Reimagining data access: Why synthetic data matters now
As the banking industry continues its digital transformation journey, organizations are investing heavily in data analytics, artificial intelligence (AI), and machine learning (ML) to modernize client experiences and improve risk management. From chatbots and fraud detection to credit scoring and real-time personalization, the ambition to provide smarter, faster, and more secure services is evident.
Yet a recurring challenge often hinders these advancements: access to actionable data. Bank teams working with AI models or evaluating new systems frequently encounter layers of internal clearance: legal reviews, privacy impact assessments, IT risk controls, and compliance approvals. These safeguards, grounded in the Data Privacy Act of 2012 and reinforced by BSP’s governance frameworks, are essential. They can also lead to delays that can extend from weeks to months, particularly when real customer data is involved.
Sometimes, AI and analytics initiatives are deprioritized not because of a lack of capability or vision, but due to uncertainty about how to safely utilize data without introducing privacy or operational risks. This raises a crucial question: How can banks expedite innovation when data access, rightfully governed by privacy regulations, frequently becomes a bottleneck?
One solution gaining global traction is synthetic data– a technique designed to help banks move faster while enhancing compliance. Unlike anonymized or masked data, which still originates from real client records, synthetic data is entirely artificial. It is generated by training an AI model on a real dataset to understand its structures, relationships, and patterns. The model then generates new, fictional records that statistically resemble the original data but contain no actual customer information.
The outcome is a dataset that can be utilized for model testing, simulations, training, and internal development without risking privacy breaches. Synthetic data empowers banks to conduct high-value analytics, create digital services, and collaborate with third-party partners without exposing real data. It provides a practical solution to a very real dilemma– how to innovate swiftly without compromising privacy or compliance.
It should be emphasized that synthetic data is not a panacea. It is increasingly recognized by regulators and financial institutions globally as a fundamental privacy-enhancing technology. It is being embraced by public companies, Tier 1 banks, and technology providers in heavily regulated sectors. In regions like Singapore, synthetic data is now included in national privacy-enhancing technology frameworks and is being explored for secure sandbox environments.
The Bangko Sentral ng Pilipinas, through its Digital Payments Enhancement Roadmap, continues to propel digitalization and financial inclusion in the banking industry. Simultaneously, the imperatives of cybersecurity and data governance have never been more pressing. Institutions must demonstrate not only their ability to innovate but also their capacity to do so in ways that uphold trust, accountability, and resilience.
While traditional data-handling methods such as anonymization or masking continue to play a role, they are not always effective in preventing re-identification or delivering usable results for AI training. Synthetic data offers a promising alternative. The risk of exposing personally identifiable information is significantly reduced since it does not stem from real customer records. And because it can closely replicate the statistical properties of the source data, it remains valuable for analytics and machine learning.
Accuracy and trust remain paramount. Synthetic data can be validated using tools that assess its fidelity to real-world patterns, its efficacy in model training, and its resilience to privacy risk. These validations are bolstered by audit reports, enabling legal, risk, and compliance teams to authorize and scrutinize usage with greater confidence.
In benchmarking tests conducted across industries, synthetic data has demonstrated the ability to preserve 70 to 99 percent of the predictive accuracy of genuine data, depending on the complexity of the use case and model. This level of performance unlocks new avenues for innovation, without unlocking doors to regulatory penalties or customer data exposure.
Here in the Philippines, synthetic data remains a relatively nascent topic, but one that is worth exploring. I am currently preparing a white paper titled ‘Why Philippine Banks Need Synthetic Data Now: Unlocking Innovation While Ensuring Compliance,’ which will delve into the strategic and regulatory considerations behind its adoption.
Synthetic data is about showcasing leadership in how data is utilized, shared, and safeguarded. In an era where speed and trust are both critical, banks that embrace synthetic data can move forward with confidence, creativity, and assurance.
