Towards practical Quantum Credit Risk Analysis

Abstract

In recent years a CRA (Credit Risk Analysis) quantum algorithm with a quadratic speedup over classical analogous methods has been introduced [1]. We propose a new variant of this quantum algorithm with the intent of overcoming some of the most significant limitations (according to business domain experts) of this approach. In particular, we describe a method to implement a more realistic and complex risk model for the default probability of each portfolio’s asset, capable of taking into account multiple systemic risk factors. In addition, we present a solution to increase the flexibility of one of the model’s inputs, the Loss Given Default, removing the constraint to use integer values. This specific improvement addresses the need to use real data coming from the financial sector in order to establish fair benchmarking protocols. Although these enhancements come at a cost in terms of circuit depth and width, they nevertheless show a path towards a more realistic software solution. Recent progress in quantum technology shows that eventually, the increase in the number and reliability of qubits will allow for useful results and meaningful scales for the financial sector, also on real quantum hardware, paving the way for a concrete quantum advantage in the field. The paper also describes experiments conducted on simulators to test the circuit proposed and contains an assessment of the scalability of the approach presented.

A More General Quantum Credit Risk Analysis Framework

“…For this reason, the data used were specifically generated by domain experts who took into account what realistic and reliable values for input measurements could be. All the relevant data are available in a public repository [ 28 ].…”

“…The complete and non-aggregate results, as well as the code used to generate them, are available in a public repository [ 28 ].…”

“…As described in [ 1 ], credit risk can be evaluated through three primary measures: the probability of default (PD), the loss given default (LGD), and the economic capital ( ). The PD represents the likelihood that the debtor will become insolvent, while the LGD is the estimated loss following the insolvency of the counterparty.…”

“…This paper is intended as an extension of previous work [ 1 ]. In particular, this extended version includes new data, analysis, and theoretical developments not present in the original paper and that derive from recently acquired access to 7- and 27-qubit QPUs.…”

“…The field of quantum finance aims to use quantum computing to solve a variety of computational problems in finance more effectively than classical methods [ 2 , 3 ]. In recent years, researchers have focused on achieving a quantum advantage in credit risk analysis (CRA) [ 1 , 4 ]. CRA is a crucial risk management tool that assesses the risk of loss from a debtor’s insolvency [ 5 ].…”