PPT（populous）Populous白皮书.pdf

WHITEPAPER INVOICE FINANCE ON THE BLOCKCHAIN By Steve Nico Williams Populous 2 TABLE OF CONTENTS ABSTRACT .............................................................................................................................. 2 INTRODUCTION ..................................................................................................................... 3 EXTENSIBLE BUSINESS REPORTING LANGUAGE ‘XBRL’......................................................... 4 USING XBRL IN TARGETED CLIENT ACQUISITION .............................................................. 5 UNDERSTANDING THE ANAYLSIS ....................................................................................... 8 USING XBRL IN CONJUNCTION WITH BANKRUPTCY CREDIT ULAS ......................... 8 ALTMAN Z-SCORE ULA ................................................................................................ 9 ACCURACY AND EFFECTIVENESS ....................................................................................... 9 ORIGINAL Z-SCORE COMPONENT DEFINITIONS VARIABLE DEFINITION ........................... 9 Z-SCORE ESTIMATED FOR PRIVATE FIRMS ................................................................... 10 Z-SCORE ESTIMATED FOR NON-MANUFACTURERS EMERGING MARKETS ............. 10 SMART CONTRACTS ............................................................................................................. 11 HOW OUR SMART CONTRACT WORK WITHIN POPULOUS ............................................. 12 ACTORS ......................................................................................................................... 12 SYSTEM MODULES ........................................................................................................ 12 PLAT INTERACTIONS ........................................................................................... 14 INVOICE AUCTIONS....................................................................................................... 15 BIDDING ON AUCTIONS ................................................................................................ 16 WALLET ......................................................................................................................... 16 FLOW OF FUNDS ........................................................................................................... 16 DEPOSIT OF FUNDS ...................................................................................................... 17 WITHDRAWAL OF FUNDS ............................................................................................. 18 CONVERSION OF FUNDS............................................................................................... 18 INCENTIVE............................................................................................................................ 18 CONCLUSION ....................................................................................................................... 19 REFERENCES ........................................................................................................................ 20 3 ABSTRACT Applying for a business loan from the bank is not always an ideal solution for small and medium-sized enterprises SMEs, especially for some businesses that require immediate funding for sud-den working capital increases, wages and short-term investments which have not planned for. While large financial institutions and independent invoice finance companies dominate asset-based lending and factoring, Peer-To-Peer P2P invoice finance plats have recently entered the industry. Similar to traditional invoice finance providers, these plats provide solutions that allow SMEs to get immediate funding on monies owed to them by their customers, rather than waiting for customers to pay invoices within a 45 to 90 day period which usually causes a strain on the cash flow of the SMEs. With the continued rise of P2P lending plats entering the industry, invoice finance marketplaces are becoming more accessible to businesses globally. The total mar-ket size for invoice finance has been growing rapidly and reached over 3 trillion USD worldwide. Not having a deep understanding of credit and underwriting expertise can cause serious financial losses for the P2P plat operators and investors on their plats. General credit and under-writing experience in this industry are often not enough for building a successful and sustainable invoice factoring operation. What we propose is an invoice factoring plat built using XBRL data to create a new type of credit risk system using credit scoring and bankruptcy ulas such as the Altman Z-Score which can be used to per an in-depth credit risk analysis on targeted potential borrowers, linked companies, and their customers. While also providing targeted mar-keting solutions to find borrowers who need invoice finance using s such as K-means clus-ter analysis, while also implementing the use of smart contracts on the plat, we can not only prevent duplicate invoice finance fraud but create a cost effective and efficient solution in oper-ating a business with huge global potential. 4 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 s currently estimated that late payments are costing UK SMEs as much as £1.9 billion a year. 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 of funding that releases the cash tied up in an SME’s outstanding sales invoices instantly at a cost that both the SME and investor agree on. There are currently over 40,000 businesses across the UK using invoice finance to support them at various stages in their business life cycle. 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, 50 of SMEs accounted for the UKs total turnover of £3tn and 46 of SMEs experienced some of cash flow problem and late payment. 5 EXTENSIBLE BUSINESS REPORTING LANGUAGE ‘XBRL’ XBRL is a global standard for exchanging business ination which is freely available. It s also currently used to define and exchange financial ination, such as a company’s financial statements. XBRL allows the expression of semantic meaning commonly required in business reporting. Since the announcement in April 2011 that UK companies are required to file their annual accounts and corporation tax returns in this at to the Companies House and HMRC. Some 1.9 million companies are now successfully filing their financial statements in this at each year. The accounts range from complex ones from large organizations to simple reports from small companies. They vary significantly in at and presentation, since they are filed under principle-based accounting standards which do not prescribe the layout of accounts. HMRC uses XBRL data to assess accounts and tax returns, help guide tax risk and policy decisions, judge the consequences of legal challenges and gain a better understanding of the business population. It says that XBRL filing has been extremely successful With the UK’s Companies House making 6 years of XBRL data freely available for over 1.9 million UK companies. We have a good starting point to analyzing past financial data and forecast credit risk on companies over various different in industries and sectors. Before this can be attempted we developed a of extracting the XBRL data from its current document into our database, this has given us roughly over 2.8 billion points of data yearly which is updated daily as soon as a company files their account to the Companies House for which enables us to per credit risks analysis. 6 USING XBRL IN TARGETED CLIENT ACQUISITION Below we have combined two data sets the first is 2012 charge data, taken from the Companies House and the second is 2012 accounting data extracted from XBRL data, also taken from the Companies House. Our goal today is to see how XBRL data will prove to be valuable by determining how the selected financial institution s customers are targeted and grouped. The results of this analysis should explain how we intend target clients efficiently and effectively, which ultimately will lead to more SMEs obtaining invoice finance and of course an increase in our revenue model and for investors funding invoice on the plat. Variables accounted for in this analysis are With the combined data set in hand and with a favorable number of observations, we can now take a deeper look into how the data can provide useful insight. Using cluster analysis we are given a number of different approaches towards understanding patterns within any given data set 7 We will focus our analysis on these three variables as our prime concern for each company under the influence of a financial institution. We are using approximately 60 observations. The above record is an example of 8 observations taken from the record. A very reliable way to do this is to group the companies in separate clusters. Each cluster represents the four financial institutions we have used for this analysis. The financial institutions concerned here are as follows The Clustering algorithm we have used here is called the K-Means Algorithm, which has been statistically implemented on the dataset using the R Programming Language. The objective is to clusters on the basis of common behavior between the companies in focus from the three variables namely 1. Debtors, 2. Creditors Due within One Year and 3. Cash Bank in Hand. The output will produce 4 clusters. K-means clustering with 4 clusters of sizes 18, 1, 31, 6 8 Cluster means The number of companies in each cluster can be depicted as 9 UNDERSTANDING THE ANAYLSIS After conducting this rcise we can clearly see some interesting and useful insights from the analysis. For example, there is a larger concentration of companies in cluster 3 than in the other cluster. This indicates that most lenders according to the data set would prefer to lend to compa-nies that have variable values similar to the mean variable values found in cluster 3. Also, still looking at cluster 3, we can see from the analysis that Lloyds TSB Commercial Finance had the most customers in that cluster. This type of analysis would be very useful to a competitor who may wish 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. In cluster 2, we can see that only HSBC targeted the largest company in the analysis. This could be a strategy worth pursuing knowing that no other lender was 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. Furthermore, a lender armed with this sort of analysis could easily target those companies which have been more profitable to them in the past and stay ahead of the com-petition. The lender could also use the ination derived to put strategies in place to take busi-ness from competitors, or even corner a relatively young but strongly going sector of the asset based lending industry. Using the K-means cluster analysis can the basis on which a company can be objectively parameterized, as it will also the groundwork for further analysis, for ex-ample, whether the company is borrowing money greater than its peers within the same industry. USING XBRL IN CONJUNCTION WITH BANKRUPTCY CREDIT ULAS Being able to extract over 1500 data points per company is a game changer. This gives us a great opportunity to analyse not only the credit risk of a company in question or their trading partners but the industry as a whole. XBRL data is ted daily by companies to Companies House and updated on our system instantly, creating a real-time insight to how the UK economy is pre-ing. By using XBRL data in conjunction with different ulas such as the Altman Z-Score ula has allowed us to some extent to ineffectively create our own in house crediting rating system more advanced than the current industry standard. 10 ALTMAN Z-SCORE ULA The Z-score ula for predicting bankruptcy was published in 1968 by Edward I. Altman, who was, at the time, an Assistant Professor of Finance at New York University. The ula may be used to predict the probability that a firm will go into bankruptcy within two years. Z-scores are used to predict corporate defaults and an easy-to-calculate control measure for the financial distress status of companies in academic studies. The Z-score uses multiple corporate income and balance sheet values to measure the financial health of a company. ACCURACY AND EFFECTIVENESS In its initial test, the Altman Z-Score was found to be 72 accurate in predicting bankruptcy two years before the event, with a Type II error false negatives of 6 Altman, 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 in predicting bankruptcy one year before the event, with a Type II error classifying the firm as bankrupt when it does not go bankrupt of approximately 15–20 Altman, 2000. From about 1985 onwards, the Z-scores gained wide acceptance by auditors, management accountants, courts, and database systems used for loan uation. The ula 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 companies balance sheets and their frequent use of off-balance sheet items. ORIGINAL Z-SCORE COMPONENT DEFINITIONS VARIABLE DEFINITION NOTE The use of “ / “ is a stand-in for division ÷ X1 Working Capital / Total Assets X2 Retained Earnings / Total Assets X3 Earnings Before Interest and Taxes / Total Asset