Populous
Populous
Token: PPT


Invoice Finance on the Blockchain

ICO dates
Start date: 2017-07-24
End date: 2017-07-24

Registrated in: UK

Platform: Ethereum

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Contents
1.
Abstract
3
2.
Introduction
4
3.
Extensible Business Reporting Language “XBRL”
5
3.1 Using XBRL in targeted client acquisition
6
3.2 Understanding the analysis
9
3.3 Using XBRL in conjunction with bankruptcy credit formulas
9
4.
Altman Z-score Formula
11
4.1 Accuracy and effectiveness
11
4.2 Original Z-score component definitions variable definition
12
4.2.1 Z-score estimated for private firms
12
4.2.2 Z-score estimated for non-manufacturers & emerging markets 12
5.
Smart Contracts
13
5.1 Actors
14
5.2 System modules
14
5.3 Platform interactions
15
5.4 Invoice auctions
16
5.5 Bidding on auctions
17
5.6 Wallets
18
5.6.1 Flow of funds
18
5.6.2 Deposit of funds
18
5.6.3 Withdrawal of funds
19
6.
Incentive
20
7. ...

1.
Abstract
Small and medium sized enterprises (SMEs) are always in need of short-term financing
especially when there is a sudden and immediate need for increased working capital to
fund wages or the purchase of raw materials. They will very often take out short-term
loans from their bank which is not the ideal solution and the short-term finance industry
is dominated by banks and other lending institutions such as the traditional invoice
finance companies. However, newer and more innovative P2P (peer-to-peer) invoice
finance platforms have recently entered the industry.
These P2P invoice financing platforms operates in the same manner as the traditional
invoice financing companies by providing short term liquidity on invoices for short
durations of up to 90 days. Rather than waiting for their customers to settle invoices that
have due dates of 45 to 90 days, the invoices can be sold to invoice financing companies
to access “immediate” funds. P2P platforms are unique in that the...

2.
Introduction
Keeping a positive cash flow is the most important part for any SME, even more so in an
economy which is currently recovering from a recession. After all, having access to the
monies owed to an SME allows that SME to create new opportunities, develop existing
plans, purchase new equipment, pay salaries and negotiate the best terms with their
suppliers.
Unfortunately, keeping a regular flow of cash in the business is often easier said than
done especially if late payments to the SMEs are holding them back. It is currently
estimated that late payments are costing UK SMEs of more than £2bn a year
(Musaddique, 2017). If an SME is selling its products or services to other businesses on
credit terms, invoice factoring or invoice discounting also known as invoice finance, could
help.
It's a form of funding that releases the cash tied up in an SME’s outstanding sales
invoices instantly at a cost that both the SME and investors agree on. There are currently
over 4...

3.
Extensible Business
Reporting Language “XBRL”
XBRL (eXtensible Business Reporting Language) is a global standard for exchanging
business information and is freely available to anyone. It is developed and published by
XBRL International, Inc. and it is used to define and exchange financial information.
Companies submit financial statements to government regulators each year using the
XBRL reporting format which standardises these data so they can be reviewed and
compared regardless of geographic origin.
HM Treasury made it a requirement to file all annual accounts and corporate tax returns
in XBRL format in April 2011 (GOV.UK, n.d.). Following the announcement of this
requirement, approximately 1.9 million companies now file annual accounts and
corporate tax returns in this format to UK Companies House and HM Revenue and
Customs (XBRL, 2015).
HM Revenue and Customs uses XBRL data to assess annual accounts and tax returns,
helps guide tax risk and policy decisions, j...

UK Companies House made available 6 years of worth of XBRL data at no cost and covers
over 1.9 million UK companies. This data presented a good starting point to analyse past
financial data and forecast credit risks on companies covering a wide range of industries
and sectors. Using these data we built a XBRL back-end to extract approximately over 2.8
billion points of data which formed part of our in-house credit reference system and
targeted marketing database.
3.1
Using XBRL in Targeted Client Acquisition
We have already tested the XBRL data extraction tool using 2012 data sets available
from UK Companies House. The two data sets available for testing were the charge data
and accounting data. Our goal in the test was to assess the validity of the extracted data
to determine whether the data would be useful to selected financial institution’s
customers.
The following analysis results explain how we intend to target clients efficiently and
effectively. This will ultimately lead...

Our findings showed a number of observations favorable to what we wanted to achieve.
We took a deeper look into how the data can provide useful insight using cluster analysis
to give a different approach towards understanding patterns within the data set. In the
cluster analysis, we placed emphasis on three variables which are considered key lending
parameters in the finance industry, namely; debtors, creditors due within one year and
cash bank in hand.
Debtors (£)
Creditors Due Within One Year (£)
Cash Bank Value (£)
1
1,760,305
2
148,924
3
386,104
4
276,045
5
80,631
6
100,455
7
283,543
8
33,178
1,294,833
177,105
321,764
203,015
70,597
134,662
281,284
25,193
157,795
10,154
40,928
4,740
5,589
32,682
14,315
31
Approximately 60 observations were recorded and we selected 8 observations to
d...

Cluster means:
Debtors
1
244,621.7
2
1,760,305.0
3
53,023.1
4
515,789.0
CreditorsDueWithinOneYear
248,586.9
1,294,833.0
71,335.0
479,904.5
CashBankInHand
49,489.11
157,795.00
19,910.06
48,039.67
The number of companies in each cluster were depicted as:
Clusters
BIBBY FINANCIAL SERVICES LIMITED
HSBC BANK PLC
LLOYDS TSB COMMERCIAL FINANCE LIMITED
RBS INVOICE FINANCE LIMITED
1
2
3
4
2
0
6
0
3
1
4
1
8
0
19
3
5
0
2
2
8
...

3.2
Understanding the Analysis
The cluster analysis was very revealing and presented us interesting and useful metrics
of the finance industry. For example, there is a larger concentration of companies in
cluster 3 than in the other clusters. This indicates that most lenders according to the data
set would prefer to lend to companies that have variable values similar to the mean
variable values found in cluster 3.
Furthermore, the cluster analysis for Lloyds was found to contain more customers than
others. This type of analysis would be very useful to a competitor who may want to know
why Lloyds are gaining a larger market share and what level of lending they are
providing to their customers to acquire such a large customer base.
Cluster 2 showed HSBC only targeting the large companies when providing funding. The
analysis reveal a possible strategy worth pursuing in the knowledge that no other lender
were willing to lend to a company of that scale. To a lender with deep pockets, this could...

Populous Roadmap

1
November 2016
Concept development
2
Febuary 2017
White paper publication
3
April 2017
Smart contract development
4
July 2017
Initial coin offering
5
September 2017
XBRL intergration
6
November 2017
Public beta release
Stephen Williams
Stephen Williams Founder & CEO

Zvezdomir Zlatinov
Zvezdomir Zlatinov Solidity Developer

Jason Tuang
Jason Tuang Financial Consultant

Jonathan Millar
Jonathan Millar Specialist Invoice Finance Risk Consultant - Advisor to Populous