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Business Intelligence in Banking  Yesika Kristina 1501146792  Dea Pradana Darmawan 1501155014  Sukianti 1501169991  Merianti 1501171320  Meshiya Caterlee.

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Presentation on theme: "Business Intelligence in Banking  Yesika Kristina 1501146792  Dea Pradana Darmawan 1501155014  Sukianti 1501169991  Merianti 1501171320  Meshiya Caterlee."— Presentation transcript:

1 Business Intelligence in Banking  Yesika Kristina 1501146792  Dea Pradana Darmawan 1501155014  Sukianti 1501169991  Merianti 1501171320  Meshiya Caterlee 1501171485  06 PLM

2 Outline  Business Intelligence Definition  Business Intelligence in Banking  Problems of BI Implementation  Storage of BI Implementation  BI Architecture  BI and Data Warehouse  BI Application and Users

3 Business Intelligence Definition a variety of software application Raw data analysis

4 Business Intelligence in Banking Business Intelligence Solutions Quickly and efficiently detect and reduce incidents of fraudulent activity. Calculate the probability of customers will default of loan and estimate the cost of recovery. Accurately estimate the risk of customers loan based on:  The financial assets and earning capacity of borrower.  The prevailing economic climate. Analyse credit portfolios, enable banks to quickly identify potential delinquency cases and act early as preventative measure. Ensure compliance with statutory and regulatory requirements.

5 Problems of BI Implementation − Strategic Alignment − Process Improvement − System Implementation − Change Management − Data Usage − Data Definition and Management − Data Visualisation − Vendor Issues

6  Storage Area Network (SAN)  Network Attached Storage (NAS)  Hierarchical Storage Management (HSM) Storages of BI Implementation

7 BI Architecture Five- layered BI Architecture Data source layer ETL (Extract-Transform-Load) layer Data warehouse layer End use layer Metadata layer

8 BI & Data Warehouse Business Intelligence Data Warehouse

9 BI Applications & Users BI Applications  Spreadsheet.  Reporting and querying software.  OLAP (Online Analytical Processing).  Digital dashboards.  Data mining.  Data warehousing.  Decision engineering.  Process mining.  Business performance management.  Local information system. BI Users  IT user  Power user  Casual user  Enterprise user  Business user  Extra-enterprise users

10 Conclusions and Suggestions BI Benefit Problem Application Banking

11 Q & A

12 Thank you …


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