Synthetic Data For Financial Services

Financial Services firms routinely deal with some of the most sensitive data of any industry, making privacy and compliance critical concerns. From fraud prevention to business security and reputation management, synthetic data is driving safe innovation without compromising security.

Risk Modeling, Stress Testing, and Portfolio Optimization

Financial institutions use synthetic data to simulate various risk scenarios, from market volatility to credit defaults, and to optimize portfolio performance under different economic conditions. By stress-testing portfolios and assessing the impact of rare or extreme market events, institutions can better prepare for potential risks and improve decision-making.

Example: Banks use synthetic data to model how an economic downturn or sudden market shifts might affect their loan portfolios or investment strategies. This allows for more informed portfolio management and risk mitigation, without relying on real customer data, which may be incomplete or insufficient to cover extreme scenarios.

Fraud Detection and Anti-Money Laundering (AML)

Synthetic data plays a vital role in enhancing fraud detection and anti-money laundering (AML) systems. By generating transaction data that mimics both typical and suspicious patterns, financial institutions can train machine learning models to identify fraudulent activities and detect money laundering schemes. This allows institutions to build more robust systems that are capable of recognizing new and evolving threats in real time.

Example: Synthetic transaction datasets can simulate fraudulent behaviors such as unauthorized card transactions, phishing attacks, or account takeovers. Similarly, for AML purposes, synthetic data can replicate complex money laundering activities like layering through multiple accounts or rapid transfers across borders, allowing institutions to detect hidden patterns that signal illicit behavior.

By using synthetic data to create realistic, yet anonymized scenarios, financial institutions strengthen their fraud detection and AML efforts, ensuring that their systems are prepared to handle increasingly sophisticated threats without risking the exposure of sensitive customer information.

Simulating Customer Behavior and Credit Worthiness

Synthetic data allows financial institutions to simulate customer behaviors, such as loan applications, spending habits, and defaults, helping to refine predictive models while protecting privacy. By generating realistic synthetic credit histories, banks can better assess creditworthiness and improve their scoring algorithms.

This approach helps institutions model how customers might respond to economic changes, identify those likely to default or seek refinancing, and create more accurate risk profiles, enabling better decision-making and more personalized financial services.

Regulatory Compliance and Reporting

Synthetic data can be generated to comply with regulatory requirements without exposing sensitive customer information during audits or stress tests. By using synthetic datasets for certain reporting use cases, financial institutions can ensure they meet transparency standards while reducing privacy and compliance risks.

Example: A bank may use synthetic data to demonstrate its capital adequacy in stress test scenarios mandated by regulators without ever revealing real account information.

Privacy-Preserving Data Sharing

Synthetic data enables banks and financial institutions to share realistic, anonymized datasets with third-party fintechs, researchers, or partners, facilitating collaboration without exposing sensitive customer information. This type of data sharing allows institutions to innovate and develop new products or services while ensuring that they remain compliant with data privacy regulations, such as GDPR or CCPA.

By using synthetic data, financial institutions can safely share valuable data insights for purposes like developing AI-powered tools, conducting market research, or running data-driven analytics. For example, a bank might collaborate with a fintech company to develop personalized financial products, using synthetic transaction data that mirrors real customer behavior patterns. This approach allows both parties to leverage insights for innovation without risking the privacy of actual customer data.

Synthetic data also helps to eliminate the burdensome legal and security challenges that often come with data-sharing agreements, streamlining partnerships and reducing the time needed to access useful data. With synthetic datasets, institutions can bypass restrictions on data usage while still benefiting from realistic datasets that reflect the original data's statistical properties.