Data in fintech
fintech data: transaction and behavioral data, alternative-data underwriting, fraud and KYC/AML, and data governance.
This block builds sector-specific data fluency for fintech professionals. You will examine how core data concepts, such as data models, pipelines and quality frameworks, apply specifically to financial technology products. You will map the data landscape unique to fintech: transaction feeds, credit bureau files, KYC documents, open banking APIs and behavioral scoring data. You will learn the metrics used to judge data quality, freshness and reliability in payments, lending and banking contexts, strictly from a data lens, not financial ratios. Finally, you will cover the privacy and governance rules, including GDPR, PSD2, CCPA and PCI DSS, and the practical audits fintech data teams run to remain compliant, defensible and audit-ready.
What you'll master
- Identify and evaluate the core fintech data sources, including transaction, KYC, credit bureau and open banking data
- Apply data-quality metrics (completeness, latency, accuracy, lineage) to assess fintech datasets and pipelines
- Design a basic data governance and access-control framework compliant with GDPR, PSD2 and PCI DSS
- Conduct a practical data audit to detect gaps, bias, drift or compliance risks in a fintech dataset
Key terms
Modules
Applies core data skills to real fintech use cases: transactions, underwriting, fraud, and governance.
Covers mapping data sources, tracking quality, benchmarking vendors, and measuring pipeline and product metrics.
Covers regulatory mapping, privacy by design, governance audits, and breach and inquiry response.
Latest articles
Recent articles from the blog that apply to Fintech.
- Three pipeline design decisions that determine whether your AML model survives its first regulatory examinationMost fintech fraud and KYC/AML pipelines fail not because the models are weak but because the data architecture cannot defend itself under examination. This playbook walks through the design sequence that keeps you compliant, explainable, and operationally credible when regulators arrive.
- How Tala built a credit engine for the world's most invisible borrowersTala lends to borrowers who don't exist in any credit bureau, using smartphone data as a substitute for a credit file. Here is what their model actually does, what it has produced, and what fintech data leaders can reasonably take from it.
- Real-time streaming data: a CDO playbook for getting it rightMost organizations collect streaming data but few actually act on it fast enough to matter. This playbook gives CDOs a concrete sequence for building real-time data capability that delivers operational value, not just architectural complexity.
- Feature stores: the missing infrastructure layer in your ML data supply chainMost ML failures are not model failures. They are data supply chain failures, and feature stores are the architectural response that serious ML organizations have adopted to fix them.
- Data governance under pressure: what CDOs must get right in 2026Regulatory pressure, AI proliferation, and cross-border data flows have turned data governance from a compliance checkbox into a board-level strategic concern. CDOs who treat it as infrastructure rather than policy will be the ones still standing when the audits arrive.
- Data governance in 2026: why compliance alone is no longer enoughRegulatory pressure on data has never been higher, but CDOs who treat governance purely as a compliance function are already falling behind. The organizations pulling ahead are the ones treating governance as a business capability with measurable commercial value.