Topic: ⚡️⚡️⚡️[ANN] [PRE-ICO] LendsBay | Credit ecosystem - page 84. (Read 7917 times)

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⠀

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.

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

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

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

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.

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.

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

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

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.

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)

TOKEN SALES: TERMS AND CONDITIONS
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

LendsBay team have already developed a functional and ready-to-use alpha version of the application ⠀⠀

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

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.
➡️ LinkedIn

ALEXEY KHALIULLIN (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.
➡️ 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

EVGENY KAPLIN (Advisor). CEO Modultrade. 17+ years in International Trade and Banking business with Strategic consulting experience. Successfully launched and ran a global trade finance platform in one of the top Europe bank. McKinsey, Sberbank, Cargill, Gazprom.
➡️ LinkedIn