Synthetic Data

By: A Staff Writer

Updated on: Sep 05, 2023

A Brief on Synthetic Data and Its Applications in Financial Services Firms.

Synthetic data refers to data that is generated artificially rather than by real-world events. While not a direct byproduct of organic processes, synthetic data is designed to mimic real data regarding essential characteristics and statistical properties. This form of data serves a range of functions, particularly in fields where data is sensitive, costly, or difficult to obtain. Financial services firms are increasingly adopting synthetic data to navigate various challenges associated with privacy regulations, risk management, and technological innovation.

Synthetic Data and its Relevance in Financial Services

Regulatory Compliance

Companies in the financial services industry must adhere to strict regulations such as the General Data Protection Regulation (GDPR) in the EU or the Gramm-Leach-Bliley Act in the United States. Synthetic data can de-identify personal information, allowing organizations to conduct analyses without violating privacy laws.

Data Scarcity and Quality

High-quality, relevant financial data can be scarce and expensive to acquire. For risk modeling or fraud detection tasks, synthetic data can supplement real data to create more robust models.

Simulation and Modeling

Financial services firms often rely on predictive models for activities like algorithmic trading, risk assessment, and customer segmentation. Synthetic data can be specifically tailored to simulate various market conditions, allowing for more comprehensive testing of models.

Technological Innovation

Developing machine learning models and AI-based solutions demands extensive datasets for training and validation. Synthetic data provides an avenue to generate this required data computationally, thus aiding in accelerated technological advancements.

Examples of Applications for Synthetic Data

1. Fraud Detection: Synthetic data can emulate transactional data with fraudulent patterns to train machine learning algorithms. For instance, synthetic data could simulate a wide array of fraudulent behaviors, which real-world data might not sufficiently capture.
2. Risk Assessment: Firms can use synthetic data to create scenarios that test the resilience of investment portfolios under various market conditions. This enables better stress-testing and risk-mitigation strategies.
3. Customer Experience: Synthetic data can help design personalized banking solutions by mimicking customer behavior without utilizing actual customer data, thereby ensuring privacy.
4. Regulatory Reporting: Artificial datasets can be created to validate the functionality and accuracy of systems responsible for generating regulatory reports, ensuring compliance requirements are met.
5. Algorithmic Trading: In a study by Cornell University, synthetic data was shown to be beneficial in training trading algorithms, helping them adapt to market anomalies and volatilities that may not be present in historical data.

Statistics

• According to a report by McKinsey & Company, the use of synthetic data can reduce data preparation times by up to 50%.
• A Gartner report suggests that by 2024, 60% of data used to develop AI and analytics projects will be synthetically generated.

Synthetic data presents a compelling solution to several challenges faced by financial services firms, including but not limited to, regulatory compliance, risk management, and technological innovation. By effectively utilizing synthetic data, companies can achieve more reliable results, cost savings, and faster development in various financial models and AI-based tools.

Recommendations

• Financial services firms should evaluate the feasibility of integrating synthetic data into their existing data management systems.
• Organizations should consult with legal and compliance teams to ensure that the use of synthetic data aligns with regulatory standards.
• Ongoing monitoring is essential to ensure that synthetic data retains its quality and relevance over time.

By embracing synthetic data, financial services firms stand to gain a competitive edge through enhanced analytics, risk management, and regulatory compliance.