QC Ware applies quantum computing principles in banking sector

Itaú Unibanco announced results of a collaboration with QC Ware to explore quantum computing algorithms for the banking sector.
6 May 2022

Itaú Unibanco, the largest bank in Latin America. (Source – Shutterstock)

  • Itaú Unibanco collaborates with QC Ware to explore quantum computing algorithms for the banking industry
  • The financial quantum algorithm identifies additional at-risk banking customers
  • Quantum algorithm defines future accuracy and performance advantages and inspires improvements today

As innovations in quantum computing continue to enable more use cases, the banking sector is one industry that could benefit the most from the technology. Over the years, the banking sector continues to adopt new technologies, as it tries to compete with fintech players as well as meet customer demands.

In the banking sector, quantum computing could lead to a range of applications. This includes analyzing large areas of heterogeneous data to make financial predictions and to understand economic phenomena, analysis of financial markets, and the management of asset allocation and risk management.

According to a McKinsey report, “financial institutions that can harness quantum computing are likely to see significant benefits. In particular, they will be able to more effectively analyze large or unstructured data sets. Sharper insights into these domains could help banks make better decisions and improve customer service, for example through timelier or more relevant offers.”

One bank that is exploring quantum computing algorithms for the banking industry is Itaú Unibanco, the largest bank in Latin America. Collaborating with QC Ware, a leading quantum software and services company, the bank recently announced the first results of the collaboration.

With the aim of using quantum computing for customer retention, QC Ware developed quantum machine learning algorithms that improve the accuracy of the models currently used to predict customer churn.

During the collaboration, the two teams developed novel methods that run on today’s classical computers, and can already improve the prediction models, achieving a substantial increase in the previously tested customer retention model. Moreover, these algorithms will also run even faster on future quantum computers using the inherent ability of quantum computers to perform complex linear algebra tasks.

The ongoing objective of the collaboration is to understand the power of quantum algorithms and help prepare Itaú Unibanco to fully deploy quantum solutions in banking.

Combining Itaú Unibanco’s expertise in banking, with QC Ware’s leading edge in both classical and quantum algorithms, the end goal is to build quantum expertise within the company and prepare it for the imminent deployment of quantum computing throughout the financial services industry.

“Keeping our customers satisfied is a top priority at Itaú Unibanco and we will continue to stay at the forefront of implementing innovative technologies. We see in quantum computing the potential to greatly improve customer interactions and we have already benefited from QC Ware’s insights with existing customer retention algorithms,” commented Moisés Nascimento, Chief Data Officer at Itaú Unibanco.

To ensure the best results, Itaú Unibanco provided QC Ware with two years’ worth of anonymized user data and approximately 180,000 data points, with the goal of better understanding which customers were likely to leave the bank in the next three months. QC Ware developed quantum methods for training a customer retention model based on determinant sampling techniques. The quantum methods have improved accuracy and decreased run times compared to classical techniques.

Furthermore, QC Ware also found a way to deploy a variant of these methods on today’s traditional computing devices, which improved Itaú Unibanco’s model, increasing the number of withdrawals captured by 2%, and increasing the overall model’s precision from 71% to 77.5%. The algorithm can continue to run on classical computers for the time being and it is ready to run on future quantum hardware.

“This has been an insightful project for us, and novel use of both quantum and quantum-inspired determinant sampling techniques to enhance machine learning models,” said Iordanis Kerenidis, Head of Quantum Algorithms at QC Ware. “We are thrilled to have developed powerful quantum methods and also find ways to improve both performance and efficiency today. We’re excited about the prospects of quantum computing in financial services.”