Data in banking
the data that runs a bank: transactions, credit scoring, fraud and AML detection, risk models, and the strict governance, privacy and model-risk rules around them.
Banking runs on data: payment flows, credit bureau feeds, transaction logs, core banking systems, and regulatory reporting all generate structured and unstructured datasets that drive lending, risk, and compliance decisions. This block builds fluency in the data assets unique to banking, from KYC and transaction records to loan performance and market data feeds, and how their quality is measured and governed. You will learn the sector's core data infrastructure (core banking platforms, data warehouses, APIs), the metrics used to assess data quality and completeness, and the regulatory frameworks (BSA/AML, GDPR, BCBS 239) that shape how banks collect, store, and audit data. The goal is practical fluency, not general finance theory.
What you'll master
- Identify and map the core data sources banks rely on, from core banking systems to bureau and payments data
- Evaluate data quality using banking-specific metrics like completeness, lineage, and reconciliation accuracy
- Interpret regulatory requirements such as BCBS 239, KYC/AML, and GDPR as they apply to banking data governance
- Design and run practical data audits and checks to catch gaps, duplication, or compliance risks in banking datasets
Key terms
Modules
Covers how core data disciplines power credit scoring, fraud detection and risk model governance in banking.
Covers mapping the banking data estate and measuring its quality, governance maturity and analytics performance.
Covers privacy law, consent, cross-border transfers and access audits that constrain how banks use data.
Latest articles
Recent articles from the blog that apply to Banking.
- 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.
- How JPMorgan Chase built data contracts across 50+ domainsJPMorgan Chase spent years grappling with fragmented data ownership across hundreds of business lines before systematically formalizing who owns what and on what terms. Their approach to data contracts offers a working model for CDOs who need accountability without organizational paralysis.
- Zero-trust architecture for enterprise data access: what CDOs actually need to understandZero-trust has become a standard fixture in security conversations, but most explanations stop at the network perimeter and never reach the data layer where CDOs actually operate. This article breaks down how zero-trust applies specifically to data access, where it works well, and where it creates friction that leaders need to anticipate.
- 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.
- How JPMorgan Chase built data contracts across 50+ domainsJPMorgan Chase's data mesh initiative forced the bank to confront a problem most large organizations prefer to defer: who actually owns a data product, and what obligations come with that ownership? Their approach to data contracts offers a detailed, replicable model for CDOs managing complex, federated data environments.