Synthetic Data

Companies use AI to eliminate human errors, but biased data can cause the AI to make the same mistakes repeatedly.

Synthetic data for AI and ML offers greater flexibility compared to real data. Synthetic data serves as an ideal resource for advancing AI and ML projects. Generated by AI algorithms, it can be customized to create expanded, reduced, more balanced, or enriched versions of the original dataset. This flexibility allows the data to be tailored for specific needs, such as addressing imbalances by upsampling minority groups or mitigating human biases in the original data by incorporating fairness constraints. In essence, synthetic data acts as a versatile tool for data scientists to refine and optimize datasets for better ML performance.

What are the challenges of original data?

• Costs: Gathering, storing, cleaning, formatting, and labeling data is time-consuming and resource-intensive. These costs increase for projects requiring frequent training.
• Scale: Sufficient data volumes or data that accurately reflect real world scenarios are often unavailable or impractical to collect.
• Compliance: Managing sensitive data, such as financial or personal identification information, requires careful handling to ensure compliance with privacy laws and prevent potential security breaches.
• Bias: Achieving a comprehensive, unbiased dataset that aligns with privacy regulations can be difficult with original data sources.

What are the use cases of synthetic data?

Here are some outlined use cases:

Synthetic data for AI / ML

• Synthetic data helps overcome challenges posed by limited or low-quality datasets, improving the accuracy of ML results.

Data Bias Reduction

• Synthetic data can help mitigate bias in AI training models. Publicly available data often carries inherent biases, but synthetic data can be used to balance these out. For example, if training data contains biased opinions or content, synthetic data can be generated to provide a more neutral or balanced perspective, ensuring fairer AI models.

Training AI for Detecting Fraudulent Transactions

• Blockchain platforms and regulators track transactions to spot signs of illegal activities like fraud and money laundering. To enhance the detection of suspicious transactions and anomalies, synthetic data is utilized to train AI systems, boosting the ability to prevent fraudulent activities and ensure compliance within the blockchain network.

Fraud Pattern Detection for Banks

• Synthetic data replicates real world fraud patterns, enabling banks to improve their fraud detection systems and reduce false positives. By simulating a range of risk scenarios, synthetic data helps banks refine their risk management strategies, enhancing the accuracy of fraud detection and ensuring better performance in preventing financial crimes.

Synthetic Data in Financial Markets(Trading, Portfolio Optimization, and Risk Management)

• Synthetic data allows institutions to create large volumes of data on various investment scenarios, enabling them to assess the performance of different portfolios. This process helps identify the most profitable and efficient portfolios, ultimately improving returns for clients.

How is synthetic data created?

Generative Adversarial Networks (GANs)

• GANs use a two-part neural network system, where one network generates synthetic data and the other evaluates its quality. This method is commonly used to create synthetic time series, images, and text.

Variational Auto Encoders

• VAEs extend GANs by adding an encoder to the system, generating synthetic data that is highly realistic and structurally similar to real data.

Gaussian Copula

• This method uses statistical techniques to generate synthetic data with specific properties, such as normal distribution. It is typically used for data with discrete distributions, like event probabilities.

Transformer-based Models

• Models like OpenAI’s GPT use large datasets to learn complex patterns and generate synthetic data that mirrors the original. These models are widely used in natural language processing and have expanded into areas such as computer vision, speech recognition, image synthesis, music generation, and video sequence generation.

Agent-Based Models

• Simulate the actions and interactions of individual agents within a system to generate synthetic data. These models are especially valuable in situations where the behavior of each entity influences the overall patterns seen in the data.

Challenges with synthetic data

Synthetic data offers a promising solution to the challenges of real data, providing benefits such as enhanced privacy and improved AI training. While it can supplement datasets and boost model performance, it still faces significant hurdles, including high costs, accuracy verification challenges, potential misuse. Despite its potential, adoption remains cautious, particularly in industries like defense, healthcare, finance, insurance, and government, which require rigorous standards for data accuracy and security. As the technology matures, its broader acceptance and application are expected to increase.