecting to the app
Adaptation for Telegram
Connecting to a credit bureau
Connecting to telecoms/online credit history providers
Entry into the UK and US markets
MARCH 2019
Creating social groups: Co-workers/University
Linking to a payment system
Implementing the social ratings system (proximity rating)
Implementing the behavioural ratings system
Creating an API
Entering developing markets
SEPTEMBER 2019
Implementing the blockchain ratings system (LBR): distributed accounting and storage of ratings data
Constructing a ratings model based on multiplicity of data
Providing the suppliers of goods and services with secure access to the ratings system data to create their own ratings
Granting financial organisations secure access to ratings data
FEBRUARY 2020
Creating a universal rating for economic relations (LBU)
Creating various ecosystem elements
Building a consolidated ecosystem of transparent relationships
Createding a web prototype
Market research
UX testing
Builtding a financial model
Developed the server and user part of the application
JUNE 2018
Conducting a pre-ICO
JULY 2018
Release of the beta version of the app for Android/iOS
Developing the legal component (loan agreements, lawsuits, debt collectors)
Establishing ratings and pricing mechanisms
SEPTEMBER 2018
Carrying out an ICO
Connecting to the app
Adaptation for Telegram
Connecting to a credit bureau
Connecting to telecoms/online credit history providers
Entry into the UK and US markets
MARCH 2019
Creating social groups: Co-workers/University
Linking to a payment system
Implementing the social ratings system (proximity rating)
Implementing the behavioural ratings system
Creating an API
Entering developing markets
SEPTEMBER 2019
Implementing the blockchain ratings system (LBR): distributed accounting and storage of ratings data
Constructing a ratings model based on multiplicity of data
Providing the suppliers of goods and services with secure access to the ratings system data to create their own ratings
Granting financial organisations secure access to ratings data
FEBRUARY 2020
Creating a universal rating for economic relations (LBU)
Creating various ecosystem elements
Building a consolidated ecosystem of transparent relationships
PRE ICO/ICO
LBT tokens will be released on the Ethereum platform and will fully comply with the ERC20 standard, which guarantees the compatibility of the token with third-party services and also ensures ease of integration.
LBT tokens are not limited to use on the ecosystem platform. After the platform is launched, LBTs token will be available for purchase/sale on cryptocurrency exchanges.
LBT tokens are not limited to use on the ecosystem platform. After the platform is launched, LBTs token will be available for purchase/sale on cryptocurrency exchanges.
TOKEN FUNCTIONS
> As utility tokens
> As a reward for the successful repayment of a loan through the app
> As a risk insurance tool for investors: in case of default, part of the amount is repaid in tokens that can be sold on the exchange
> As payment for a PRO subscription, which includes advanced features in the app
> As payment for services: guarantees on the part of borrowers, legal support, debt collection services
> For the global credit rating function on the blockchain (fuel) and subsequently for the whole ecosystem
> For recording the actions of borrowers in the system
> For third-party access to records/history with the consent of the borrower
> For recording credit history from third-party lenders (IFIs/banks)
> As utility tokens
> As a reward for the successful repayment of a loan through the app
> As a risk insurance tool for investors: in case of default, part of the amount is repaid in tokens that can be sold on the exchange
> As payment for a PRO subscription, which includes advanced features in the app
> As payment for services: guarantees on the part of borrowers, legal support, debt collection services
> For the global credit rating function on the blockchain (fuel) and subsequently for the whole ecosystem
> For recording the actions of borrowers in the system
> For third-party access to records/history with the consent of the borrower
> For recording credit history from third-party lenders (IFIs/banks)
TOKEN SALES: TERMS AND CONDITIONS
A total of LBT 100 million will be available, where 1 LBt = mLBt 1 million.
A total of LBT 100 million will be available, where 1 LBt = mLBt 1 million.
Of the total number of tokens:
5% will be available during the pre-ICO
70% will be available for sale during the ICO
15% will go to the system's insurance fund
5% will go to the founders
3% will go to the bounty programme
2% will be distributed among team members
Money received through the pre-ICO will be distributed as follows:
30% for development of the app
50% for ICO preparations
20% on salaries and the bounty programme
Money received through the ICO will be distributed as follows:
70% for project development
20% for the insurance fund to support the functioning of the platform (optional)
10% for the team, the founders and participants in the bounty programme
5% will be available during the pre-ICO
70% will be available for sale during the ICO
15% will go to the system's insurance fund
5% will go to the founders
3% will go to the bounty programme
2% will be distributed among team members
Money received through the pre-ICO will be distributed as follows:
30% for development of the app
50% for ICO preparations
20% on salaries and the bounty programme
Money received through the ICO will be distributed as follows:
70% for project development
20% for the insurance fund to support the functioning of the platform (optional)
10% for the team, the founders and participants in the bounty programme
LendsBay team have already developed a functional and ready-to-use alpha version of the application ⠀⠀
➡️ BETA VERSION OF THE APP/MVP ⬅️https://d.radikal.ru/d29/1806/17/f7310c041f68.png
LENDSBAY TEAM
The project has a unique team with great experience in banking in such areas as: risk management, corporate finance, it, marketing, derivatives, investment business.
ALEXANDER KOPTELOV (Founder, CEO). Graduated from the Department of Computational Mathematics and Cybernetics at Lomonosov Moscow State University, Master's in Finance from the New Economic School in Moscow. Banking experience includes seven-plus years in market risk, three-plus years in IT and four-plus years in corporate finance at Zerich Bank, Alfa Bank and Raiffeisen Bank. Associate director of Sberbank CIB.
➡️ LinkedIn
ANTON GAZIZOV (Founder, CFO/IR). Graduated from Cambridge University with a degree in Economics. Has extensive experience in corporate finance at a number of major global banks and investment companies, such as Goldman Sachs, Rothschild Investment Corporation, Deutsche Bank and VTB Capital. Managing Director of Sberbank CIB.
➡️ LinkedIn
ANDREY CHEREMKHIN (Founder, COO). Graduated from the Law Faculty at Moscow State Pedagogical University with a degree in Civil Law. More than 10 years' experience in the legal profession, including in the fields of intellectual property and IT, extensive judicial practice in courts of various instances. Entrepreneur.
➡️ LinkedIn
LYUDMILA LUKASHOVA (Founder, CRO). Graduated from the Faculty of Mathematical Methods in Economics at the Financial University under the Government of the Russian Federation. More than 10 years' experience in retail risk at top-three Russian banks, Chief Risk Manager.
➡️ LinkedIn
VLADIMIR GORBUNOV (Head of IT). Graduated from Russia's National University of Science and Technology (MISiS). More than 10 years’ experience in IT/programming and designing online and offline systems and applications. Three-plus years' experience in programming for iOS/Android. Extensive experience with Big Data and blockchain technology. Technical Director of a developer/integrator company.
➡️ LinkedIn
DARIA BATAMIROVA (Marketing strategy). Graduated from the British School of Design with a degree in Graphic Design and from the State University of Management with a degree in Sociology and Psychology. Over 13 years' experience in marketing communications and branding for the agencies JWT, BBDO, Y & R, LEO Burnet, DDB, DRAFTFCBADV and with international clients. Director of Marketing and Communications at Sova Capital Limited.
➡️ LinkedIn
KRISTINA SHILOVA (SMM). Graduated from the St Petersburg State University of Film and Television with a degree in Audiovisual Engineering. Also studied Marketing and PR. Five-plus years' experience in online marketing. A Google AdWords and Yandex.Direct certified specialist. Extensive experience with SMM and targeted advertising.
➡️ LinkedIn
ANGELICA PHILLIPS (UK Market). Prior to co-founding ANDN Consulting, was a partner at Norton Rose Fulbright Corporate Finance Department, London, and spearheaded Norton Rose Fulbright’s CIS practice. Vast experience in advising on emerging markets transactions, CIS and CEE.
➡️ LinkedIn
➡️ LinkedIn
ANTON GAZIZOV (Founder, CFO/IR). Graduated from Cambridge University with a degree in Economics. Has extensive experience in corporate finance at a number of major global banks and investment companies, such as Goldman Sachs, Rothschild Investment Corporation, Deutsche Bank and VTB Capital. Managing Director of Sberbank CIB.
➡️ LinkedIn
ANDREY CHEREMKHIN (Founder, COO). Graduated from the Law Faculty at Moscow State Pedagogical University with a degree in Civil Law. More than 10 years' experience in the legal profession, including in the fields of intellectual property and IT, extensive judicial practice in courts of various instances. Entrepreneur.
➡️ LinkedIn
LYUDMILA LUKASHOVA (Founder, CRO). Graduated from the Faculty of Mathematical Methods in Economics at the Financial University under the Government of the Russian Federation. More than 10 years' experience in retail risk at top-three Russian banks, Chief Risk Manager.
➡️ LinkedIn
VLADIMIR GORBUNOV (Head of IT). Graduated from Russia's National University of Science and Technology (MISiS). More than 10 years’ experience in IT/programming and designing online and offline systems and applications. Three-plus years' experience in programming for iOS/Android. Extensive experience with Big Data and blockchain technology. Technical Director of a developer/integrator company.
➡️ LinkedIn
DARIA BATAMIROVA (Marketing strategy). Graduated from the British School of Design with a degree in Graphic Design and from the State University of Management with a degree in Sociology and Psychology. Over 13 years' experience in marketing communications and branding for the agencies JWT, BBDO, Y & R, LEO Burnet, DDB, DRAFTFCBADV and with international clients. Director of Marketing and Communications at Sova Capital Limited.
➡️ LinkedIn
KRISTINA SHILOVA (SMM). Graduated from the St Petersburg State University of Film and Television with a degree in Audiovisual Engineering. Also studied Marketing and PR. Five-plus years' experience in online marketing. A Google AdWords and Yandex.Direct certified specialist. Extensive experience with SMM and targeted advertising.
➡️ LinkedIn
ANGELICA PHILLIPS (UK Market). Prior to co-founding ANDN Consulting, was a partner at Norton Rose Fulbright Corporate Finance Department, London, and spearheaded Norton Rose Fulbright’s CIS practice. Vast experience in advising on emerging markets transactions, CIS and CEE.
➡️ LinkedIn
ADVISORS
MURAT YENIKEYEV (Risk advisor). Graduated from the Department of Mechanics and Mathematics at Lomonosov Moscow State University, Master's in Finance from the New Economic School in Moscow. Spent two years working for a top-three telecoms operator and has eight-plus years of experience in the field of retail risk.
➡️ Y KHALIULLIN[/b] (Advisor). Graduated from the Cambridge University with a Master degree in Economics. Alexei worked at Deutsche Bank and Morgan Stanley in London, in a PE fund Alfa Capital Partners in Moscow. From 2005 to 2010 was the CFO of a premium fitness club chain "World Class". From 2010 to 2015 built a successful consumer business. Since 2015-consultant and project leader at The Boston Consulting Group, specializing in operations efficiency in a wide range of industries, as well as government innovation policy.
➡️ [url=https[Suspicious link removed]y-khaliullin-25a43399/]LinkedIn
ANDREY LUKASHEVICH (Advisor). Graduated from the Department of Computational Mathematics and Cybernetics at Lomonosov Moscow State University, MBA at INSEAD Business School. Worked as a partner at Kei-Ei Consulting, Ward Howell International and as an M&A consultant at PwC. 2015-2018 MD, CEO Delivery Club (the largest food delivery service, presented in 97 cities of Russia). Currently CEO of the investment division Mail.ru Foodtech Ventures.
➡️ LinkedIn
➡️ Y KHALIULLIN[/b] (Advisor). Graduated from the Cambridge University with a Master degree in Economics. Alexei worked at Deutsche Bank and Morgan Stanley in London, in a PE fund Alfa Capital Partners in Moscow. From 2005 to 2010 was the CFO of a premium fitness club chain "World Class". From 2010 to 2015 built a successful consumer business. Since 2015-consultant and project leader at The Boston Consulting Group, specializing in operations efficiency in a wide range of industries, as well as government innovation policy.
➡️ [url=https[Suspicious link removed]y-khaliullin-25a43399/]LinkedIn
ANDREY LUKASHEVICH (Advisor). Graduated from the Department of Computational Mathematics and Cybernetics at Lomonosov Moscow State University, MBA at INSEAD Business School. Worked as a partner at Kei-Ei Consulting, Ward Howell International and as an M&A consultant at PwC. 2015-2018 MD, CEO Delivery Club (the largest food delivery service, presented in 97 cities of Russia). Currently CEO of the investment division Mail.ru Foodtech Ventures.
➡️ LinkedIn
I join crypto ecosystem, it's mine skin!
This will make lending as easy as possible.. This is good, LendsBay can't wait to see what you have to offer!!! Truly this medium of loan is good!!!
Where can I take a look at your market research?
Thank you very much for your question!
You can look at our market research in the whitepaper https://lendsbay.io/static/documents/whitepaper_eng.pdf
I see this as a reliable cryptographic trading system using Blockchain technology. Using this platform will create a new approach to loans between lenders and borrowers. All procedures are based on smart contracts. High security, fast transaction speed, low transaction fees and special interest rates are lower than banks ... Surely there will be a lot of investors pay attention. But I still worry about the specific solution for the borrower to repay?
Thank you very much for your comment. The borrower credit rating within the Lendsbay ecosystem will indicate his/her ability to repay based on traditional credit costing data, relatively new type of data sources such as telecoms and internet search engines, as well as proprietary proximity rating. Although no one can guarantee the 100% loan repayment, we plan to propose to LendsBay users insurance and diversification mechanisms, on top of the credit rating.
With your system you have to start somewhere. Will your database start from scratch or where will you pull data from in the beginning to get the system going?
It is hardly possible to create borrower risk evaluation model using external databases. Every credit business and platform has unique client profile. This fact causes different dependencies and results in different set of meaningful variables for scoring models. We plan to start with expert best practice risk estimation and limit models based on 30+ years experience in retail/personal loans risk assessment. It will also be helpful to use Credit bureau, TelCo and Internet companies data from beginning. As soon as we collect our own information we will develop proprietary scoring models, including proximity based scoring
At this point in time when there are no or little credit facilities on blockchains, I think the idea behind the project is spectacular..
Just joined your airdrop campaign and I am looking forwards to be a part of your great project.
Goodluck !
Goodluck !
we need credit system on blockchain, I'm in
LendsBay Credit ecosystem for trusted financial transactions between people based on transparency
and blockchain technology. I believe in this project and I see its a reliable platform, using this platform will bring a good working relationship between lenders and borrowers.
and blockchain technology. I believe in this project and I see its a reliable platform, using this platform will bring a good working relationship between lenders and borrowers.
I see this as a reliable cryptographic trading system using Blockchain technology. Using this platform will create a new approach to loans between lenders and borrowers. All procedures are based on smart contracts. High security, fast transaction speed, low transaction fees and special interest rates are lower than banks ... Surely there will be a lot of investors pay attention. But I still worry about the specific solution for the borrower to repay?
Thank you very much for your comment. The borrower credit rating within the Lendsbay ecosystem will indicate his/her ability to repay based on traditional credit costing data, relatively new type of data sources such as telecoms and internet search engines, as well as proprietary proximity rating. Although no one can guarantee the 100% loan repayment, we plan to propose to LendsBay users insurance and diversification mechanisms, on top of the credit rating.
With your system you have to start somewhere. Will your database start from scratch or where will you pull data from in the beginning to get the system going?
this project will make our life easier in the future!
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