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Academy module

Banking & Risk Analytics

Banking risk analytics: four domains converge into lineage-tracked data products, then capital output — architecture diagram for Banking & Risk Analytics, Analytics Legends Academy module M097

As of 2026-08-16

Edited by , sole editor and publication director — 27 years in the SAP ecosystem. How these pages are produced, and where automation is used.

Banking is the most regulation-dense SAP vertical and the largest EMEA demand pool (22%,). Four risk domains with different latency/granularity/audit profiles: credit (IFRS 9 ECL 3-stage, AnaCredit), market (VaR, FRTB), liquidity (LCR/NSFR — the only near-real-time one), operational. BCBS 239 lineage is the governance backbone — every risk KPI must trace end-to-end from S/4HANA subledger → Datasphere → SAC, or it's a regulatory finding. RWA/CET1 capital + parameterised stress tests are the real output. The premium goes to regulation-fluent architects, not build-only consultants.

What you will learn

  • Model credit, market, liquidity, and operational risk as four separate data shapes — not one merged risk mart
  • Design BCBS 239 lineage into the Datasphere data products from day one, source to SAC dashboard
  • Decide when to replicate vs. federate AnaCredit/FRTB data, and when liquidity reporting must be near-real-time
  • Defend a parameterised, re-runnable stress-test model and a reconciled RWA/CET1 number under regulator scrutiny

Module overview

Banking analytics is the most regulation-dense vertical in the SAP estate, and the single biggest demand pool — Banking & Insurance is the largest slice of EMEA SAP analytics demand at 22%. A consultant who can speak the regulatory language fluently — not just build dashboards — commands the strongest day-rate premium in the field.

Four risk domains, four data shapes. (1) Credit risk — probability of default, exposure at default, loss given default; the IFRS 9 expected-credit-loss (ECL) model with its 3-stage staging logic; AnaCredit granular loan-level reporting to the ECB. (2) Market risk — value-at-risk, sensitivities, the FRTB (Fundamental Review of the Trading Book) capital framework. (3) Liquidity risk — LCR (liquidity coverage ratio), NSFR, intraday liquidity — the only domain that genuinely needs near-real-time, not batch. (4) Operational risk — loss-event data, scenario analysis. Each domain has a different latency, granularity, and audit profile; treating them as one "risk mart" is the classic junior error.

Prerequisites

  • Intermediate hands-on experience on SAP analytics projects
  • Review core concepts first: C087, C083, C046

Outcomes

  • Understand the core concepts behind banking & risk analytics
  • Apply Banking in a typical SAP analytics engagement
  • Explain the core architecture and decision points for Banking & Risk Analytics
  • Apply a repeatable implementation pattern in a 15-minute lab format

Full module available to members. The full module adds: the decision framework · the end-to-end scenario walkthrough · the KPI scorecard · the anti-patterns · the code blocks · the knowledge check · the diagrams.

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