How Quantum Computing could innovate into Banking & Finance

Every sector has changed in the last two decades due to technological advancements. Now every business is equipped with technology, software, or equipment to fast their operation and improve their services. Looking at how the bank sector changed due to technology or artificial intelligence in the past decades, it was rapid. According to the UK Government, the total number of bank and building society branches would decline by 34% between 2012 and 2021. According to German Bundesbank figures, the number of bank branches in Germany declined from roughly 40,000 to 25,000 from 2010 to 2020, and it is expected to continue to shrink. The business is undergoing rapid transformation, with most financial transactions online. Much of this shift is driven by information technology, with security, dependability, and service availability as critical success criteria.

The banking industry is also evolving: instead of collecting interest, they must pay penalty interest on deposited cash. When it comes to generating sustainable, lucrative financial products and analyzing risks, existing models and IT systems are nearing their limits. This is the point where quantum technology comes in: it allows for the quick computation of complicated models, among other things.

Quantum technology is a means of creating new information processing paradigms. Recent computational models provide more effective risk evaluation, increased income owing to more robust market positioning, and cost-cutting automation. Quantum computing may be defined as processing information utilizing quantum mechanics principles on the most basic level. Quantum computing may be divided into three major components. First, there is the encoding of classical information in qubits. Second, using quantum gates to modify or compute this information. Finally, the measuring method extracts classical information.

How may quantum computing be used in finance and banking?

Portfolio optimization and asset allocation

Investment decisions are mostly centered on allocating budget to various assets and asset classes to achieve the desired return on investment while reducing certain risks. When selecting whether to establish long or short positions in particular assets or asset values under the constraint of a capped investment amount, a combinatorial optimization issue arises that analytical approaches cannot handle quickly. As a result, heuristics have been created to attempt to get solutions using approximation approaches. Even yet, they fall short in terms of runtime and solution quality.

State-of-the-art approaches try to include additional stochastic parameters like interest rates, different economic scenarios, or uncertainties in input parameters in general, which can

be referred to as robust optimization. Furthermore, if the input parameters are interdependent, scenario-based methodologies can be incorporated and explicitly included in the optimization process to account for various macroeconomic and political outcomes. Services based on quantum algorithms may deliver better results, solve problems in less time, or consider additional limitations. As stress testing may already be addressed throughout the asset allocation process, risk management might begin with backward-looking models and progress to forward-looking models.

Risk management

The valuation (of risks) of financial instruments is a famous example. Several common scenarios are constructed (hundreds of thousands), and the related pay-outs/risks are computed. Finally, an expected value or a risk measure, such as value-at-risk, is calculated. Quantum characteristics can be utilized to generate random paths/scenarios and evaluate expectation values. In the qubits, the pathways (strategies) are encoded. Their collective state is modeled after the (probability-weighted) pay-out function. 2nD routes can be constructed using In qubits. A quantum method with a quadratic speedup may then calculate the expectation value. A more significant number of qubits is required to allow for a better resolution to adapt this technique to meaningful use cases.

Synthetic data generation

Every day, the necessity of synthetic data production grows. The heart of a generative adversarial network is the generation of such synthetic data. It comprises two neural networks, a generator, and a discriminator, which compete. The generator generates a sample of synthetic data to fool the discriminator depending on a set number of input parameters. The discriminator attempts to differentiate between this fake data and actual training data. If both sides are properly taught, the created data is so excellent that the discriminator cannot tell it apart from the accurate data. This framework enables an incredibly promising training procedure in which quantum algorithms can replace and augment other processes.

You can get an exponential edge over traditional networks by producing data from samples of observations in high-dimensional areas. Correlations may be quickly taken into account utilizing quantum computers, resulting in significant gains in risk assessments of financial infrastructure, insurance company portfolios, fraud and terrorist funding detection, and energy network stability. Furthermore, generative models might be utilized to effectively load distribution functions into quantum computers so that operations on the represented data can be performed

Summary

Quantum computing is neither science fiction nor technology that can only be employed in the laboratories of a few scientists. Quantum Computers are now making inroads into the commercial sector. Businesses have already begun their path, gaining strategic knowledge and discovering methods to analyze applications and business cases as a critical step.