This statistic depicts the distribution of invoices' payment periods among medium-sized enterprises (SMEs) in Italy during the second quarter of 2020. According to the source, 63.3 percent of medium-sized companies took between 30 and 90 days to pay their invoices, while only 17.4 paid their invoices within 30 days.

List of all suppliers with invoices in 2020, detailing the number of invoices received, the sum of the total amount and the sum of the total paid up to the current date.

This statistic depicts the share of unpaid invoices among small and medium-sized enterprises (SMEs) in Italy in the second quarter of 2020, by company size. According to the figures, the share of unpaid invoices was larger among small-sized enterprises (19.8 percent) than among medium-sized enterprises (17.4 percent).

From October 2021 to **** November 2021, the total number of E-invoices of goods and service tax (GST) was **** billion in India. GST e-invoicing has helped in better authentication, standardization and implementation of GST across the country.

The publication includes the City of Hämeenlinna’s external purchases by invoice line for 2020, with the exception of lines restricted by data protection. In practice, accounts that are protected by the Personal Data Act have been cut off from the files.

In the material, the account describes the purpose of the purchase and the cost location of the buyer in the city. The supplier is the seller of a product or service that has received payment. Invoices are net prices excluding VAT. The cost location information in the recording plan, including different levels, can be attached to the data by using the number of the cost point.

The data includes purchases of services, materials, supplies and goods by the municipality of Siilinjärvi, as well as purchases included in the accounting groups Rental expenses and Other operating expenses in the municipal accounts. Invoices paid to private individuals and corporations formed by private individuals without a business ID have been removed from the data. Other invoices without a Business ID (foreign journalists, associations and roads without a Business ID) have been left in the data. Due to the limitations, the amounts cannot be compared with the financial statements. The material has been published in accordance with the instructions for opening purchase invoice data of municipalities and joint municipal boards published by the Association of Finnish Local and Regional Authorities, where applicable. For technical reasons, the information does not include the service classification, and the accounting account numbers and names are in accordance with the municipality's own accounting chart, contrary to what has been issued by decree. The data have been extracted from the municipality's accounts payable. Debit invoices are shown as positive values in the data. Negative values indicate that this is a credit note. The invoice includes the gross amount, the tax-exempt amount and the tax component. Compared to the 2021 data, in the 2020 data, each invoice is on a single line, and an attempt has been made to select the account to which most of the invoices have been allocated, as well as the VAT code.

Information on Umeå municipality's incoming supplier invoices for 2020 are collected in this data set. The data is presented in tabular form and contains invoice month, departmant, supplier, organisation number, account, account text, amount (exkl. VAT) and verification number. Some data is hidden due to GDPR. No invoice images are presented. The information can be downloaded in file format CSV, JSON and Excel.We can produce and send invoice copies by mail (not e-mail/digital) as desired.Contact us with the verification number that you want copies of, preferably in excel format.Send your request to leverantorsgruppen@umea.sePage 1-9; Free.Page 10; 50 krPage 11--; 2 kr / pagePostage is added. No VAT is charged.For extracting invoice copies, we need your billing information and the address to which the invoice copies are to be sentOrganisation number Name Customer reference Address Zip code City

This statistic depicts the length of time it takes law firms worldwide to send out an invoice in 2019 and in 2020. During the 2019 survey, 24 percent of respondents stated it takes their firm about one to two weeks to send client invoices. In 2020, the share of respondents who gave the same answer increased, reaching 30.5 percent.

Invoices paid by the Rail Inspection Organisation (Transport Department) in 2020

 Primary Parcel file containing primary owner and land information; Addn file containing drawing vectors for dwelling records; Additional Address file containing any additional addresses that exist for a parcel; Assessment file containing assessed value-related data; Appraisal file containing appraised value-related data; Commercial file containing primary commercial data; Commercial Apt containing commercial apartment data; Commercial Interior Exterior data Dwelling file Entrance data containing data from appraisers' visits; Other Buildings and Yard Improvements Sales File Tax Rate File for the current billing cycle by taxing district authority and property class; and, Tax Payments File containing tax charges and payments for current billing cycle.In addition to the CSV files, the following are included: Data Dictionary PDF; and, St Louis County Rate Book for the current tax billing cycle.

This statistic depicts the share of unpaid invoices among small and medium-sized enterprises (SMEs) in Italy in the second quarter of 2020, broken down by macro sector. According to the figures, the largest share of unpaid invoices was registered among companies in the agricultural sector (**** percent), whereas the share of unpaid invoices among enterprises from the industrial sector accounted for 12.7 percent.

Invoices 1st half of 2020

The leading European countries based on market volume in invoice trading in 2020 were Italy, the United Kingdom, Spain, and France. In that year, the total market volume in Italy was worth approximately *** million U.S. dollars. Invoice trading is a type of auction financing which involves businesses selling their unpaid invoices to online investors through an online peer-to-peer platform. At its core, this concept takes the principle of peer-to-peer lending and applies it to invoice finance. An SME would sell an unpaid invoice to a group of investors, who provide an advantage of that invoice. When the invoice is paid, the SME receives its remaining part with the group of investors keeping the rest (minus a fee for the online peer-to-peer platform). This system theoretically allows SMEs faster access to money from unpaid invoices.

This is a collection of CSV files that contain personal property assessment data. In addition to the CSV files, the following are included: Data Dictionary PDF; and, St Louis County Rate Book for the current tax billing cycle.

This dataset includes municipal reporting information for the Municipal Coronavirus Relief Fund (CRF) Program for Connecticut municipalities from July 1, 2020 - December 31, 2021. This dataset includes invoice transactions; payroll expenditures for this reporting period are published in a separate dataset available here: https://data.ct.gov/d/huqw-chzi/ Municipal expenditures from the previous reporting period (March 1 - June 30, 2020) are available here: https://data.ct.gov/d/3vh2-vbmk The CRF Program was established by the CT Office of Policy and Management (OPM) to reimburse municipalities for costs incurred in responding to the COVID-19 pandemic with funds from the Federal Coronavirus Aid, Relief, and Economic Security Act (CARES Act). More information about the CRF Program can be found here: https://portal.ct.gov/OPM/Coronavirus/Coronavirus-Relief-Fund/Municipal-CRF-Program

In 2023, Polish factoring companies purchased over ** million invoices, which is an increase of more than ** percent compared to the previous year.

This dataset contains links to the Datasets of Bills Signed by Governor Brown in 2020.

Business process event data modeled as labeled property graphs

Data Format
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The dataset comprises one labeled property graph in two different file formats.

#1) Neo4j .dump format

A neo4j (https://neo4j.com) database dump that contains the entire graph and can be imported into a fresh neo4j database instance using the following command, see also the neo4j documentation: https://neo4j.com/docs/

/bin/neo4j-admin.(bat|sh) load --database=graph.db --from=

The .dump was created with Neo4j v3.5.

#2) .graphml format

A .zip file containing a .graphml file of the entire graph


Data Schema
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The graph is a labeled property graph over business process event data. Each graph uses the following concepts

:Event nodes - each event node describes a discrete event, i.e., an atomic observation described by attribute "Activity" that occurred at the given "timestamp"

:Entity nodes - each entity node describes an entity (e.g., an object or a user), it has an EntityType and an identifier (attribute "ID")

:Log nodes - describes a collection of events that were recorded together, most graphs only contain one log node

:Class nodes - each class node describes a type of observation that has been recorded, e.g., the different types of activities that can be observed, :Class nodes group events into sets of identical observations

:CORR relationships - from :Event to :Entity nodes, describes whether an event is correlated to a specific entity; an event can be correlated to multiple entities

:DF relationships - "directly-followed by" between two :Event nodes describes which event is directly-followed by which other event; both events in a :DF relationship must be correlated to the same entity node. All :DF relationships form a directed acyclic graph.

:HAS relationship - from a :Log to an :Event node, describes which events had been recorded in which event log

:OBSERVES relationship - from an :Event to a :Class node, describes to which event class an event belongs, i.e., which activity was observed in the graph

:REL relationship - placeholder for any structural relationship between two :Entity nodes

The concepts a further defined in Stefan Esser, Dirk Fahland: Multi-Dimensional Event Data in Graph Databases. CoRR abs/2005.14552 (2020) https://arxiv.org/abs/2005.14552


Data Contents
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neo4j-bpic19-2021-02-17 (.dump|.graphml.zip)

An integrated graph describing the raw event data of the entire BPI Challenge 2019 dataset.
van Dongen, B.F. (Boudewijn) (2019): BPI Challenge 2019. 4TU.ResearchData. Collection. https://doi.org/10.4121/uuid:d06aff4b-79f0-45e6-8ec8-e19730c248f1

This data originated from a large multinational company operating from The Netherlands in the area of coatings and paints and we ask participants to investigate the purchase order handling process for some of its 60 subsidiaries. In particular, the process owner has compliance questions. In the data, each purchase order (or purchase document) contains one or more line items. For each line item, there are roughly four types of flows in the data: (1) 3-way matching, invoice after goods receipt: For these items, the value of the goods receipt message should be matched against the value of an invoice receipt message and the value put during creation of the item (indicated by both the GR-based flag and the Goods Receipt flags set to true). (2) 3-way matching, invoice before goods receipt: Purchase Items that do require a goods receipt message, while they do not require GR-based invoicing (indicated by the GR-based IV flag set to false and the Goods Receipt flags set to true). For such purchase items, invoices can be entered before the goods are receipt, but they are blocked until goods are received. This unblocking can be done by a user, or by a batch process at regular intervals. Invoices should only be cleared if goods are received and the value matches with the invoice and the value at creation of the item. (3) 2-way matching (no goods receipt needed): For these items, the value of the invoice should match the value at creation (in full or partially until PO value is consumed), but there is no separate goods receipt message required (indicated by both the GR-based flag and the Goods Receipt flags set to false). (4)Consignment: For these items, there are no invoices on PO level as this is handled fully in a separate process. Here we see GR indicator is set to true but the GR IV flag is set to false and also we know by item type (consignment) that we do not expect an invoice against this item. Unfortunately, the complexity of the data goes further than just this division in four categories. For each purchase item, there can be many goods receipt messages and corresponding invoices which are subsequently paid. Consider for example the process of paying rent. There is a Purchase Document with one item for paying rent, but a total of 12 goods receipt messages with (cleared) invoices with a value equal to 1/12 of the total amount. For logistical services, there may even be hundreds of goods receipt messages for one line item. Overall, for each line item, the amounts of the line item, the goods receipt messages (if applicable) and the invoices have to match for the process to be compliant. Of course, the log is anonymized, but some semantics are left in the data, for example: The resources are split between batch users and normal users indicated by their name. The batch users are automated processes executed by different systems. The normal users refer to human actors in the process. The monetary values of each event are anonymized from the original data using a linear translation respecting 0, i.e. addition of multiple invoices for a single item should still lead to the original item worth (although there may be small rounding errors for numerical reasons). Company, vendor, system and document names and IDs are anonymized in a consistent way throughout the log. The company has the key, so any result can be translated by them to business insights about real customers and real purchase documents.

The case ID is a combination of the purchase document and the purchase item. There is a total of 76,349 purchase documents containing in total 251,734 items, i.e. there are 251,734 cases. In these cases, there are 1,595,923 events relating to 42 activities performed by 627 users (607 human users and 20 batch users). Sometimes the user field is empty, or NONE, which indicates no user was recorded in the source system. For each purchase item (or case) the following attributes are recorded: concept:name: A combination of the purchase document id and the item id, Purchasing Document: The purchasing document ID, Item: The item ID, Item Type: The type of the item, GR-Based Inv. Verif.: Flag indicating if GR-based invoicing is required (see above), Goods Receipt: Flag indicating if 3-way matching is required (see above), Source: The source system of this item, Doc. Category name: The name of the category of the purchasing document, Company: The subsidiary of the company from where the purchase originated, Spend classification text: A text explaining the class of purchase item, Spend area text: A text explaining the area for the purchase item, Sub spend area text: Another text explaining the area for the purchase item, Vendor: The vendor to which the purchase document was sent, Name: The name of the vendor, Document Type: The document type, Item Category: The category as explained above (3-way with GR-based invoicing, 3-way without, 2-way, consignment).

The data contains the following entities and their events

- PO - Purchase Order documents handled at a large multinational company operating from The Netherlands
- POItem - an item in a Purchase Order document describing a specific item to be purchased
- Resource - the user or worker handling the document or a specific item
- Vendor - the external organization from which an item is to be purchased

Data Size
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BPIC19, nodes: 1926651, relationships: 15082099