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Applications of Quantum Computing in Banking and Financial Sector

  /  Latest News   /  Applications of Quantum Computing in Banking and Financial Sector
Applications of Quantum Computing in Banking and Financial Sector

Applications of Quantum Computing in Banking and Financial Sector

Key Drivers and Use Cases of Quantum Computing in Banking and Financial Sector

From securities pricing to portfolio optimization, many financial services activities require assessing a range of potential outcomes. To do this, banks use algorithms and models that calculate statistical probabilities. These are effective but are not infallible, as was shown during the financial crisis in 2008 when low-probability events took place more often than expected.

The impact of the COVID-19 pandemic has shown that accurate and timely assessment of risk remains a challenge for financial institutions. Even before 2020, the past two decades have witnessed financial and economic crises that led to rapid changes in how banks and other market participants assessed and priced risk of different asset classes. This drove the introduction of increasingly complex and real-time risk models infused by artificial intelligence (AI) but still based on classical computing.

The arrival of quantum computing is potentially game-changing. However, there is a way to go before the technology can be rolled out at scale. Financial institutions have started accessing the necessary hardware and to develop the quantum algorithms they will require. Still, a growing number of initiatives suggest a tipping point is on the horizon. The financial sector, including banks, can exponentially increase the speed of transactions using quantum computing that allows institutions to scale their processing with fewer costs as opposed to employing more IT or human resources.

Let’s look at the use cases of quantum computing in banking and financial services.

Portfolio Analysis: Identification of the most attractive portfolios given thousands of assets with interconnecting dependencies

Fraud Detection: Quick and accurate detection of fraud indicators to enable proactive fraud risk management

Enhanced Cybersecurity System: Development of next-generation cryptography to protect confidential customer data

Optimization: Improved efficiency in clearing large batches of transactions that have varying credit, collateral, and liquidity constraints

High-Frequency Trading: Rapid execution of complex quantitative buy-sell strategies will improve financial firms’ abilities to generate greater returns while controlling risk.

Clustering: Grouping of seemingly disparate sets of assets to discover patterns in areas like asset performance, consumer sentiment, and risk aversion.

These applications of quantum computing are shaping the banking and financial services sector. However, in assessing where quantum computing will have the most utility, it’s useful to consider four capital markets industry archetypes: buyers, sellers, matchmakers, and rule setters. Here are the key drivers of how quantum computing is fuelling this industry.

Scarcity of Computational Resources

Companies relying on computationally heavy models like hedge fund WorldQuant, with over 65 million machine learning models, employ a Darwinian system to allocate virtual computing capacity. For example, suppose if model X performs better than model Y, model X gets more resources, and model Y gets less. The cost of processing power that rises exponentially with model capacity is a bottleneck in this business model. It could be unlocked by qubits’ exponentially accelerated delivery over classical bits.

High-dimensional Optimisation Problems

Banks and asset managers optimize portfolios based on computationally intense models that process large sets of variables. Quantum computing allows faster and more accurate decision-making, i.e., determining an optimal investment portfolio mix.

Combinatorial Optimisation Problems

Combinatorial optimization seeks to improve an algorithm by using mathematical methods to minimize the number of possible solutions to search faster. This can be useful in algorithmic trading, i.e., helping players select the highest bandwidth path across a network.