Data Governance and Compliance for Custom Fintech Software: Ensuring Data Privacy and Security

With technology's constant evolution, the opportunities in the fintech world continue to expand. However, so do the risks—cybercrime is more present than ever. Ransomware attacks have steadily intensified in recent years, with 65 percent of financial organizations worldwide reporting an incident in 2024, a significant increase compared to 55 percent in 2022 and 34 percent in 2021.

One of the biggest challenges organizations face today is meeting regulatory compliance standards—and for good reason. Strong data governance in fintech prevents financial crimes and guarantees client safety and trust. So, how do modern companies ensure data privacy regulations, and what role does AI play in this process?

How to stay compliant and secure in the BFSI industry

The fintech market is only growing and getting stronger, and according to Forbes, its size is expected to reach $698 billion by 2030. Given the influence and power of digital solutions, data has become one of the most valuable assets for any organization, regardless of its size and purpose.

As the name suggests, data governance is the discipline responsible for ensuring a businessy's data quality, security, and availability. Implementing a strong framework has multiple benefits. The most significant advantage is the ability to analyze well-structured information to improve decision-making and discover new opportunities for innovation, growth, and efficiency.

At the same time, data governance in fintech usually includes policies to comply with governmental regulations and ensure the safety and privacy of sensitive information—key to avoiding costly government fees and backlash. Among the several norms and frameworks, the most important are the GDPR, the PCI DSS, and ISO/IEC 27001.

The GDPR is a European privacy and security law that imposes obligations on any organization that collects or processes data related to individuals in the EU, regardless of where a service provider is based. Since May 2018, all businesses handling EU citizens' data must adhere to strict data protection principles and accountability requirements. Non-compliance with the GDPR can result in costly fines, making it crucial for companies to follow its regulations carefully.

The PCI DSS is a widely adopted set of policies and procedures designed to secure credit, debit, and cash card transactions. Industry experts developed it to protect cardholder data, including card numbers, expiration dates, and other sensitive information.

Unlike the GDPR, PCI DSS is not a law or legal requirement but rather a contractual obligation. However, regulatory compliance ensures that businesses follow best practices for the technical and operational security of processing, storing, and transmitting payment account data, reducing the risk of fraud and data breaches.

The ISO/IEC 27001 is an international Information Security Management Systems (ISMS) standard that provides organizations with a structured framework for implementing, maintaining, and continuously improving cybersecurity measures. The main goal is to ensure data confidentiality, integrity, and availability while effectively preparing businesses to handle evolving cyber threats.

BFSI companies can use a wide range of best practices when implementing financial data governance programs. Some of the most common are:

• Automate tasks to increase efficiency
• Create a data catalog
• Balance between data safety and convenience
• Constantly monitor and improve data management

Challenges in the fintech governance

Investors, customers, and governments are increasingly scrutinizing the responsibility and transparency of digital businesses—especially those handling large volumes of data. However, most firms struggle to implement effective data governance in financial services. In 2023, approximately 93 percent of fintech companies reported difficulties meeting compliance requirements.

In both large and small fintech companies, data silos and poor data architecture are common issues. These problems often lead to inconsistent or duplicate information, challenging implementing an effective data governance framework.

A solution to this challenge is for data architects to design appropriate data models and architectures that integrate information across different storage systems. Additionally, businesses may need to develop a comprehensive data catalog to manage their data assets efficiently.

The increasing reliance on data to implement business intelligence only challenges financial data governance. While fast and seamless information access and sharing are crucial, privacy and security must remain your top priorities. It is also essential to prevent data leakage across the systems where you store and share data. Ensuring proper encryption, access controls, and monitoring can help mitigate risks while maintaining efficiency.

One of the biggest drawbacks for digital companies is supplying quality data to train AI models while staying compliant with governance frameworks. This is especially true when it comes to data management in fintech, where handling sensitive information requires strict oversight and advanced security measures.

Unfortunately, many current data storage and governance tools still fall short, leaving gaps in AI data management that can lead to regulatory and ethical risks.

The primary concern is that technology is evolving much faster than regulations. Without clear controls and transparency around what AI systems are trained on, there's a high risk of unintentionally exposing sensitive company data or proprietary customer information. As fintech companies continue to adopt artificial intelligence, strengthening data management practices must remain a top priority.

The role of AI in data governance

The success and power of artificial intelligence have only fueled industries' growing need and ambition to implement it across all business operations. However, its rapid application has raised concerns among both investors and customers, particularly regarding the potential for misuse or abuse of this tool.

At its core, artificial intelligence processes vast amounts of data to learn, recognize patterns, and generate outputs. Regardless of how complex large language models (LLMs) like ChatGPT may seem, their fundamental principle remains the same: the quality of an AI model heavily depends on the data you train it on. This raises one of the most pressing concerns in data protection and fintech: managing sensitive information responsibly while ensuring data security and privacy.

Many companies train AI models using their own databases for internal analytics and business intelligence. It has proven to be a powerful tool for automation and predictive analysis. However, the challenge arises when these models process sensitive data, such as customers' personal information. This can lead to data leaks or compliance issues, especially under regulations like the GDPR.

For example, if a customer decides to end their relationship with a company, GDPR mandates that their data must be erased from the system. But let's suppose an AI model has been trained using that customer's data. The company may need to decommission and retrain the AI without the revoked information—an expensive and complex process.

That's why fintechs must implement data security best practices throughout the AI lifecycle. From the moment data is collected, stored, and used, companies must ensure it aligns with compliance requirements and ethical standards. Embracing artificial intelligence to support security strategies can be a game-changer. In fact, according to IBM, organizations that applied AI for cybersecurity reduced the cost of breaches and saved an average of $2.22 million over companies that didn't use this technology.

The future of fintech compliance

To stay ahead of the competition, you must anticipate regulatory trends and proactively strengthen your strategies—especially for cybersecurity in fintech and protecting sensitive data. And for a simple yet crucial reason, cyberattacks and data breaches are only increasing. In 2024, the average global cost of a data breach reached $4.88 million, with one in three breaches linked to shadow data.

Nowadays, artificial intelligence has become one of the most vital tools for most industries. In fact, 84 percent of fintech companies now leverage AI and machine learning to meet compliance requirements. However, AI is not a set-it-and-forget-it solution—it demands strong oversight, effective leadership, and continuous monitoring to truly deliver value.

By implementing advanced cybersecurity measures, data privacy laws, and transparent communication, your company will ensure compliance and unlock new opportunities for innovation and growth strengthening your investors' and customers' trust and loyalty to your business.