On offsetting obligations vs making payments

Alex Troyanovskyy
2 min readMay 29, 2018

Current FinTech landscape is notably represented by many DLT implementations — crypto-currencies/tokens — aimed at providing frictionless exchange of economic values. This article is about non-DLT approaches to the same goal and based on the idea of offsetting mutual obligations instead of sending/receiving any medium of exchange. Obligations representing economic values arise, for example, as a result of trading on credit or lending/borrowing, and the real value of an obligation is not less that its nominal value because not paying the debt may compromise debtor’s reputation and cost more than just debt’s amount. Obligations as data (usually reflected in books as receivables and payables) may be processed by means of an information system. Two known elaborations of this vision are highlighted below.

The first is developed by Samer ElBizri, protected by several patents (e.g.
US 8,229,807 B2 — SYSTEM AND METHOD OF OFFSETTING INVOICE OBLIGATIONS), and also described in some other patent applications (e.g.
US 2015/0066755A1 — RESOLUTION SYSTEM LINKING, MATCHING, TRANSFERRING, PAYING AND NETTING OBLIGATIONS). The second, named ARMO (Automatic Reduction of Mutual Obligations) and prototyped by the author of this article is described here. Both approaches are based on the same math model — directed graph which represents the set of obligations among entities, however different designs of those two kinds of system lead to different scope of possible applications. So, let’s look into some details.

It is implied that a number of entities dealing with each other on regular basis are willing to participate in a program which could provide offset of participants’ mutual obligations, and all those entities will send data about their partners’ obligations to the information system supporting such program (thus both approaches provide KYC- conformity). All the participants become nodes of a graph, and some pairs of nodes get connected with directed edges denoting obligations between those nodes. An edge’s value shows the amount owed by edge’s beginning node (obligor, i.e. debtor) to the end node (obligee, i.e. creditor). That graph may contain chains of nodes and some of those chains may form cycles. All nodes’ mutual obligations within a cycle may be simultaneously reduced by the lowest value of the cycle’s edge(s). Nodes’ obligations within non-cycling chains may be reduced by means of the system’s Agents which take care of transferring obligations between chains’ end nodes while offsetting obligations of chains’ internal nodes.

Samer’s Resolution System, being focused primarily on participants’ needs, not only utilizes Agents but also enables applying Business Rules specific to some particular obligations — which means the system requires humans’ involvement in delivering final results to participants and having those results approved or rejected. On the other hand, ARMO system works automatically between operation periods (e.g. Business Day), reduces only cycles of homogeneous obligations accrued by participants at the end of recently closed period, and delivers results of obligations’ reduction to all participants along with the data about next operation period. In other words, ARMO system is a middle-robot which provides fair and inexpensive service for all participants and synchronizes their economic activities — potentially at the scale of national economy.