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Case Study

Finance & Risk referential

FOCUS ON: Case studies,

SUMMARY

Avantage Reply supported a large European bank in (re)writing operational and regulatory Risk reports by respecting the principles for effective data aggregation and reporting with regards to BCBS 239 guidelines.

Avantage Reply converted Shadow IT into target reports production processes leveraging Data governance, but has also defined and automated final report quality controls using the Dataiku tool.

CUSTOMER GOALS

The bank wished to optimize the current production process and to get the production of a fully managed report through the deployment of a new tool like Dataiku and the usage of a component like Finance & Risk referential.

The goal was to obtain reports natively reconciled on the basis of Finance & Risk referential which has led to a reduction of time dedicated to analyzing and solving discrepancies between Risk and Finance reports.

CHALLENGES

One of the main difficulties was to estimate with accuracy the amount of data non-present in the current Datamart, and this was in order to build up the Finance & Risk referential in the best possible way.

Regarding compliance, issue as non-compliance BCBS 239 principles could lead to penalties. Regulatory instances could also decide to increase RWA (by applying a multiplying factor to ensure conservatism margins), or in extreme cases, decide to apply a change of RWA methodology calculation. Thus, compliance with these guidelines for effective data aggregation and reporting was crucial.

SOLUTION

The project was conducted in a two-phase approach due to the difficulty of estimating with accuracy the number of data non-present in the current Datamart. The first phase, about reports technical analysis, has been dedicated to studying reports, contributing and feeding reporting tools, to determine which indicators should be added to the existing Datamart and written technical specifications. The second phase was about the reports’ development and more specifically about the integration of missing data into the Datamart. This working method allowed us to create aggregate indicators to facilitate Risk and Finance data usage and then to write the report by using Dataiku tool.

Furthermore, because this project follows BCBS 239 guidelines, a glossary was put in place to ensure data provided in reports was consistent with regulators expectations or internal data usages.

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