  In recent years, a CRA (Credit Risk Analysis) quantum algorithm with a
quadratic speedup over classical analogous methods has been introduced. 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.
