CaixaBank is progressing in its preparation strategy for the arrival of quantum computing. After successfully performing the first real tests of quantum computing to study the applications of this technology in financial services, the institution has taken a step further and developed the first machine learning algorithm to classify risks in Spanish banking leveraging Quantum Computing.
The Spanish bank has applied a hybrid computing framework — which combines quantum computing and conventional computing in different phases of the calculation process — to classify credit risk profiles. To do this, CaixaBank used a public data set corresponding to 1,000 artificial users, with a similar profile to existing customers, but with information configured specifically for the test.
With this project, the institution is making improvements in risk scenario simulations and machine learning, underpinning increasingly complex algorithms which require large quantities of data to learn, whilst also progressing its analysis of quantum computing applications. The results of this test, which demonstrates that hybrid computing can achieve results comparable to those offered by the conventional solution in less time, will be published in more detail in specialist channels so that the conclusions are available to the community.
Hybrid computing uses this exponential computing advantage to perform complex calculations of parameters optimising machine learning algorithms and combines them with classical computing methods to make the most out of both systems. With the application of hybrid algorithms (quantum and classical) in risk analysis, the institution can reach the same conclusions as the classical method in much less time.
