Inspeer

Report date: 03 Nov 2017 19:17
Update date: 25 Dec 2017 16:44
Token Lab Index
75%
More in the Methodology
About
A Peer-to-peer lending service that works with Cryptocurrency and Fiat
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Project Score
Name:
Inspeer
Symbol:
INSP
Website:
WhitePaper
15
Domain age
10.08.2017
ICO
Dates:
11 Dec 2017 - 11 Feb 2018
6
Prototype / MVP
iOS:
Yes
10
Android:
Yes
10
Web:
Yes
10
Desktop:
N/A
Team
Successful projects:
Yes
15
Advisors:
Yes, both tech & business
15
Basic members:
Denis Kabanets, Nikolay Otvechalin, Denis Ryabikin
15
External investments
Investments:
No
2
Mentions
First mentioning:
13 Sep 2017
2
Media
Bitcointalk:
5
Coindesk:
N/A
1
Other media links:
5
Community
Blog:
3
GitHub:
N/A
2
Facebook:
Open website
1 803
5
Twitter:
Open website
1 090
4
Telegram:
Open website
3 656
5
Slack:
N/A
CAP
Min:
$2,000,000
10
Max:
$30,000,000
6
Platform
Ethereum
Buy with
ETH, BTC, Fiat
Tokens
Name:
INSP token
Type:
ERC-20
ICO emission:
42,500,000
Total Supply:
50,000,000
Estimated listing date:
TBA
Other information
Law status:
Not defined (token represents the right to receive a part of Inspeer distributable profits)
Addons:
N/A
Comments:
N/A
Collected on pre-sale:
N/A
Collected on sale:
N/A

    Variety of Financial Services

    Currently Inspeer successfully operates as a micro-lending online platform in Russia under LightFin.ru brand. We've built an efficient scoring system, and developed loan distribution pipelines.
    We are starting from Russia and Estonia markets with further expansion to Asia and EU.
    From the very beginning Inspeer will provide loans in cryptocurrencies alongside with fiat. Issuance of virtual cards and credit cards is planned for the close future.
    We are going to develop extended financial institution to support small and middle-sized businesses, and blockchain projects.
    InsCore: Machine Learning Scoring Model

    Lack of reliable scoring is a very common problem faced by majority of P2P lending platforms. To solve this, we introduce InsCore.
    InsCore is a scoring model consisting of 1000 scoring cards for microlending and collateral lending. Thanks to our partners at Scorista, who specialize in machine learning and artificial intelligence, we are able to instantly conduct a detailed analysis on the borrower. We analyze for solvency as well as credibility and reliability of borrowers. The analytical library of the service contains more than 20 000 predictors and variables from traditional and alternative sources.
    OLAF Anti-fraud system

    OLAF is an online system to track users who substitute identification data, this includes the database of devices and computers of such users. We aim to cut down all possible risks for the benefit of our customers. No stone is left unturned.


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