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Driving ROI through f inancial r eporting

Driving ROI through f inancial r eporting. Jason McCreight, Deloitte. Outline. Introduction Regulatory landscape for banks XBRL XBRL and Multidimensionality Data Point M odels and Di mensions Standardised Reporting Multidimensionality in Regulatory Reporting

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Driving ROI through f inancial r eporting

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  1. Driving ROI through financial reporting Jason McCreight, Deloitte

  2. Outline • Introduction • Regulatory landscape for banks • XBRL • XBRL and Multidimensionality • Data Point Models and Dimensions • Standardised Reporting • Multidimensionality in Regulatory Reporting • Implications For Banks • Benefits Beyond Compliance • Summary

  3. 1. IntroductionA coming of age for regulatory reporting

  4. 2. Regulatory landscape for banksA recap of the past five years CRD IV (European BASEL III adaptation) European Directives Basel I & II (CRD, II, III) Basel III (CRDIV) Basel III (CRDIV) EBA (European Supervisory Authority) Financial Supervision Banking (National Supervisory Authority) Reporting Obligations COREP FINREP Automated “eRegulation” & XBRL Manual EUC & paper reporting

  5. 2. Regulatory landscape for banks“Navigating the Compliance Labyrinth”: A Deloitte Publication of respondents agreed strongly that compliance with regulatory requirements had become more challenging over the last five years of respondents agreed strongly that compliance with regulatory requirements will become more challenging in the next 3-5 years increase in expenditure on compliance Deloitte Centre for Banking Institutions: 20 of largest 50 US banks by assets http://www.finextra.com/finextra-downloads/newsdocs/Deloittecompliance.pdf

  6. 3. XBRLThe basics

  7. 3. XBRLA global standard for business reporting Local banks National Supervisory Authority European Banking Authority XBRL XBRL mandated by EBA

  8. 4. XBRL and multidimensionality • A multidimensional database (MDB) is often called a “cube”. This is a dramatic simplification as they can often have many more than the 3 dimensions that this visually represents • Itis a database optimised for analysis • These databases can rapidly process data so that answers can be found quickly • The data elements in the COREP reporting templates have been converted to a highly structured multi-dimensional data model • NSAs and banks can use the Data Point model to structure the design and processing of their internal reporting processes end systems. Bank NSA Requires manual interpretation and re-keying of data DPM Approach Traditional Approach DPM Bank NSA DPM approach allows both banks and NSAs to align their systems to the data definitions

  9. 5. Data Point Models and DimensionsExamples Data point model Dimension

  10. 6. Standardised Reporting Regulator Regulator XBRL Solution 1 Solution 2 Solution 3 Solution 1 Solution 2 Solution 3 Bank X Bank Y Bank Z Bank X Bank Y Bank Z • XBRL’s impact on financial reporting and data exchange has been compared to the impact of barcodes on merchandising • Like the barcode, XBRL is a system for coding and decoding information: • Multiple forms of information consolidated into a single, standards-based format • A single piece of financial data is multidimensional

  11. 7. Multidimensionality in Regulatory ReportingCRDIV XBRL as a transition mechanism • Multidimensional data is very rich and powerful • In an ideal world a Regulator could provide an MDB & ask it to be populated. However numerous software companies provide solutions • It is this link that businesses can leverage to enhance other internal and external reporting and go “on the offensive”, whilst simultaneously adhering to regulatory standards Standardised Transmission of data between Banks & Regulators REGULATORY CUBE REGULATORY XBRL BANKS

  12. 8. Implications For Banks

  13. 9. Benefits Beyond Compliance Value-add effort maximisation XBRL

  14. 10. Summary • XBRL is a data transmission mechanism. • However it is different to many of its predecessors as it allows the richness of multidimensionality to be built in & retained • This kind of technology is quite new – it has been around for 20 years • It has been adopted & used consistently in Finance functions; handling results consolidation & publication, planning & analytics • Today’s principle point was that it can also be used more in the Regulatory area. • Wider use should help to produce and analyse accurate data more efficiently • It can be used both by the banks & the regulators to drive greater compliance but should also produce benefits beyond this

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