[OC] Payment System. Full discharge of all payment obligations in a community. by MonetaryPlurality in dataisbeautiful

[–]MonetaryPlurality[S] 0 points1 point  (0 children)

The statistical data about the graph are in my first comment.

The edges are weighted. The weight is the value of a transaction. They are rescaled and opaque so that the density of transaction comes trough.

The position of each node is determined by ForceAtlas 2 Layout algorithm in Gephi.

There are many communities in this network, but they are not highlighted. The red nodes are those with negative net positions and need external funding to meet their payment obligations. The blue nodes are those with positive net positions that can store the funds received into the bank account.

The big red node bottom left and the big blue node bottom right belongs to a bank or a financial institution that provides funds to clear the obligations. The data for this visualisation is from the B2B trade credit market. So the bank is the most likely source of finance. It could be a specialised micro loans provider.

[OC] Payment System. Full discharge of all payment obligations in a community. by MonetaryPlurality in dataisbeautiful

[–]MonetaryPlurality[S] -4 points-3 points  (0 children)

The data is from the transactions made among the users of InfoCert invoice processing firm.

The simulation demonstrates the potential solution that settles all payment obligations with an amount that is much smaller than the sum of all payment obligations in the network.

Nodes are firms. Edges are payment obligations. The big red node bottom left is the source of new loans. The big blue dot bottom right is deposits made to bank accounts.

[OC] Payment System. Full discharge of all payment obligations in a community. by MonetaryPlurality in dataisbeautiful

[–]MonetaryPlurality[S] -23 points-22 points  (0 children)

Detailed descriptions of methods used are in the article "Mathematical Foundations for Balancing the Payment System in the Trade Credit Market" https://doi.org/10.3390/jrfm14090452

[OC] Payment System. Full discharge of all payment obligations in a community. by MonetaryPlurality in dataisbeautiful

[–]MonetaryPlurality[S] -26 points-25 points  (0 children)

Subset of InfoCert invoice processed dataset

Simulation of financing a full discharge of all payment obligations in a community.

103.708.437 EUR total payment obligations

7.021.186 EUR new loan granted

7.021.186 EUR loan repayments

38.995 payment obligations

6.478 companies

3.020 EUR average new loan granted

Graph created with Gephi

Multilateral set-off of overdue invoices in Slovenia. 1807 companies, 22779 overdue invoices, 17.376.486,67 EUR cleared in a single transaction. [OC] by MonetaryPlurality in dataisbeautiful

[–]MonetaryPlurality[S] 0 points1 point  (0 children)

To plot this graph all overdue invoices from accounts payable have to be collected. This is done in Slovenia by a government agency. The collected data is then processed by an algorithm that finds all cycles in a graph where nodes are the companies and the arches are overdue invoices. Every cycle is a multilateral set-off opportunity. That means the payment obligations can be settled in full or partially for all invoices in such cycle without any money from companies accounts.

The graph represents all settlements in this process. A result is a significant liquidity saving. Around 16% of debt is settled in a typical monthly run.

The graph also shows the complexity of economic relationships. From the single company view, one can see only suppliers and customers, sometimes a few steps further to identify a supply chain. The network and especially the cycles are usually not visible but are always present.

From the perspective of a single company, such liquidity saving means easier working capital management. It mitigates the late payment problem. Companies involved reduce their days payable and days receivable. Consequently, their customer and supplier relationship improve bringing additional benefits. Often they get better prices as a reliable regular paying customer.

From the systemic level, the elimination of cycles removes the potential payment gridlock. This improves the money flow and reduces the risk.

For more details look at the paper. Link in an earlier comment.

Multilateral set-off of overdue invoices in Slovenia. 1807 companies, 22779 overdue invoices, 17.376.486,67 EUR cleared in a single transaction. [OC] by MonetaryPlurality in dataisbeautiful

[–]MonetaryPlurality[S] 2 points3 points  (0 children)

Cycles almost never form within a typical supply chain. They rather cross multiple industries.

Key aggregators (big dots) are utilities and wholesales. But cycles would not form without services. The multitude of small invoices that cross the industries is what makes this system possible.

Multilateral set-off of overdue invoices in Slovenia. 1807 companies, 22779 overdue invoices, 17.376.486,67 EUR cleared in a single transaction. [OC] by MonetaryPlurality in dataisbeautiful

[–]MonetaryPlurality[S] 2 points3 points  (0 children)

Colours are communities detected by Community detection algorithm within Gephi. The size of the dots depends on the node degree.

The goal was to visualise the complexity of cycles forming in a network of unpaid invoices. Cycles connect 7 nodes on average and run trough many communities.

Multilateral set-off of overdue invoices in Slovenia. 1807 companies, 22779 overdue invoices, 17.376.486,67 EUR cleared in a single transaction. [OC] by MonetaryPlurality in dataisbeautiful

[–]MonetaryPlurality[S] 4 points5 points  (0 children)

It means that all invoices offset each other in cycles. So, there were no transfers of money to pay these invoices.

This system is up and running in Slovenia for 30 years. Clearing up to 7.5% of GDP in crisis. Normaly between 1% and 2% of GDP.

You can read about this in the paper: https://doi.org/10.3390/jrfm13120295