Episode Details

Why do fintech AML pipelines fail regulatory examination when the models themselves are sound? The position here is that examiners do not score area under the curve, they ask reproducibility questions, and most data architectures cannot rebuild a March alert with March data. Three design decisions decide the outcome: immutable timestamped feature storage, documentation that records who approved a feature and which regulation it maps to, and explanation captured at inference time rather than manufactured later.

You walk away knowing why dbt column level lineage maps transformations but not business intent, why SHAP values may be plausible rather than faithful, and how to run a one hour reproduction test on your last high value alert.

Key takeaways

• Freeze every model input as it existed on the decision date so an alert can be rerun with that day's data.
• Keep design records for each feature covering who built it, when, why, and which regulation it maps to.
• Log contributing features live at inference time instead of reconstructing a reason with post hoc explainers.
• Treat vendor lineage statistics, such as dbt Labs' 80% column level lineage claim, as marketing until checked against your own graph.
• Take your last high value AML alert and try to reproduce inputs, output and reason in under an hour.

Chapters
0:00 The March alert nobody could explain
1:08 Why model accuracy does not pass exams
2:03 Lineage tooling cannot show business intent
3:18 Post hoc explainers versus inference-time logging
4:16 Retrofit costs and a Monday reproduction test

Go deeper, free lessons

• AML, KYC and sanctions screening in practice
• Reading the transaction ledger: what payment and behavioral data reveal
• Governing fintech data: consent, lineage, and regulatory defensibility
• the regulatory map every fintech data leader must carry
• The regulatory bodies map: who enforces what and how examinations work

Full article and transcript: https://www.mba-training.com/blog/fraud-kyc-aml-detection-pipeline-fintech

MBA Training, mba-training.com