Vipin Kumar, Head of CUSO IB Data Strategy and Analytics at Deutsche Bank, joins me to unpack one of the toughest problems in financial services: managing data quality in a highly regulated industry. From the outside, it might look like a box-checking exercise. In reality, it’s a complex mix of legacy systems, global frameworks, regulatory controls, and the constant push to balance defensive compliance with offensive business value. Vipin makes it real with examples that connect directly to how we all experience data in daily life. Key Takeaways Data quality isn’t just about accuracy—timeliness, completeness, and consistency all matter, especially when billions are on the line. Regulations push banks into “defensive” strategies, but there’s growing opportunity to apply “offensive” strategies that use data for prediction, analytics, and competitive edge. Measuring effectiveness requires agreement between data producers and consumers, with preventive and detective controls working together. AI and machine learning are starting to automate checks, spot patterns, and even strengthen anti-money laundering defenses. Timestamped Highlights 00:45 What data quality means in a regulated industry 03:15 The challenges of managing fragmented legacy systems 06:40 How producers and consumers measure effectiveness of frameworks 09:30 The pizza delivery analogy for making sense of data quality 14:20 Why accuracy is harder than timeliness or completeness 16:50 The role of AI and machine learning in improving governance 19:20 Shifting from defensive compliance to offensive strategy in banking 22:40 Regulators testing AI-driven approaches to anti-money laundering Memorable Quote “Producer has preventive controls. Consumer has detective controls. True data quality happens only when both align 100%.” — Vipin Kumar Call to Action If you enjoyed this conversation, share it with a colleague who thinks about data quality or governance. Don’t forget to follow the show on Apple Podcasts or Spotify so you never miss an episode.
