  Receivable financing is the process whereby cash is advanced to firms against
receivables their customers have yet to pay: a receivable can be sold to a
funder, which immediately gives the firm cash in return for a small percentage
of the receivable amount as a fee. Receivable financing has been traditionally
handled in a centralized way, where every request is processed by the funder
individually and independently of one another. In this work we propose a novel,
network-based approach to receivable financing, which enables customers of the
same funder to autonomously pay each other as much as possible, and gives
benefits to both the funder (reduced cash anticipation and exposure risk) and
its customers (smaller fees and lightweight service establishment). Our main
contributions consist in providing a principled formulation of the
network-based receivable-settlement strategy, and showing how to achieve all
algorithmic challenges posed by the design of this strategy. We formulate
network-based receivable financing as a novel combinatorial-optimization
problem on a multigraph of receivables. We show that the problem is NP-hard,
and devise an exact branch-and-bound algorithm, as well as algorithms to
efficiently find effective approximate solutions. Our more efficient algorithms
are based on cycle enumeration and selection, and exploit a theoretical
characterization in terms of a knapsack problem, as well as a refining strategy
that properly adds paths between cycles. We also investigate the real-world
issue of avoiding temporary violations of the problem constraints, and design
methods for handling it. An extensive experimental evaluation is performed on
real receivable data. Results attest the good performance of our methods.
