Lecture 6: Multilateral Trade Credit Set-off

Overview

Tomaž Fleischman presents Multilateral Trade Credit Set-off (MTCS) as an algorithm to resolve late payments and improve liquidity in business networks. By modeling obligations as a graph and applying a minimum cost maximum flow algorithm, MTCS can clear up to 11% of inter-company debt in real-world Italian networks. The system's effectiveness can be further enhanced by injecting engineered liquidity, demonstrating significant network multipliers, especially during economic crises.

Key takeaways

Late payments are a systemic problem causing bankruptcies and hindering economic growth, particularly for SMEs.
The MTCS algorithm, using graph theory and min-cost max-flow, can clear up to 11% of inter-company debt in real-world networks.
Scale-free network topology limits clearing efficiency, but injecting engineered liquidity (e.g., via blockchain) can significantly increase debt resolution by up to 5x.
Cycles Protocol aims to leverage blockchain for privacy-preserving multilateral trade credit set-off, enabling collaboration in competitive economic environments.
MTCS offers a risk reduction technique distinct from netting, preserving original obligations and avoiding the introduction of new risks.
The effectiveness of MTCS and liquidity injection is amplified during economic crises or in liquidity-constrained regions.

Chapters

0:00 Introduction to Multilateral Trade Credit Set-off (MTCS)

Tomaž Fleischman from Informal Systems introduces MTCS.
Focus on resolving late payments and their systemic impact.
Outline of the lecture: problem definition, observation, algorithm, empirical setting, challenges, and liquidity injection.

0:42 The Problem of Late Payments in Business

Late payment occurs when agreed credit terms are not met.
Consequences include increased costs, depleted cash reserves (especially for SMEs), administrative burdens, and reduced productivity.
Systemically, late payments cause unemployment, bankruptcies, and barriers to market entry for small firms.

5:51 Measuring and Observing Late Payments

Data from Intrum Justitia shows agreed payment terms are consistently longer than actual payment times.
Larger companies often extend payment terms to suppliers.
Late payments are prevalent across European countries and various economic sectors.

9:15 Reasons for Late Payments and Firm-Level Impact

Reasons range from tactical non-payment and genuine financial trouble to administrative issues.
A common scenario involves buyers demanding discounts or delaying payments, sometimes leading to extortion.
Late payments are a complex issue requiring significant firm effort to manage.

13:58 Case Study: Late Payment Clearing in Slovenia

Slovenia implemented a public mechanism for reporting and clearing late payments from 1991-1994 and again from 2002.
The mechanism's effectiveness correlated with economic conditions, showing spikes during crises (2008, COVID-19).
Private clearing mechanisms now handle more volume but do not share data.

20:27 How to Manage Late Payments: Obligation Networks

Example: Alice owes Bob 2, Bob owes Charlie 2, Charlie owes Alice 1.
A simple cycle of 1 can be cleared multilaterally without external funds.
Individual firms lack the network visibility to perform this clearing autonomously.

24:00 MTCS Algorithm: Balancing and Flow

The problem is reformulated from finding cycles to finding balances.
Calculate net position for each entity (e.g., Alice: -1, Bob: 0, Charlie: +1).
Introduce a source and sink to balance the network.

27:14 Identifying Cycles via Saturating Flow

Apply a minimum cost maximum flow algorithm to find a saturating flow from source to sink.
This flow represents the most efficient way to balance the network using imaginary liquidity.
Subtracting the saturating flow from the original network reveals the cleared cycles.

30:12 Formalizing the Balanced Network

Define balance as outflow minus inflow for each node.
Create a balanced network G by adding source and sink nodes with edges to/from nodes with negative/positive balances.
Assign capacities to edges equal to the absolute balance, and costs (initially set to 1 for all edges).

34:09 Minimum Cost Maximum Flow Algorithm

Apply the min-cost max-flow algorithm to the balanced graph.
Objective: maximize total flow (achieve saturation) while minimizing total cost.
The resulting saturating flow identifies the optimal clearing paths.

36:55 Subtracting Flow to Reveal Cleared Cycles

Subtract the saturating flow from the original network's capacities.
The resulting graph should have all nodes balanced (zero flow) and no remaining source/sink connections.
This process effectively removes the cleared obligations, leaving only residual debts.

40:33 Pseudocode for MTCS Algorithm

Steps: 1. Balance the network (add source/sink). 2. Find saturating s-t flow using min-cost max-flow. 3. Subtract flow.
Python's NetworkX library is recommended for implementation.
Costs can be uniform (cost 1) or customized to reflect risk or other factors.

50:52 Empirical Setting: Italian Invoice Data (Infocert)

Analysis of 45,000 firms and 2 million invoices from Infocert, an Italian tax compliance firm.
Network exhibits scale-free topology with micro-companies forming the largest group.
MTCS algorithm applied to this large network.

55:24 MTCS Results on Italian Network

Average remaining debt after MTCS is ~11% of initial debt over two years (2019-2020).
Network structure remained stable even during the COVID-19 crisis.
Net creditors and larger firms benefit more from the clearing mechanism.

59:08 Topological Features: Scale-Free Networks

Scale-free topology, characterized by power-law distributions, limits clearing efficiency.
Invoice amounts and number of business partners follow power laws, creating long tails.
This structure acts as a 'brake' on clearing larger amounts and flows.

1:07:13 Liquidity Injection to Counteract Topology

Engineered obligations (e.g., from a bank, mutual credit system, crypto) can be introduced.
An engineered node acts as a source of liquidity to facilitate clearing.
Experiments show significant network multipliers: injecting 20% liquidity can clear ~50% of debt.

1:14:03 Impact of Liquidity Injection on Late Payments

Increasing engineered liquidity significantly drops late payments.
The network effects show how liquidity clears debt through multiple hops.
The mechanism is most effective during liquidity shortages or economic crises.

Summary, takeaways, and chapters were generated by AI from the video's transcript and may contain errors. The video belongs to its creator, MIT OpenCourseWare.