The aim of this paper is to describe existing approaches to automation in enterprise management systems of invoice factoring operations. The relevance of this problem is estimated by the volume and dynamics of growth of the invoice factoring market in the Russian Federation. The existing performance problems inherent in software built on the basis of classical algorithms are considered, and their optimization by using quantum algorithms is proposed. Attention is focused on the problem of parallel computing as the main feature, which allows to achieve a significant increase in computing power. Possible way of solution, based on the joint use of quantum and classical approach within one software complex is given. Application of quantum Monte Carlo method for the purpose of improvement of the automated information system of the enterprise is described. The received result allows to draw a conclusion about perspective of use of the given technique for construction of the combined quantum and classical algorithms in systems of management of factoring operations.
The aim of this paper is to describe existing approaches to automation in enterprise management systems of invoice factoring operations. The relevance of this problem is estimated by the volume and dynamics of growth of the invoice factoring market in the Russian Federation. The existing performance problems inherent in software built on the basis of classical algorithms are considered, and their optimization by using quantum algorithms is proposed. Attention is focused on the problem of parallel computing as the main feature, which allows to achieve a significant increase in computing power. Possible way of solution, based on the joint use of quantum and classical approach within one software complex is given. Application of quantum Monte Carlo method for the purpose of improvement of the automated information system of the enterprise is described. The received result allows to draw a conclusion about perspective of use of the given technique for construction of the combined quantum and classical algorithms in systems of management of factoring operations.
The continuous growth of financial markets dictates the need for its participants to seek new approaches to financial analysis to gain competitive advantages, including through the use of new approaches in the field of computing. Quantum computing can be used as a tool for obtaining these advantages over competitors. In particular, Monte Carlo modeling, although widely used in financial risk management, requires significant computing resources due to the large number of scenarios required to obtain an accurate result. To optimize this approach, quantum amplitude estimation algorithms are used, which accelerate this process if pre-calculated probability distributions are used to initialize input quantum states. However, in the absence of these distributions in existing approaches to this topic, they are generated numerically using classical computing, which completely negates the advantage of the quantum approach. This article proposes a solution to this problem by using quantum computing, including for the generation of probability distributions. The article discusses the creation of quantum circuits for modeling the evolution of risk factors over time for capital flows, interest rates, and credit risks, and presents the combination of these models with quantum amplitude estimation algorithms as an example of using the obtained algorithms for credit risk management. In conclusion, the article analyzes the possibility of using the obtained circuits in financial analysis.
This research aims at determining the use of quantum computing on financial structures, mainly pertaining to portfolio, derivatives, Monte Carlo assessments, and risk analyses. The problems appear within the context of the continually development of the financial industry where the usage of the classic methods when solving the problems associated with large data set and the application of complex models can be slow. There are two types of quantum computations: the first is a classical computation that uses an approach very close to a Turing machine; the second is quantum parallelism that enables a system to compute an unlimited number of possibilities simultaneously and will not suffer from these limitations. The study points out that there are several application areas where quantum algorithms provide solutions better than classical ones, for instance, in cutting computational time for portfolio, and providing better solution in derivative pricing. Applying quantum algorithms, financial systems can provide increased precision and liabilities management, as well as improved decision making. The paper also discusses future applications of quantum computing in fraud and security detection, which would both significantly benefit from quantum methods. A still experimental field, the optimisation study does show that Quantum could bring significant changes in the techniques used by the financial industry to address its most critical issues
Aim. IThe presented study aims to identify current trends and key directions for the introduction of quantum technologies into the management practices of Russian credit institutions to improve their performance in the medium term. Tasks. The authors comparatively analyze current trends in the application of quantum technologies in the banking business; forecast key trends in their development in the medium term in the context of the Russian banking sector. Methods. The authors use the methods of comparative and system analysis, scientific abstraction, and hypothesis. Results. Practical examples of the introduction of modern quantum technologies in the foreign and Russian banking business are provided. As a result, priority directions and areas of their application are identified with an emphasis on the domestic banking sector: credit scoring, assessment of the value and risk of investment, diversification of investment portfolios, financial forecasting, crisis prediction, fraud detection, protection of banking and customer databases from the quantum threat, and increasing the level of banking cybersecurity in general. Conclusions. Despite the novelty of quantum technologies in the banking business, the role they will play in banking in the near future is difficult to overestimate. Quantum computers can significantly improve the performance of banking IT systems by solving computational tasks that even super-powerful conventional computers are not able to tackle on their own. They significantly increase the efficiency of modeling complex systems and processes; accelerate the soultion of problems associated with resource optimization, logistics, finance, and trading; simplify work with big data using artificial intelligence (AI) and machine learning models in neural networks; help to combat cyber attacks and fraud in financial markets.
Classical computing methods do not enable to withstand the cumbersome computational operations of complex applications and a bunch of untouched problems that still exist until today. The proposed quantum method tackles the computational complexity problem by exploiting the parallel processing techniques of quantum computing. This paper aims to take advantage of quantum computing power in resource distribution management for a large scale of resources. To achieve this, we use quantum extreme value searching algorithm as a minimum searching algorithm. Moreover, to prove the efficiency of the quantum strategy, we compare the quantum method and the randomized one.
The development of quantum entanglement opens new opportunities for financial markets, facilitating faster data processing, optimizing strategies and improving risk management. Quantum algorithms, with their exponential computational power, are able to solve problems available for classical computers, which makes them promising for high-frequency trading, stress testing of banking systems and liquidity analysis. Introduction of quantum cryptography and optimization algorithms changes the approach to the security of financial operations and forecasting of asset dynamics. The article discusses the main directions of quantum computers use in finance, analyzes their consequences, potential threats and regulatory challenges. Special attention is paid to the impact of the quantum revolution on the financial sector of the economy and the possibility of its transition to new technologies.
Forecasting and managing risk incurring well are major tasks among financial markets that are very volatile, highly interdependent, and with huge streams of data. Unfortunately, these circumstances cannot be handled by conventional computational models due to their inability to work with large data sets and to identify the nonlinear patterns in finance. Quantum computers (QCs) have revolutionized how financial decisions are being made with the new quantum parallelism and excellent optimization algorithms. In this work, Quantum Powered Financial Intelligence (QPFI) System, as a combination of Quantum Machine Learning (QML), Quantum Optimization, and Quantum Monte Carlo Simulations is offered to optimize market forecast and risk estimation. Better pattern detection is realized to predict asset prices using QML models, for example, Variational Quantum Circuits and Quantum Support Vector Machines. Quantum Approximate Optimization Algorithms are used for portfolio allocation optimization of risk vs. return tradeoffs and quantum Monte Carlo simulations for speed-up in stress testing and credit risk assessment. Another way fraud detection is helped by QGANs is in detecting any concealed anomalies in transactional data. The suggested hybrid quantum-classical solution is scalable through cloud-based quantum infrastructure, which facilitates pragmatic implementation within financial institutions. Through the presentation of improved accuracy, computational power, and real-time decision-making, this study identifies the prospect of quantum computing to revolutionize risk management and forecasting models in financial institutions. Future research directions involve enhancing hardware stability, regulatory aspects, and practical quantum trials in banking and trading companies.