SAP - FİNANSAL HİZMETLERDE ANALİTİK DÖNÜŞÜM - SAP Forum 2013
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SAP - FİNANSAL HİZMETLERDE ANALİTİK DÖNÜŞÜM - SAP Forum 2013

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Müşteri ilişkileri, ana bankacılık, risk yönetimi ve finans gibi alanlarda, SAP HANA ve iş analitiği çözümlerimizle, veriyi daha önce olmadığı kadar hızlı kullanmanın avantajlarını ...

Müşteri ilişkileri, ana bankacılık, risk yönetimi ve finans gibi alanlarda, SAP HANA ve iş analitiği çözümlerimizle, veriyi daha önce olmadığı kadar hızlı kullanmanın avantajlarını yakalayın. Dünyadan örneklerle çığır açan analitik çözümleri dinleyin.

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  • Bankingsource: Inge Nickels, EMEA PresalesInsurance source: Ralf Strassner
  • SAP’s approach to in-memory computing was engineered taking advantage of In-Memory from the ground up. Based on an understanding of the architecture and the inherent power it offers, SAP designed SAP HANA taking advantage of the following:Multi core architecture for massive parallel processingLarge amounts of RAM memory positioned on the main motherboard next to the CPUs - Software that runs in memory - Database architecture leveraging row and column storeCommodity x86 architecture
  • So, what’s inside HANA? This architecture diagram explains the main components and capabilities. …So, I keep throwing around words like ‘massive’ amounts of data and ‘amazing’ speed. What kinds of scale, speed and improvement are customers seeing?
  • Technology Stack with Analytics on any data source - SAP HANA is based on In-Memory technology to host the data (RAM) Data structure is both column-based (analytics) but also row-based (transactional).It providesAccess to databases in main memory: 1000 to 10,000 times faster than from storage drivesFuture blade servers: up to 500 GB RAM + arrangement of 100 blades with up to 50 TB main memoryFuture data compression in stacks: 20x greater than todayHow big is a terabyte: holds about 330,000 3MB photos or 250,000 MP3sReportingFrom Application point of view especially the Financial Reporting & Transaction HistoryThe Financial Reporting Allow for flexible ad-hoc reportingfor urgent on-demandinformation down tothemostdetailedlevel (i. e. postingdocument)Provision of daily balance sheet, profit & loss statement, notes reports and reconciliation of changes of equity, cash and impairmentThe Transaction HistoryProvide current account holders ad-hoc access to their payment transaction history, e.g. to for tax filing purposes From general Analytical processing capabilities it enables Campaign analysisCustomer segmentationChurn risk calculationAcceleratorsAFI RDL Extension accelerates reading of all core accounting processes on the Bank Analyzer Result Data LayerCO-PA Accelerator for the Profitability Analysis is only second priority and complementary to the Integrated Financial and Management Accounting (IFMA) solution for anks, because it covers only the Non-Banking (ERP-) business of a bank.BW on HANA DatabaseSAP NetWeaver Business Warehouse Accelerator (BWA) is based on In-Memory technology to host the data (RAM). Data structure is column-based. Moving forward, BWA evolves into SAP HANA as SAP in-memory computing becomes the primary persistence mechanism for BW (with HANA 2) Basis for EDW for SAP and Non-SAP dataHigh Performance ApplicationsLiquidity Risk Management is a intraday risk calculation on huge data volumes. It is Basel III compliant and independent from Bank Analyzer implementation.Fraud Detection especially for forCredit Cards. Fraudulent behavior results in significant revenue loss in companies across industries.  In order to mitigate this loss, companies must be equipped with tools that can perform predictive analysis to gain insight into fraudulent behavior. These tools must be capable of screening customer data across millions of transactions and documents
  • Business ScenarioCredit Card / Debit Card AnalysisAnalysis for different areas: Financial, Marketing, Operations, Customer ServiceAnalyze 20billion credit card TNX per year to gain insight into Geography , Month, Shopper, Customer Profile , Commercial ,etcNegotiate fees/charges with EstablishmentsGenerate Profit information by Integrating Costs data from SAS and POS dataCurrent issuesMassive Transaction volumes from POS feeding GL and BW systems.Daily 2mi POS , Monthly 400Mi TXNsRedundant data marts and large number of cubes to manage information in BW.Cannot perform analytics on current data volumes in reasonable time .Increased cost and maintenance of BW system – 12TB of dataValue PropositionAllow for data mining of transaction data, ad hoc analysis on large volumes of non-aggregated data Basis for wide variety of analysis of Customers (Establishment, Equipment type -POS-, …) and Events –(Credit Card, Debit Card, Voucher-, Value, Day/hour, number of agreed payments, …)Scale to additional data volumes from new Business (Payment Srvs for Professionals)Lower TCO because of less storage requirements and no-aggregated to maintain.
  • Business ScenarioTo manage communication with customers via different channels (ATM, call center, online banking, mail, branch, ...) the data warehouse infrastructure is setup to collect and provide data around customers and customer interaction history. Based on historical data, HANA enables different scenarios, e.g.:Cross-selling: proposals of "next best product" per customerChurn management: prevent loss of customersData analysis: ad-hoc reports or analysis tasks to discover patterns in interaction history, which can be used to increase revenuesF2 P&L By Customer SegmentationCustomer ProfileGlobal bank - $ 2.4 trillion in assets10m customers1,000 key figures per customer recordValue PropositionAnalyze information in real-time at unprecedented speeds on large volumes of non-aggregated dataSpeeds up query performance by a factor of more than 500 – e.g. from 45min down to 5 secData compression by a factor of 6 – from 40GB to <7GBDerive customer behavior/trendsReal-time personalized cross-sell / up-sell, increases market and wallet shareReduce customer churn
  • SAP Fraud Management covers a complete cycle to manage fraud detection and investigation and improve fraud prevention going forward.Following a logical path here (anti-clockwise from top left): first, by performing analyses of fraud patterns based on historical data, you can design how fraud is going to be detected, then set-up and fine-tune detection criteria, using simulations and the calibration tool provided (fraud calibrator), then run fraud detection and perform investigation on the resulting findings. Fraud Management also provides a set of reports and dashboards to measure performance on a continuous basis.
  • Revolutionizing the way customers buy and consumer SAP solutions
  • Key Business Driver: Defining theProblemIn a Banking system with Massive Data volumes (> 5TB / 6 – 10 mio/day) and multiple Key figuresNo Intra Day insight into Financial Position across Spot and Average BalancesResource intensive daily aggregated balances for MTD, QTD, YTD on ECC GLMultiple totals ledgers adding to application complexity.Challenges with Reconciliation and Transactional Back-postings.Loading data to BW is time and resource intensive process.Solution Provides :Point in time Spot & Average Balance calculations and Liquidity position.Basis for Daily yield calculations and regulatory reportingReal Time informed decisions regarding opportunities and risks by managing Balance SheetsSide by side scenario for accelerated financial reportingDays to minutes to get to Key Financial KPIsSolution ScopeIn-memory based Fin Operational Metrics on replicated Banking GL Line Item details (SLT based replication)
  • DescriptionTraditional, periodic accounting takes place over regular intervals, during which financial statements are prepared. Since traditional reporting provides the company’s financial position, results of operations, cash flows, and other financial information several weeks or even months after the reporting period has ended, the information reported is not as actionable as it could be if received sooner. The earlier a company issues its financial statements/KPIs for a particular period, the more useful the statements/KPIs will be to its users. Real-time financial reporting/analysis allows organizations to understand their financial position in a near real time basis which allows management to quickly adapt to opportunities and address problems. Current SituationQueries can run for hours or overnight KPIs calculated on monthly/quarterly cycle. Not a notion of daily or real-time proactive performance management Inability to react quickly to problems/opportunitiesLarge data volumes create latency and need for aggregationWith Sap In MemoryReal time visibility/analysis of the factors (down to transaction level) that drive revenues, expenses and shareholder value across product, customer, channel, geography etc.Enables intra-period information on income statement, cash flow and balance sheet condition without having to perform consolidations or wait for books to close Drive budget/performance accountability by providing daily insight into budget versus actual Improve financial performance by implementing more effective expense management and timely monitoring of expense details. Analyze information in real-time at unprecedented speeds on large volumes of non-aggregated data
  • You will see along the following slides that some scenarios are based on a “secondary” database, some other like LRM and Fraud on a HANA primary DB since developed directly on HANA
  • Backend Tools:HANA Predictive Analysis Library (PAL)PAL is a set of predictive analysis functions written in C++ and executed in HANA.HANA - R Integration Through the R integration solution, developers can leverage open source R’s 3000+ external packages to perform a wide-range of data mining and statistical analysis.Frontend Tools:SAP Predictive AnalysisRich UI for modeling workflow and visualization.Expertise:SAP Performance and Insight Optimization; PartnersApplications:Retail: Affinity insight; Demand Signal ManagementUtilities: Smart Meter analyticsCRM customer segmentationHANA – Data Mining / Statistical AnalysisThree ways to implement in-memory data mining and statistical analysis SQLScriptSQLScript is a set of SQL extensions which allow developers to push data-intensive logic into the database in order to avoid massive data copies to the application server and to leverage sophisticated parallel execution strategies of the database.R R is integrated 3000+ external packages can be leveraged to Perform wide-range data mining and statistical analysis.Predictive Analysis Library (PAL)PAL is the predictive analysis library for applications built on top of the SAP HANA database. The functions are written in C++ and executed in database calculation engine. PAL has a roadmap for data mining and statistical algorithms.

SAP - FİNANSAL HİZMETLERDE ANALİTİK DÖNÜŞÜM - SAP Forum 2013 SAP - FİNANSAL HİZMETLERDE ANALİTİK DÖNÜŞÜM - SAP Forum 2013 Presentation Transcript

  • SAP Real Time Analytics Use Cases for Banking Aslihan Akin/Jörg Bartelt SAP Forum Turkey, September 20th, 2013
  • © 2013 SAP AG. All rights reserved. 3 Industry & Market Dynamics
  • © 2013 SAP AG. All rights reserved. 4 Retail and Commercial Banking Value Map Comprehensive solutions for commercial and retail banking Retail Banking Operations Retail Deposits Retail Lending Centralized Payment Processing Finance Finance Transformation Performance Management Finance and Risk Data Management (IFRA) Collaborative Finance Operations People and Talent Core Human Resources and Payroll Talent Management Workforce Planning and Analytics Procurement Supplier Discovery and Lifecycle Management Strategic Sourcing and Contracting Procurement and Order Collaboration Collaborative Invoice to Pay IT Management Application Lifecycle Management IT Infrastructure Management IT Portfolio and Project Management IT Service Management IT Strategy and Governance Multichannel Mobile Customer Internet Banking Call Center Automated Teller Machines Sales and Service Customer Acquisition Customer Management Social Media Engagement Risk and Compliance Credit Risk Management Liquidity Risk Management Governance, Compliance and Surveillance Enterprise Risk Reporting Commercial Banking Operations Cash and Liquidity Management Commercial Lending Leasing Financial Services Network Technology Solutions Analytics Consumer ExperienceData Management Enterprise Mobility SAP HANA Platform Application Develop-ment and Integration
  • © 2013 SAP AG. All rights reserved. 5 An Innovative Approach to Analytics What is SAP HANA? Yesterday Today Disk Partitioning Insert Only on Delta Compression Row and Column Store No aggregatesMemory + + + + Memory Logging and Backup – Solid State / Flash / HDD CPU Multi-Core Massively Parallel SingleOptimized Platform 64-bit address space supports 2TB RAM 100GB/s throughput Software and data reside on HDD • IO constraint • Support many platforms • Optimized for None • Take advantage of latest advances in hardware • Minimum IO time • Optimized for x86 platform Disk CPU +
  • © 2013 SAP AG. All rights reserved. 6 SAP HANA appliance software SAP HANA™  In-Memory software + hardware (HP, IBM, Fujitsu, Cisco, Dell)  Data Modeling and Data Management  Real-time Data Replication  SAP BO Data Services for ETL capabilities from SAP Business Suite, SAP Business Warehouse (SAP NetWeaver BW), and 3rd Party Systems  SAP NetWeaver as In-Memory app. server Capabilities Enabled  Analyze information in real-time at unprecedented speeds on large volumes of non-aggregated data  Create flexible analytic models based on real-time and historic business data  Build new category of applications (e.g., planning, simulation) to significantly outperform current applications  Minimize data duplication SAP HANA SQL MDXBICSSQL SAP BusinessObjects tools Other query tools SAP Business Suite Other data sources SAP NetWeaver Business Warehouse SAP HANA Studio and Modeler SAP In-Memory Database Calculation and Planning Engine Row & Column Storage Real-Time Data Replication SAP Business Objects Data Services SAP NetWeaver
  • Banking Real Time Analytics Use Cases
  • © 2013 SAP AG. All rights reserved. 8 SAP Analytics & HANA for Banking Use Cases – Main Categories Today Banks can run new business, they could never before  Manage fraud risk with real time analytics  ŸEnhance cross-sell and up-sell opportunities with predictive customer behavior analytics  Discover new business areas for customer specific servicing with sentiment intelligence, etc. or they can better run their current business (time-to-market, TCO)
  • © 2013 SAP AG. All rights reserved. 9 Business Analytics Scenarios in Banking Fast and accurate Risk Reporting Support regulatory Compliance Manage liquidity risks in real time to compare asset values globally Real time fraud detection & action Real Time Analytics in Capital Markets Core Applications Sales, Service, Marketing Financial Accounting Operations and IT Improved customer service (e.g. spending analysis) Advanced analysis capabilities Real time operational reporting Optimize marketing campaigns with customer segmentation and channel analytics Enhance cross sell opportunities with customer behavior analytics Use real time branch sales analytics to manage by exception SAP Customer Engagement Intelligence Accelerated global Financial Reporting Predictive modeling analytics for finance Customer retention and acquisition Support regulatory requirements Financial analysis on massive data sets Reduce overall cost of information and IT staff support Improved IT support Improve quality of master data Improved financial management Strategic Workforce Planning incl. simulations and Reporting Risk Management
  • © 2013 SAP AG. All rights reserved. 10 SAP HANA for Banking use case examples Transactional Banking Fast and accurate Risk Reporting Support regulatory Compliance Manage liquidity risks in real time to compare asset values globally Real time fraud detection & action Real Time Analytics in Capital Markets Transactional Banking Sales, Service, Marketing Financial Accounting Operations and IT Improved customer service (e.g. spending analysis) Advanced analysis capabilities Real time operational reporting Optimize marketing campaigns with customer segmentation and channel analytics Enhance cross sell opportunities with customer behavior analytics Use real time branch sales analytics to manage by exception Accelerated global Financial Reporting Predictive modeling analytics for finance Customer retention and acquisition Support regulatory requirements Financial analysis on massive data sets Reduce overall cost of information and IT staff support Improve quality of master data Improved IT support Improved financial management Strategic Workforce Planning incl. simulations and Reporting Risk Management
  • © 2013 SAP AG. All rights reserved. 11 Use Case: Credit Card Analytics and Fraud Management Enabled by Custom Development and SAP Fraud Management Description  Credit Card operator performs analytics on massive data marts Current situation  Cannot perform analytics on current data volumes in reasonable time Value proposition  Allow for data mining of transaction data  Ad hoc analysis on large volumes of non- aggregated data  Detect Fraud Result  Enhanced customer analytics for Financial, Marketing, Customer Service organizations  Fraud Reduction BI Tools •Data Provisioning and Integration Operational Systems (SAP and non-SAP) Transactional Cards Detection and Alerting Investigation and Decision SAP HANA ...
  • © 2013 SAP AG. All rights reserved. 12 SAP HANA for Banking use case examples Sales, Service and Marketing Fast and accurate Risk Reporting Support regulatory Compliance Manage liquidity risks in real time to compare asset values globally Real time fraud detection & action Real Time Analytics in Capital Markets Transactional Banking Sales, Service, Marketing Financial Accounting Operations and IT Improved customer service (e.g. spending analysis) Advanced analysis capabilities Real time operational reporting Optimize marketing campaigns with customer segmentation and channel analytics Enhance cross sell opportunities with customer behavior analytics Use real time branch sales analytics to manage by exception Accelerated global Financial Reporting Predictive modeling analytics for finance Customer retention and acquisition Support regulatory requirements Financial analysis on massive data sets Reduce overall cost of information and IT staff support Improve quality of master data Improved IT support Improved financial management Strategic Workforce Planning incl. simulations and Reporting Risk Management
  • © 2013 SAP AG. All rights reserved. 13 Use Case: Channel Analytics and Customer Segmentation Enabled by Custom Development and SAP Predictive Analysis Description  Manage communication with customers via different channels (ATM, call center, online banking, mail, branch, ...) Current situation  Massive scale of data mart prohibits analytics to be performed in a timely manner. Value proposition  Understand customer behaviors and trends in real time Result  Conduct churn management to minimize loss of customers  Gain insights with advanced statistics  Optimize targeting and customer segmentation  Anticipate customer behaviors and make personalized offers and promotions SAP Predictive Analysis
  • © 2013 SAP AG. All rights reserved. 14 Use Case: Accelerate your Insight into Market Sentiments Enabled by SAP Sentiment Intelligence Description  Analyze your sources and channels and gain more insights Current situation  Increased data volumes through unstructured data. Value proposition  Data harvesting from social media like Twitter, Facebook, blogs etc  Text data processing capabilities for aggregation and insight into unstructured data Result  Gain insights into customer requests for lead generation, product/service issue and topics/context around market sentiments  Be proactive to improve your customer satisfaction CRM (SAP and Non-SAP) Online text sources Social media Sentiment Intelligence SAP Data Services Data Extraction Transfer and Loading (ETL) UI5/Toolkit, BI, Visual Intelligence Insight to Action SAP HANA Text Analysis (NLP) SI Models & Views
  • © 2013 SAP AG. All rights reserved. 15 SAP HANA for Banking use case examples Risk Management Fast and accurate Risk Reporting Support regulatory Compliance Manage liquidity risks in real time to compare asset values globally Real time fraud detection & action Real Time Analytics in Capital Markets Transactional Banking Sales, Service, Marketing Financial Accounting Operations and IT Improved customer service (e.g. spending analysis) Advanced analysis capabilities Real time operational reporting Optimize marketing campaigns with customer segmentation and channel analytics Enhance cross sell opportunities with customer behavior analytics Use real time branch sales analytics to manage by exception Accelerated global Financial Reporting Predictive modeling analytics for finance Customer retention and acquisition Support regulatory requirements Financial analysis on massive data sets Reduce overall cost of information and IT staff support Improve quality of master data Improved IT support Improved financial management Strategic Workforce Planning incl. simulations and Reporting Risk Management
  • © 2013 SAP AG. All rights reserved. 16 Use Case: Liquidity Risk Management KPIs with Real Data - Impact on End to End Processing End to End: 48 hours 10 mins 10 sec => Speed not for the sake of speed but to create tangible benefits 60+ minutes 1 second 220 Mio Cash Flows Standard HANA 4.000x faster End to End: From 2 days down to 10 min “Proposed intraday liquidity reports ‘not feasible’, banks say. Basel Committee's proposed new reports on daily liquidity needs would involve "thousands upon thousands" of data points. Risk Magazine, Sept 2012
  • © 2013 SAP AG. All rights reserved. 17 Monitor key performance indicators Manage alert workload with efficient evaluation, qualification and remediation of fraud Execute mass and real-time detection and stop suspicious business transactions Define fraud detection strategy through simulation and calibration Analyze fraud patterns and define detection rules and models Use Case: Achieve effective fraud management Enabled by SAP Fraud Management powered by HANA SAP HANA * Can be preformed with SAP HANA studio, SAP Predictive Analysis (option) or 3rd-party tools BI Tools •Data Provisioning and Integration Operational Systems (SAP and non-SAP) Transactional Cards Detection and Alerting Investigation and Decision SAP HANA ... High performance processing of very large data volumes
  • © 2013 SAP AG. All rights reserved. 18 SAP Rapid Deployment solutions Example: Enterprise Risk Reporting Fast & Simple • Gain fast time to value with seamless rapid- deployment tools, embedded best practices • Speed end-user adoption with guides and educational materials Example: Enterprise Risk Reporting RDS A complete solution Deployed in weeks
  • © 2013 SAP AG. All rights reserved. 19 SAP HANA for Banking use case examples Financial Accounting Fast and accurate Risk Reporting Support regulatory Compliance Manage liquidity risks in real time to compare asset values globally Real time fraud detection & action Real Time Analytics in Capital Markets Transactional Banking Sales, Service, Marketing Financial Accounting Operations and IT Improved customer service (e.g. spending analysis) Advanced analysis capabilities Real time operational reporting Optimize marketing campaigns with customer segmentation and channel analytics Enhance cross sell opportunities with customer behavior analytics Use real time branch sales analytics to manage by exception Accelerated global Financial Reporting Predictive modeling analytics for finance Customer retention and acquisition Support regulatory requirements Financial analysis on massive data sets Reduce overall cost of information and IT staff support Improve quality of master data Improved IT support Improved financial management Strategic Workforce Planning incl. simulations and Reporting Risk Management
  • © 2013 SAP AG. All rights reserved. 20 Use Case: Real Time Daily Balance and Line Item Detail Enabled by Custom Development Description  Point in time Spot & Average Balance calculations and Liquidity position. Current situation  No Intra Day insight into Financial Position across Spot and Average Balances Value proposition  Provide real time access to financial operational key figure reporting Result  Real Time informed decisions regarding opportunities and risks by managing Balance Sheets Bank Sub- ledger
  • © 2013 SAP AG. All rights reserved. 21 Use Case: Real-time Financial Reporting/Analysis Enabled by Custom Development Description  Real-time financial reporting/analysis allowing organizations to understand their current financial position and management to quickly adapt to opportunities and address problems. Current situation  Financial closing often requires week. Slow financial results are not as actionable as it could be if received sooner. Value proposition  Analysis of the factors (down to transaction level) that drive revenues, expenses and shareholder value across product, customer, channel, etc. Result  Improve financial performance by implementing more effective financial management. Enabled by custom development
  • © 2013 SAP AG. All rights reserved. 22 Use Case: Finance & Risk Data Management Reasons to rethink the Information Management Landscape Transports SOA/Ent.SharedServicesBackbone SOA/EnterpriseSharedServicesBackbone Metadata Management Environment External Data sources Transactional Data Sources (Internal Sources within ICG, GCG, PB, LCB) Reference/Master Data Sources (Includes LOB Operational Master data) Centralized Staging Area Data Acquisition Data Integration Information Access and DeliveryData Storage and Aggregation Validation Cleansing Matching Deduping Certification Control & Audit Integration Transformation Aggregation Calculation Reconciliation A Application Process Repository Capability Landing Zone C/ER/F ODS Adjustments / Restatements / Corrections Regional Data Sources (LATAM, EMEA, NA, APAC) A DQ2 BR Data Quality Management Framework Data Quality Learning Repository Data Profiling Quality Rating DQ Reporting & Dashboarding Data Cleansing Record Level Validation Business Rules Processing/ Management Business Rules Repository Common Rules Processing Business Rules Engine DQ1 BR Lineage Analysis Impact Analysis Metadata Reporting Business Taxonomy Metadata Capture Metadata Search Metadata Repository Metadata Integration Information Consumers & Processors Operational Data Store (ODS) Data Warehouses Data Marts SOA/EnterpriseSharedServicesBackbone Data Security and Privacy Management Encryption Obfuscation Audit & ControlIdentification Audit Repository Role based Access/ Mapping AuthenticationAuthorization Data Federation B C App. Write-backB OLAP Query & Reporting Business Analytics Data Mining / Predictive Analytics Data Delivery Services Notification and Alerts Calculation Engine/Services Dashboarding/ Scorecarding Adjustments/updates/ Corrections Access Point Enterprise Search Enterprise Portals Event Processing Business Access Platform Business Intelligence Platform1 3 4 5 7 6 8 9 10 11 12131415 Data Acquisition Gateway Data Integration Platform Near real time messages Untransformed Data (per Bus Req) File Level Validations DQ2 Common Data Layout (CDL) Processor Analytical Platform Data Access Platform MM MM SOA/EnterpriseSharedServicesBackbone MMC SP SP SP Reference and Analytical Hierarchy Management Reference Data Repository Reference Data Validation Golden/Master Record Generation Reference Data Enrichment RAHM 16 Products Arrangements Involved Party Location Event Classification Assoc. Relationship Condition Profile Area Regulatory/ Compliance Resource Item Summary Area 17 18 Compliance Data Mart (Trade Surveillance Due Diligence AML Monitoring) Compliance Regulatory Reporting Data Marts Treasury & Cash Mgmnt Data Mart Tax Management Data Mart Performance Management Data Mart Legal Vehicle Financials Data Mart Credit Risk Data Mart Market Risk Data Mart Operational Risk Data Mart Messaging ( Real Time ) DBMS Web Service SQL Client File (Batch, intra-day batch, mini-batch etc) Real Time Messages (no persistence / no validation) 2 Customer ISG (Wholesale) CMR (Retail) Product Organization Price/Market Data Data Sources LOB Data Sources (Sector owned) Business Sectors Global Functions Finance Regulatory Reporting Marts Validation Control & Audit Integration Tranformation & Aggregation Messaging (Peer to Peer, Near Real Time, Pub/Sub ) File (Large Batch) Real Time Messages DQ1 Metadata Business Rules Security To Data Marts/Analytical Platform via Data Access Platform Messaging (Batch) DQ1 DQ1 DQ1 DQ1 DQ1 DQ1 DQ1 DQ1 Process Quality Adjustment Calculation Pricing Calculation Engine Match & Merge Reference Data Synchronization Legend Reference Data Transport Pattern Data Monitoring RAHM C B Real Time data (from web services) DW Ouput ODS Output Data Mart Output BPM/BAM/Workflow Monitoring Financials / Performance DW Human Capital DW Wholesale Contracts / Positions DW Consumer Contracts / Positions DW Wholesale Transactions DW Consumer Transactions DW Customer / Legal Entity DW Reference Data Compliance Mantas (Trade Surveillance) Web Pre-Clearance METSS (Monthly Employee Trading Supervision System) OARS (Outside Activity Reporting) Mantas (Anti-Money Laundering) SAS Analytics QuickScreen Case Management SAR (Suspicious Activity Report) Database Actimize (Sanction and Name Screening) SPIDER (Local Disclosure of Interest Reporting) WIN (Want it Now) Electronic Banking System (EBS) Shamus (Fair Lending Risk Mgmt.) CRA (Community Reinvestment Act) Wiz Fair Lending Wiz Management Dashboard Target State Applications Enterprise Risk Wholesale Risk Management/Regulatory Reporting Wholesale Credit Risk Management Dashboard Retail Risk Management/Regulatory Reporting Application Retail Credit Risk Management Dashboard Market Risk Management Dashboard / Scorecard Market Risk Management and Regulatory Reporting Operational Risk Management and Regulatory reporting and Management Dashboard/Scorecard Enterprise Risk Dashboard/Scorecard Finance Citi Franchise Database Contracts/Positions/Balances Warehouse Counterparty Information Warehouse Expense Warehouse Financial Consolidation International Regulatory Intra Citi Billing Management Dashboard Tax Application Treasury Application US Regulatory Compliance eCADD (Electronic Customer Acquisition & Due Diligence) Enterprise Risk Credit Evaluation & Approval Applications Credit Administration Workflow Integration Applications Collateral Management and Monitoring Applications Document Management Application (Banking Book) OTC Derivatives- Documentation and Management Securities Financing -Documentation Management Disbursement Approval and Control Application Credit Trading Book Limit Management and Monitoring Collections and Recovery Applications Economic and Regulatory Capital Calculation Engines Exposure Aggregation Engines Wholesale Credit Analytics Applications - (Capital Adequacy, Stress Testing, Portfolio Limits, Loan Loss Allowance, Loss Forecasting) Retail Credit Reporting & Analytics - (Capital Allocation, Stress Testing, Portfolio Limits, Loan Loss Allowance, Loss Forecasting, Collections Analytics, Fraud Analytics) Market Risk Capital Calculation Engines Market Risk Limits Manager Market Risk Reporting & Analytics - (Capital Adequacy, Stress Testing, Portfolio Limits, Loan Loss Allowance, Loss Forecasting) Operation Loss Event Data Capture Application and Issue Management Application Risk Control Self-Assessment Application Operational Risk Loss and KRI data Aggregation Engine Operational Risk Capital Calculation Engine Operational Risk Reporting Analytics - (Capital Adequacy, Stress Testing, Portfolio Limits, Loan Loss Allowance, Loss Forecasting) Compliance Product Control Applications Accounting Engine Budgeting & Planning Finance Rules Engine Fixed Assets General Ledgers Read only Applications Write-back Applications Consolidation of system landscape  Getting rid of silos and consolidate the application landscape on one data platform Modernization  Oppenness for integration of new solutions  Flixible to extend scope Leverage potential of technology innovations  In-Memory  Zero data latency  Less materilaization and more virtualization of data layers Harmonization of technology stack  Rely on a fully integrated technology stack and reduce diversity of technologies Address regulatory requirements efficiently  Industry Best-Practices (Template approach)
  • © 2013 SAP AG. All rights reserved. 23 SAP HANA for Banking use case examples Operations an IT Fast and accurate Risk Reporting Support regulatory Compliance Manage liquidity risks in real time to compare asset values globally Real time fraud detection & action Real Time Analytics in Capital Markets Transactional Banking Sales, Service, Marketing Financial Accounting Operations and IT Improved customer service (e.g. spending analysis) Advanced analysis capabilities Real time operational reporting Optimize marketing campaigns with customer segmentation and channel analytics Enhance cross sell opportunities with customer behavior analytics Use real time branch sales analytics to manage by exception Accelerated global Financial Reporting Predictive modeling analytics for finance Customer retention and acquisition Support regulatory requirements Financial analysis on massive data sets Reduce overall cost of information and IT staff support Improved IT support Improve quality of master data HR – Retirement Planning Strategic Workforce Planning incl. simulations Risk Management
  • © 2013 SAP AG. All rights reserved. 24 Use Case: Improved IT Support and reduced TCO Enabled by SAP HANA as primary or secondary database HANA as secondary database Data replication HANA DB Reporting + Application Classic DB Application HANA as primary database HANA DB No data replication Reporting + Application Application Description  Usage of in-memory databases to improve service support  Streamlined system landscapes Current situation  Complex system landscapes and huge data duplications  Continuous cost pressure Value proposition  Streamlined system landscape through direct access without need for duplications  Fast access for Business Users to all relevant data Result  Improved IT Service  Simplified landscapes resulting in lower TCO
  • … and beyond
  • © 2013 SAP AG. All rights reserved. 26 Extend Your Analytics Capabilities with Prediction ANALYTICS MATURITY COMPETIVEADVANTAGE Sense & Respond Predict & Act Raw Data Cleaned Data Standard Reports Ad Hoc Reports & OLAP Generic Predictive Analytics Predictive Modeling Optimization What happened? Why did it happen? What will happen? What is the best that could happen? The key is unlocking data to move decision making from sense & respond to predict & act
  • © 2013 SAP AG. All rights reserved. 27 … SAP HANA Predictive Ecosystem SAP HANA Platform Data Pre-Processing and Loading SAP Data Services, Information Composer, SLT, DXC, Hadoop Predictive Analysis Library (PAL) SAP HANA Studio SAP and Custom Applications R Integration for SAP HANA Business Intelligence Clients SAP Predictive Analysis
  • © 2013 SAP AG. All rights reserved. 28 SAP Extends the Power of Predictive Analytics to Unlock Big Data With Acquisition of KXEN, September 10th 2013
  • Some Customer Examples
  • © 2013 SAP AG. All rights reserved. 30 Business Warehouse on HANA: BW on HANA LIVE: Three Customer Examples – Better monitor credit risk and manage the portfolio of property loans Offer better services through comprehensive customer insights Credit Risk & Portfolio Analysis Customer Servicing Business / IT Scenario:  Obtain Customer Credit Information  Ensure Internal and external regulatory requirements Value Proposition  Ensure Compliance  Improved Management Reporting  Risk Transparency Compliance & Risk Management
  • © 2013 SAP AG. All rights reserved. 31 Business Scenarios based on HANA Accelerate and improve Business Processes Business / IT Scenario:  Risk Management including ad hoc simulation capabilities  Consolidation of data and reporting Value Proposition  Accelerated Reporting (from 4 hours to 4 min.)  Consolidation of data and reporting  Less Manual effort  Consolidated IT landscape Business / IT Scenario:  Customer Analytics based on different data sources  Faster Access to relevant information Value Proposition  Improved Customer Service  Acquire and retain customers  Increase Profitability Consolidated Reporting Customer Servicing & new business 5,000 events per second loaded onto SAP HANA 10-30% increase in revenue per year. Interactive data analysis leading to improved design thinking and game planning.  Increase conversion rates from free to paying player  Increase the average revenue per paying player  Decrease churn – keep paying players playing longer Customer Behaviour Analysis
  • Summary/Closure
  • © 2013 SAP AG. All rights reserved. 33 Bringing Together Business And Analytics/Big Data Solution Develop Transformation Map • Develop & Schedule Next Project Steps to Achieve the Defined Strategic Objectives • Cost Benefit Analysis • Final Presentation Identify Use Cases with Innovation/Discovery Workshop • Identify Top pain points to be solved • Identify Value Proposition why is it important Define Strategic Architecture & Governance • Design To Be Architecture and align with Strategy • Define future Governance/Guidelines and Competence Centre Organization & Processes Align Vision And Strategy • Align Strategy and Business Strategy • Identify Pain Points and Consequences • Define Success Factors and KPI to Measure Strategy Run Rapid Deployment Sol. • Develop POC Prototype or start with RDS
  • © 2013 SAP AG. All rights reserved. 34 Conclusion: Real Time Analytics & Big Data for Banking Real time on-demand access to source data helps banks & insurances identify customers and products, analyze risk, optimize performance, and limit fraud. Enterprise wide integration architecture for processes and data flows is a pre- requisite to solve big data problem. Innovative technology enables actionable insight with massive quantities of real time data in the main memory of the server to provide immediate results from analyses and transactions
  • Jörg Bartelt Principal Architect (Analytics/HANA) Global FSI Delivery Services SAP AG Dietmar-Hopp-Allee 16 69190 Walldorf T +49-6227-7-66855 M +49-160-90823047 E joerg.bartelt@sap.com www.sap.com Aslıhan Akın Solution Manager – Banking Industry SAP TÜRKİYE, Anel iş Merkezi Saray Mah. Site Yolu Sok. No:5/17 K:6, 34768 Ümraniye / İstanbul T +90 216 6330434 M +90 530 1730437 E aslihan.akin@sap.com www.sap.com