Compliance in finance has always been high-stakes, but today, with regulations growing more complex and penalties getting steeper, the cost of non-compliance can hit hard—financially and reputationally. But here’s the good news: RegTech, powered by data-driven AI, is emerging as a powerful ally for financial institutions and other industries.
With global RegTech sales projected to soar to nearly $82 billion by 2033, and the AI-driven segment alone is expected to reach $3.3 billion by 2026, the message is clear: AI is no longer optional—it’s foundational. It is reshaping RegTech by automating compliance, simplifying regulatory tracking, enabling real-time fraud monitoring, and accelerating KYC with accurate identity checks. That results in stronger compliance, reduced risk, and a faster, smoother customer onboarding experience.
As data volumes and regulatory demands grow, companies that are slow to adopt AI for regulatory compliance struggle to scale, risk falling behind more agile, tech-savvy competitors, and face regulatory scrutiny they’re ill-equipped to manage.
However, delivering real value using AI in RegTech rely heavily on one thing: high-quality, AI-ready data.
Is Data Readiness Slowing Down Your AI Initiatives?
RegTech firms face serious roadblocks when preparing data. Regulatory systems process and analyze huge volumes of customer, transactional, and behavioral data. If the data is inconsistent, incomplete, or outdated, AI models can misfire—leading to false positives, missed alerts, or regulatory violations. Without clean, structured, and timely data, even the smartest AI can’t deliver.
In this blog, we’ll explore the top 5 data–readiness challenges RegTech companies face — and how they can effectively overcome them.
1. Data Privacy & Security:
RegTech companies work with highly sensitive data, including Personally Identifiable Information (PII) and confidential financial records — where the slightest breach can lead to serious consequences. They must also comply with strict regulations such as GDPR, CCPA, PCI-DSS, and various region-specific banking laws. This makes data access, storage optimization, and secure sharing both complex and expensive.
How to Tackle It?
Data masking anonymizes or pseudonymizes sensitive fields before the data is used for AI training or analytics. This approach enables RegTech teams to securely collaborate, develop, and test solutions without breaching compliance rules. It’s a crucial step in compliance automation ensuring that data access protocols are in place, keeping your compliance strong and risk low.
2. Data Quality & Integrity
Bad data leads to bad decisions—and for RegTech compliance, that’s a risky business. Duplicate entries, incomplete records, inconsistent formats, especially with data coming from multiple financial institutions, countries, or departments can lead to false positives in fraud detection or missed red flags in KYC checks.
How to Tackle It?
Strengthen data quality at the foundation. With Data Profiling, Data Mapping, and auto-generated ER Diagrams, companies can understand the structure and health of their data. The frameworks ensure consistent formatting, validation checks, and deduplication — giving your AI models clean, trusted data to work with.
3. Data Integration from Multiple Sources
Financial data comes from everywhere— legacy systems, cloud platforms, APIs, PDFs, emails, and even handwritten forms. Normalizing this information for real-time analysis is a time-consuming and technically complex task.
How to Tackle It?
Metadata-driven ETL frameworks streamline the integration of structured and semi-structured data into a single source of truth. A centralized, AI-ready data repository is essential — so your compliance tools have everything they need at their fingertips.
4. Real-Time Compliance Monitoring
Modern regulations demand real-time compliance. But most organizations still rely on batch updates, laggy integrations, or manual processes. That’s not just inefficient; it’s risky.
How to Tackle It?
Real-time regulatory intelligence through API-based ingestion is necessary to stream live data from clients, transactions, and regulatory feeds directly into your models — powering dashboards, triggering alerts, and ensuring audit readiness. Thus, with the help of AI in Regulatory Technology, firms can instantly detect fraud, monitor transactions in real time, and stay on top of compliance.
5. Scalability Across Global Operations:
As RegTech companies expand across regions, they face the added complexity of local compliance laws, multiple currencies, languages, and diverse financial systems.
How to Tackle It?
Scale your AI by transforming early Generative AI prototypes into powerful, production-ready AI/ML models. These models can automate Know Your Customer (KYC) processes, assess financial risk more accurately, and detect suspicious patterns related to money laundering (AML), enabling smarter, faster compliance and fraud prevention.
Bridge the Gap Between Data Readiness and AI Investment with Saksoft
Saksoft’s Data Readiness Services are purpose-built to help organizations unlock the full value of their data and accelerate AI adoption by ensuring the data is secured, structured, and AI-ready. SolidHub, our data driven AI framework, integrates structured and unstructured data across diverse systems and formats. It simplifies data integration from cloud, on-prem, and hybrid sources, eliminating delays and manual effort. With built-in data governance, masking, and quality controls, it helps RegTech firms meet strict regulatory requirements while enabling faster, more accurate AI-driven insights.
With a team of AI-trained consultants and SMEs with deep domain expertise and hands-on experience, Saksoft helps businesses reduce manual effort by up to 35% and streamline data preparation—accelerating AI implementation by 40% through SolidHub.
Conclusion:
The future of RegTech is incredibly promising, with AI continuing to redefine how we approach compliance, reporting, and risk management. But that being said, AI in Regulatory technology isn’t just about automation—it’s about trusted automation backed by trusted data.
RegTech, originally adopted in the financial sector, is now gaining widespread attention across various industries like agriculture, manufacturing, healthcare, and logistics. While implementing RegTech might come with its set of hurdles, the rewards are immense. With the right planning, investment, and a mindset open to change, businesses can revolutionize their compliance processes.
Ready to transform your RegTech strategy with AI–ready data? Contact us today to discover how SolidHub is enabling RegTechs to build the next-gen AI-native compliance stack.
