In the modern digital age, data is often a commodity that is bought, sold, and traded like any other asset. However, when it comes to sets of financial data, the information in them is often sensitive and identifiable, which makes them subject to strict privacy laws. Because of these regulations, the use and distribution of financial data for research purposes outside of financial institutions is severely restricted.
One potential solution to the challenges of strict privacy laws on financial data sets is to create synthetic data. This approach involves creating fake data that mimics the characteristics of real data, and protects the confidentiality of customers’ personal information. The use of synthetic data allows researchers to make analyzes and predictions without compromising customer privacy.
A recent study from the United Kingdom highlights the potential for using synthetic data to overcome privacy limitations in finance. The study examines the challenges and requirements for using data generation and synthetic data techniques.
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The study authors identified three main requirements for generative frameworks for creating synthetic financial statements:
- The ability to create multiple types of financial data, including categorical, binary, complex, and numeric data.
- The generative process must have the ability to produce random numbers of data points.
- The confidentiality of financial data sets must be fine-tuned against how valuable and close to the truth the data is.
The authors emphasize that creating synthetic financial statements protects sensitive customer information and can be used without compromising customer privacy. They also note that generative techniques only learn the properties of real datasets, making it impossible for fraudsters to misuse the original datasets.
In addition, researchers have given several reasons why synthetic data should be created in the field of finance. First, due to organizational constraints, real-world datasets are often not available for testing and research, making synthetic data streams as useful as non-factual data. Second, privacy laws may prevent companies from sharing customer data, but synthetic data can be used for financial institutions’ research and development needs. Third, traditional deep learning algorithms often fail due to the problem of unbalanced separation problems, which can be solved by synthetic data and data computation methods. In addition, synthetic data can be used to train models through deep machine learning techniques and share data between financial institutions.
According to the article, there are two technical solutions for creating synthetic financial statements: creating tabular data and synthetic time series financial data. Tabular data can be generated using various methods, including conditional GANs, VAEs, and PATE-GAN, while CT-GAN is suitable for encoding continuous and discontinuous variables. However, these methods only partially address privacy concerns. Regarding the financial data of synthetic time series, the scholars proposed Quant-GAN and CGAN for modeling and time series forecasting. These models are useful for financial instrument return records and related time series models but do not offer privacy guarantees.
Synthetic data generation techniques mentioned in the paper include supervised and unsupervised machine learning methods and hybrid techniques. These techniques can be used to detect credit card fraud and include gathering information about a data set, training, testing sub-data sets, and evaluating performance using various metrics such as confusion matrix, FPR, recall, accuracy, F1 score, and accuracy rate. One study found that the random forest algorithm had the highest accuracy for detecting credit card fraud. Other techniques used in the study included artificial neural networks, tree classifiers, Naive Bayes, support vector machines, gradient enhancement classifiers, and logistic regression approaches.
In conclusion, the use of financial datasets for research outside of financial institutions is severely restricted by privacy laws. However, creating synthetic data can help overcome these challenges by protecting customers’ personal information while allowing for analytics and forecasts. The study described in this article identifies the basic requirements for generative frameworks for generating synthetic financial statements and emphasizes the benefits of generating synthetic data. Further, the article explores different techniques and methods used to generate and evaluate synthetic financial statements, such as supervised and unsupervised machine learning methods. The use of synthetic data in finance has the potential to revolutionize the industry and facilitate research and development while still prioritizing client privacy.
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Mahmoud is a PhD researcher in machine learning. He also carries a
Bachelor’s degree in Physical Sciences and Master’s degree in
Communication systems and networks. its current fields
The research is concerned with computer vision, stock market forecasting and deep
to learn. Produced many scholarly articles on person re
Determination and study of depth stability and robustness
networks.
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