pCONTACTSteve Williams, CEOADDRESS2004, 16 Great Chapel Street, London W1F 8FL,United KingdomINVOICE FINANCINGON BLOCKCHAIN2017WhitepaperDec 2017ContentsAbstract1.3Extensible Business Reporting Language “XBRL”2.53.4Introduction3.1 Using XBRL in targeted client acquisition3.2 Understanding the analysis3.3 Using XBRL in conjunction with bankruptcy credit ulas699Altman Z-score ula 114.4.1 Accuracy and effectiveness4.2 Original Z-score component definitions variable definition4.2.1 Z-score estimated for private firms4.2.2 Z-score estimated for non-manufacturers amp; emerging markets11121212Smart Contracts 135.5.1 Actors5.2 System modules5.3 Plat interactions5.4 Invoice auctions5.5 Bidding on auctions5.6 Wallets5.6.1 Flow of funds5.6.2 Deposit of funds5.6.3 Withdrawal of funds141415161718181819Incentive6.20Conclusion 217.References 22Small 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. nbsp;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. nbsp;However, newer and more innovative P2P peer-to-peer invoice finance plats have recently entered the industry.These P2P invoice financing plats 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. nbsp;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. nbsp;P2P plats are unique in that they connect invoice sellers directly with invoice buyers making the rise of P2P as an alternative lending plat more attractive to businesses globally. The global invoice financing market was valued 3 Trillion in 2013 and due to a slow-down in the world economy the invoice financing market experienced a slight contraction to approximately 2.6 Trillion in 2016. nbsp;The business environment has become more challenging and making it a more favorable environment for new fintech start-up like Populous. nbsp;However, to operate in this industry without a deep understanding of credit and underwriting principle can result in serious financial loss for the company as well as investors. nbsp;Our in-depth knowledge and expertise in the short-term finance industry allows us to build a P2P invoice financing plat using credit scoring and bankruptcy ula such as the Altman Z-score. nbsp;We will also identify potential borrowers using K-means cluster analysis.When these ulas are combined with the XBRL data set we can per “enhanced” credit risk analysis on targeted potential borrowers, linked companies and their customers. nbsp; Using Blockchain technology we can leverage smart contract to create a cost effective and efficient solution by providing a streamlined funding solution to businesses. nbsp;Blockchain technology also affords some security against fraud and can prevent duplication in the selling of invoices.3Abstract1.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. It39;s a 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 40,000 businesses across the UK using invoice finance to support nbsp;them at various stages in their business life cycle Business Comparison, n.d.. Furthermore, there are businesses across the UK at this moment using this of finance particularly at a time when more traditional financial institutions have been turning down funding requests. As of 2016, approximately 50 of SMEs accounted for the UKs total turnover White, 2015 of 3 Trillion and 46 of SMEs experienced some of cash flow problem and late payment Lobel, 2015.4Introduction2.XBRL eXtensible Business Reporting Language is a global standard for exchanging business ination and is freely available to anyone. It is developed and published by XBRL International, Inc. and it is used to define and exchange financial ination. nbsp;Companies financial statements to government regulators each year using the XBRL reporting at 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 at in April 2011 GOV.UK, n.d.. nbsp;Following the announcement of this requirement, approximately 1.9 million companies now file annual accounts and corporate tax returns in this at to UK Companies House and HM Revenue and Customs XBRL, 2015. nbsp;HM Revenue and Customs uses XBRL data to assess annual accounts and tax returns, helps guide tax risk and policy decisions, judge the consequences of legal challenges and gain a better understanding of the business. The government has indicated that the implementation of XBRL filing have been extremely successful. 5Extensible BusinessReporting Language “XBRL”3.Import or manual typing of Bookkeeping records into MS ExcelPrime recordsAdjustments to arrive at Final accounts from Trial balance using MS ExcelTrial balanceatting of Final accounts in MS Excel or MS WordFinal accounts iXBRL accountsManual tagging of accounts in iXBRLCSVTaggingsoftwareiXBRLTagged accountsWe have already tested the XBRL data extraction tool using 2012 data sets available from UK Companies House. nbsp;The two data sets available for testing were the charge data and accounting data. nbsp;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. nbsp;This will ultimately lead to more SMEs obtaining invoice finance from us and resulting in corresponding increase in our revenue model and hence increase in liquidity for funding invoices on the plat. Variables accounted for in this analysis are63.1 nbsp; Using XBRL in Targeted Client Acquisition Company NumberCompany NameSIC Code - 78109DebtorsCreditors Due within 1 YearCash Bank in HandPerson Entitled to the ChargeDescription of ChargeCompany registration numberName of the companySIC code - activities of employment placement agenciesDebtor’s valueCreditors who the business has to pay money for goods orservices or loans within a yearCash in hand or at the bankBank/person who lent the company money or took out thecharge on the companyType of charge registeredUK Companies House made available 6 years of worth of XBRL data at no cost and covers over 1.9 million UK companies. nbsp;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. nbsp;Using these data we built a XBRL back-end to extract approximately over 2.8 billion points of data which ed part of our in-house credit reference system and targeted marketing database. nbsp;Approximately 60 observations were recorded and we selected 8 observations to demonstrate our finding in the cluster analysis. nbsp;The observations were further broken down into 4 clusters to represent the four financial institutions we have used for this analysis. nbsp;We considered the following financial institution in the cluster analysisThe clustering algorithm used to carry out this analysis is called the K-means Algorithm and which have been statistically implemented on the dataset using the R-Programming Language Macqueen, 1967. The objective was to clusters on the basis of common behavior between the companies being considered based on the three key variables.The K-means clustering output gave cluster sizes 18, 1, 31 and 6 for the financial institutions considered.Our findings showed a number of observations favorable to what we wanted to achieve. nbsp;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. nbsp;7123456781,760,305148,924386,104276,04580,631100,455283,54333,1781,294,833177,105321,764203,01570,597134,662281,28425,193157,79510,15440,9284,7405,58932,68214,31531Debtors nbsp; Creditors Due Within One Year nbsp; Cash Bank Value BIBBY FINANCIAL SERVICES LIMITEDHSBC BANK PLCLLOYDS TSB COMMERCIAL FINANCE LIMITEDRBS INVOICE FINANCE LIMITEDThe number of companies in each cluster were depicted as8Debtors CreditorsDueWithinOneYear CashBankInHand244,621.71,760,305.053,023.1515,789.0248,586.91,294,833.071,335.0479,904.549,489.11157,795.0019,910.0648,039.671234ClustersBIBBY FINANCIAL SERVICES LIMITEDHSBC BANK PLCLLOYDS TSB COMMERCIAL FINANCE LIMITEDRBS INVOICE FINANCE LIMITED123852010036419240132Cluster meansThe 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 prove to be a perfect strategy if cuted correctly in a growing economy. A lender armed with this sort of analysis can very quickly and easily identify the type and size of companies needing finance, and most importantly its competitors’ strategies. This ination can the basis to ulate strategies to grow market share and take business from competitors. nbsp;Used Strategically, you can even dominate a relatively young but strongly growing sector of the asset based lending industry even before it appears on the radar of your competitors. The K-means cluster analysis can the basis on which a company can be objectively parameterized. It will also the groundwork for further analysis, for example, whether the company is borrowing money greater than its peers within the same industry. The ability to extract over 1500 data points from the XBRL data set on each company is a game changer and this gives us a great opportunity to analyse the credit risk of a company in question, their trading partners as well as the whole industry. nbsp;XBRL data ted daily by companies to UK Companies House are updated on our system instantly, creating a real-time insight to how the UK economy is pering.93.2 nbsp; Understanding the Analysis3.3 nbsp; Using XBRL in Conjunction with Bankruptcy Creditulas The combination of extracted XBRL data and the Altman Z-Score ula have not only allowed us to bypassed the need to use an external credit reference agency, but have also allowed us to gain a technological and financial edge over our competitors. nbsp;We have effectively created our own in-house credit rating system which is more advanced than the current industry standard.10The Z-score ula published in 1968 by Edward I. Altman is a standard ula used globally in the financial industry. The ula provides three predictive measures the probability that a business will go into bankruptcy within two years, whether a business will default on obligations, a control measure for financial distress. The Z-score uses multiple corporate income and balance sheet values to measure the financial health of a company.11Altman Z-score ula 4.In its initial test, the Altman Z-Score was found to be 72 accurate nbsp;in predicting bankruptcy two years before the event, with a Type II error false negatives nbsp;of 6 Altman E. I., 1968. In a series of subsequent tests covering three periods over the next 31 years up until 1999, the model was found to be approximately 80–90 accurate nbsp;in predicting bankruptcy one year before the event, with a Type II error classifying the firm as bankrupt nbsp;when it does not go bankrupt of approximately 15–20 Altman E. , 2000.From about 1985 onwards, the Z-scores gained wide acceptance by auditors, management accountants, courts, and database systems used for loan uation. The ula39;s approach has been used in a variety of contexts and countries, although it was designed originally for publicly held manufacturing companies with assets of more than 1 million. Later variations by Altman were designed to be applicable to privately held companies the Altman Z-score and non-manufacturing companies the Altman Z-score. Neither the Altman models nor other balance sheet-based models are recommended for use with financial companies. This is because of the opacity of financial companies39; balance sheets nbsp;and their frequent nbsp;use of off-balance nbsp;sheet nbsp;items.4.1 nbsp; Accuracy and Effectiveness12Z gt; 2.991.81 nbsp;2.91.23 nbsp;2.61.1 lt; Z lt; 2.6Z lt; 1.1- “Safe” nbsp;Zone- “Gray” nbsp;Zone- “Distress” nbsp;ZoneZ-score bankruptcy model Emerging MarketsZones of discriminationZ 3.25 6.56X1 3.26X2 6.72X3 1.05X4.4.2.2 nbsp; Z-score Estimated for Non-manufacturers amp; Emerging MarketsX1 ;Current Assets − Current LiabilitiesTotal AssetsX3 ;Earnings Before Interest and TaxesTotal AssetsX4 .Book Value of EquityTotal LiabilitiesX2 Retained EarningsTotal Assets;Z-score bankruptcy model Z 6.56X1 3.26X2 6.72X3 1.05X4[4].4.2 nbsp; Original Z-score Component Definitions VariableDefinition X1 ;Working CapitalTotal Asse/p