LendsBay - Credit ecosystem.
Token Count
1 LBT = 0.001 ETH


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.

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 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:

  • 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.
  • 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.
  • Creating a blockchain to store individuals' credit-rating information (LBR)
  • Using the blockchain for any financial transactions between individuals (non-credit assessment) (LBU)
  • 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


  • 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


  • 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:


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.


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.


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



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.


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.


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.


Founder, СRO

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


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.


  • 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
    • Сonnecting 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

White Paper

Investment Info
Accepting ETH
Token Info
Token LBT
Platform Ethereum
Type ERC20
Token Price 1 LBT = 0.001 ETH
Token Count 100,000,000
Pre-Sale Start Date 2018-June-18
Pre-Sale End Date 2018-July-29
Crowd Sale Start Date 2018-September-18
Crowd Sale End Date 2018-October-29

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