Flexing Data Muscles for New Challenges The Financial ... · Management and Finance functions resulting in fragmented views of bank health „Stovepipe‟ analytical solutions with

Financial Services

Flexing Data Muscles for New Challenges

The Financial Services Data Warehouse Oracle Open Day, Istanbul Nov 1, 2011

The following is intended to outline our general

product direction. It is intended for information

purposes only, and may not be incorporated into any

contract. It is not a commitment to deliver any

material, code, or functionality, and should not be

relied upon in making purchasing decisions.

The development, release, and timing of any

features or functionality described for Oracle‟s

products remains at the sole discretion of Oracle.

Agenda

• FSDWH History

• New/old issues, new approach

• The Oracle answer

<Insert Picture Here>

FSDWH History

Experience from the past

Inadequate offering •DWH provided without physical data model

•DWH not tested in existing business environment

•Products still under development

Poor planning •Limited understanding of the concept of phased approach

•Unrealistic expectations with limited fall back plan

Technical and HR constraints

•Common “spaghetti” architecture

•Limited amount of available data and of poor quality

•Lack of project management and core technical skills

•Data knowledge limited and application documentation incomplete or not updated

© 2011 Oracle Corporation - Proprietary 5

Many financial institutions-particularly in this region-have already

significant DWH experiences, not necessarily positive, due to a series of

reasons:

Develop/source

Enterprise Logical Data Model 1

Account

Contract

Product

Customer

Txns

Account

Data Warehouse

First Generation DW Design for FSI - Concept Warehouse as a Central Store of all Business Data

3 Inbound ETL

Repeat these steps for each data source or new

requirement….

Map to Source and Physicalize

2

Org

Product

Customer

Wealth

CRM

Trading

Banking

ERP

HCM

Hardware, middleware, database software,

management and administration tools, and supporting

software will also need to be acquired and integrated

along the way…


Risk

Profitability

Customer

Compliance

Proliferation of ETL and data mart silos as

new needs appear – more cost, complexity

and overhead 6

RAPM?

Capital?

Develop/source

Enterprise Logical Data Model 1

Account

Contract

Product

Customer

Txns

Account

Data Warehouse

First Generation DW Design for FSI – Reality

3 Inbound ETL

Most logical data models are overly

abstract and conceptual – with no

defined end use

Ad-hoc ETL’s needed to get data to

analytical applications –traceability

can be broken

4

Map to Source and Physicalize

Org

Product

Customer

Wealth

CRM

Trading

Banking

ERP

HCM

2 Mapping exercises often become

a “boiling of the ocean” exercise

Source ETL can be overly complex with

data quality often focused only on

technical checks

ETLs to marts may

bypass the

warehouse entirely 5

Report2

Report3

Report4

Report5

Report6

Report1

Report1‟

Inconsistencies appear –

data no longer trustworthy 7

??

??


• A Shift in Thinking

Dbms2.com (Monash Research)

Where is the industry today?

TDWI Research Report – Next Generation Data Warehouse Platforms

Information Week



New/Old issues, new

approach

Analytical Applications Infrastructure Challenges Current Situation

Inability to easily combine analytics from Risk, Performance Management and Finance functions resulting in fragmented views of bank health

„Stovepipe‟ analytical solutions with inflexible, non-modular infrastructure are unable to scale in response to increasing internal and external demands

Lack of transparency and traceability within analytical processes resulting in reduced trust in analytical results

Lack of a centralized, consistent operations and security model across analytical applications


Analytical Transformation in FSI Today‟s reality

• Driving to a profitability culture

• Traditional performance measures not enough

• More detailed and transparent understanding of profitability

Managing to Financial Performance

• Understanding compliance in the context of risk and performance key to increasing regulatory demands and competitive advantage

Risk Adjusted Performance Mandatory

• Shift from timeliness and quality of submissions to how business operates

• Increasing frequency of regulatory involvement – i.e. stress testing

• Deeper and deeper understanding of what goes into the submissions

Increasing Regulatory Pressure


Analytical Use cases in Financial Services

• Spanning the

spectrum of speed

and complexity

Algorithmic

Trading,

HFT

Performance

Measurement

Real-time

Marketing

and offers

Regulatory

Reporting

Risk-

based

pricing

Need f

or

Speed

Analytical Complexity

Enterprise-wide Risk

Computations and

Stress Testing

Trading

Desk

Analytics

Trade

Surveill-

ance

Instrumen

t Pricing

Fraud,

AML

Retail

Customer

Behavior

Analysis

The general trend here is

clear

• Far more data…

• Subject to far more

complex analytical

processing…

• With far greater

accuracy…

• In rapidly reducing time

windows


How would your data warehouse manage…

If your users

demanded better

performance?

If your users started

running more adhoc

queries

If you needed to use

more complex

analytics against

bigger datasets?


If you needed to

better manage your

storage

requirements?

If you needed to

reduce your total data

center costs?

If you wanted to store

15 years worth of

detailed data?


How would your data warehouse manage…

Architectural evolution

PROLIFERATION OF SILOS Applications spring up throughout the institution,

serving specific functions…

Manual or limited sharing of data and lack of

consistency become big headaches….

INTEGRATION TO THE RESCUE? Integration within and across common functions,

like Risk, Performance, Compliance and CRM

becomes business as usual, but to this day

remains rigid, costly, hard to trace and audit, not

future proof and easily broken…

A UNIFIED PLATFORM More than integrated… a UNIFIED platform, built

on common infrastructure, data models,

technologies and components… engineered and

designed to work together now and into the

future…


The quest for performance

Liquidity Risk

• New mandates like Basel III are driving need for on-demand answers

• Global events or crises have near instantaneous impact

• Banks need to calculate stressed results in minutes not hours

• Contingency funding strategies need to be iteratively tested in a practical timeframe

Regulatory Capital

• Reporting is stressful and time consuming with little time for analysis

• Performance bottlenecks leave little margin for error

• Demand for additional submissions with modified assumptions and calculations

• Demand for advanced analytical practices like stress testing and what-if analyses

Profitability

• Tighter controls, laws limiting bank fees and slower revenue growth driving need for efficiency and cost controls

• Need for on-demand view of costs and profitability – move beyond monthly reporting requirements.

• Need for faster and more granular review of product performance

• Need to quickly response to market shifts and changing rates.

Senior executives are left scrambling to bridge key limitations in

• Performance & Scalability

• Responsiveness

• Simulation & What-If Capabilities



The Oracle answer

Oracle Is Changing the Rules of the Game Unmatched Solutions Offering for FS in the Applications and

Technology Vendor Landscape

• Unified results and metrics across dimensions.

• Designed for Global banks-Multiple jurisdictions support.

• Data-quality specific to business ideas – not just formats

• Formal GL reconciliation process.

• Pre-built integration with operational finance flow

• Mixed workload handling – our structures allow this

• Pre-built BI apps – results handling expertise (app space)

• Unified active metadata through entire data life cycle

• Complete pre-built metadata covering all analytical disciplines from day

one.

• Hierarchies and reference data are consistent across processing and

results areas.

• One unified warehouse covering all relevant financial services

business areas .

• Ability for customers to use a common AAI framework to modify or

create new calculation/application logic

• Ability to host “externally developed” models and execute them on GL

reconciled production data for consistent outputs under base lined and

stress conditions “ON the DWH”

• Extreme Performance with greater value.


Broad & Deep Offering

Lower Cost, Lower Risk

Comprehensive Offering

Complete

Less Effort

More Value

Designed to Work Together

Integrated

More Choice Maximizes Existing

Investments

Standards-Based Architecture

Open

Oracle's Strategy for Financial Services Industry Delivering Complete, Open, Integrated Solutions


Oracle‟s Banking Footprint

Corporate Administration

Customer Analytics

Channel Analytics

Marketing Analytics

Exec Analytics

Funds Transfer Pricing

Portal & Dashboards

Business Activity Monitoring

Business Process Analytics

Op Intelligence

HR Analytics

Customer Insight

Performance Management

Operational Excellence

Analytics Platform

Consolidation Budgeting

Procurement

Profitability Mgmt

Fixed Assets

Incentive Comp

Product Hub Org Hierarchy Hub Accounting Hub Payments Hub

Master Data Management

Customer Data Hub

Rich Client F/W AJAX, Web 2.0

Systems Mgmt Web Services Mgmt Directory Services

Enterprise Content Mgmt

Database (Grid, Memory, Embed)

Web Services Orchestration (BPEL)

Identity Mgmt

J2EE Services, ESB and Rules Engine

Access Mgmt

Enterprise Technology

Profitability Analytics/RAPM

ACH

Credit Rating

POS

SWIFT

Fed Wire

OFAC

Market Feeds

Check Order

Other Systems

Retail

Platform Teller Call Center

Internet Banking Kiosk EBPP

Private Wealth Mgmt Commercial

Team Selling

Order Mgmt

Internet Banking

Security Trading

Client Servicing

Derivative Pricing

CRM & Marketing

Lead Mgmt

Order Entry Platform

On Demand

Customer Experience

Call Center

Deposits

Loans

Payments

Cards (Credit / Debit) Investments

Core Banking Ledger Leasing

Payments

Deposits

Treasury Trade Finance

Asset Mgmt

Syndicated Loans

Structured Derivatives

Lock Box Nostro Recon

Investor Services

Leasing Custody

Mortgages

Customer & Business Insight

Lifecycle Management

Selling Marketing Servicing Originating

External Interfaces

Projects

Risk Based Pricing

Economic Capital/RAPM

Operational Risk

Market Risk

Credit Risk

ALM

SOX

Regulatory Reporting

Risk Based Decisioning

Risk Management

Governance, Risk & Compliance

Compliance

BASEL II & 1A

AML

Human Capital Mgmt

Enterprise GL

Product and Transaction Processors

Cash Mgmt

Real Estate

Savings Loans

Core Banking Ledger

Compliance & Limits

Portfolio Mgmt

Trade Processing

Performance Mgmt

Balance & Positions

Fees & Commissions

Compensation

Clearing & Settlement

Oracle

Third Party

Integration

Market Data

Reconc

iliation

Financial Planning

Channels

Core Banking

Technology & Operations

Analytics

An

aly

tic

s

An

aly

tics


Performance

Management

Customer

Insight

Governance

& Compliance

Risk

Management

Oracle Financial Services Analytical Applications

Hedge Management IFRS 9 – IAS 32/39

ICAAP

Customer Profitability

Stress Testing

Loan Loss Forecasting Pricing Management

RAPM

Balance Sheet Planning

Know Your Customer

Treasury Risk

Credit Risk

Governance and Compliance Regulatory Compliance (Financial Crime)

Channel Insight

Analytical CRM

Anti-Money Laundering

Trading Compliance Broker Compliance

Fraud Detection Operational Risk

Retail Credit Risk

Corporate Credit Risk

Portfolio Analytics

Marketing Analytics

Service Analytics

Channel Usage

Channel Performance

Economic Capital

Regulatory Capital

Liquidity Risk

Economic Capital Advanced (Credit Risk)

Operational Risk Economic Capital

Performance Management and Finance

Accounting Hub

Activity-Based Costing

Consolidation Profitability

Budgeting and Forecasting

Asset Liability Management

Market Risk

Basel II

Retail Portfolio Risk Models and Pooling

Funds Transfer Pricing

Reconciliation

Comprehensive coverage, derived from deep core

banking domain expertise, provides best of breed

capabilities in key disciplines.

Unified platform supports analytical “intersections” to

address emerging or overlapping analytical needs without

extensive “re-wiring” and rebuilding of supporting data

infrastructure.

Financial Services

Data Warehouse


Oracle Financial Services Meets Today‟s

Demands Financial Services Data Warehouse

Capabilities: • Pre-built business oriented data quality and reconciliation processes

• Pre-built, readily deployable, end-use proven, physical data model for FSI

• Industry leading FS analytical applications pre-built and ready to run IN the Data Warehouse

• Unified and conformed model for reporting and business intelligence across all functional domains

• Optimized to leverage the power of EXADATA to ensure support for new analytical use cases

The foundation for a comprehensive analytical processing platform

Can readily host both Oracle Financial Services Analytical Applications and other custom built or 3rd party engines

Analytical Application Processing (Within Warehouse)

Results Sourcing

Results for consumption Common input area for analytical processing

External Calculation Engines

Oracle Calculation Engines & Statistical

Models

Analytical Applications Infrastructure (Data Quality., ETL, Metadata, Stress Testing, Modeling, Execution, etc)

Hosted Statistical Models


FS Logical Data Model

Transactions (Events)

State (Contracts)

Party

Organization

Calendar

Product

Location

Oracle Financial Services Data Warehouse Standards Based LDM to extend Warehouse Model

Core Subject Areas:

• Events (red):

• The recorded, time-stamped results of

executing FS business processes

• Transaction is the primary concept

• State (grey): Current snapshot of FS organization

• Cumulative effect of events

• Contract, Account are primary concepts

• The primary raw material for analytics

• Domain (FS)specific, and central part of the

FSDM

Context Subject Areas:

• Related „Dimensions‟ for core data

• „Who‟ – Party, Organization Subject Areas

• „What‟ – Product Subject Area

• „Where/When‟ – Geography, Calendar

Subject Area

• Not FS specific, but customized for FS domain


Oracle Financial Services Data Warehouse Financial Services Physical Data Model

• Pre-built, comprehensive and ready-to-deploy

• Covers all classes of FSI business data

• Pre-built and fully conformed to meet FS reporting needs

• Complete mapping between sourcing and results


Results Sourcing

Results for consumption Common input area for analytical

processing



Models




Oracle Financial Services Data Warehouse Common Sourcing

• Pre-built, comprehensive and ready to deploy

• One sourcing area shared across all analytical needs

• Segments data elements based on analytical needs


Results Sourcing


processing



Models



Facts Dimensions

Financial

Instrument

Transaction

Ledger

COA

Product

Org

Customer

Exposure

Time Series Data (Rates,

Indicators, risk factors etc)

Display Codes/Master tables

Master Data

Campaign


• Fully conformed dimensions for cross-functional analysis

• Pre-built, comprehensive and ready to deploy

• Eliminates data mart silos

• Multiple jurisdictions

• Results versioning

Oracle Financial Services Data Warehouse Unified Results Across All Key Analytical Areas


Results Sourcing


processing



Models



End-use Specific Facts

Common Conformed Dimensions

COA

Product

Org

Customer

ICAAP, Economic Capital

ALM, Basel II, Basel III …

RAPM, Profitability, Liquidity …

Time Series Data

(Rates, Indicators, risk factors etc)

Shared BI Facts


66 Million Exposures. 371 Million Cash Flows.

1 Billion Transactions. 250 Million Accounts. 172 Rules.

65 Million Exposures.

Profitability on a daily basis

Intraday liquidity analysis

Regulatory capital in minutes not days

Process (Rules Execution Across Different Rule Types) Elapsed Time

Allocation Rules on Transactions (41) 1:02:01

Allocations from Ledger to Transactions (6) 14:42

Aggregation of Transactions to Ledger (6) 0:00:58

Aggregation from Transactions to Instrument (18) 1:08:00

Instrument level Allocations (42) 0:55:02

Ledger to Instrument Allocations (10) 0:25:40

Ledger level Allocations(30) 0:15:06

Instrument to Ledger Aggregations(19) 0:44:22

Total (172) 4:45:51

Process Elapsed Time

Contractual Liquidity Run 0:59:42

Business As Usual Liquidity Run 0:09:19

Baseline Run 0:69:01

Stress Test Run 0:10:06

Process ELAPSED TIME

Data Load from Stage Tables to Basel Processing Tables 0:02:07

Pre-Processing of Data (for RWA Calculations) 0:16:24

Risk Parameter Estimation 0:19:49

Risk Weighted Assets (RWA) Computation 0:51:01

Total Elapsed Time 1:29:21

OFSAA and Exadata Performance in Action What Could YOU Do With Extreme Performance

OFSAA on Exadata

Performance Test Results Confirm

the Possibilities


Oracle Data Warehouse Solution for Typical Bank


Oracle Value Proposition – Time & Risk

Oracle FS Data Warehouse:

drastically shortened time to

value, significantly reduced

implementation risk. Pro

du

ctio

n &

Ro

llou

t

De

sig

n, B

uild

,

& T

est

Re

qu

ire

me

nts

&

An

aly

sis

18-26 Weeks

Standard DW Implementation: significant time to design, build, implement & test.

Production &

Rollout Design, Build, & Test

Requirements &

Analysis

2+ Years


“Traditional” Activities Related to DW

Implementation

Phase Activity Typical

Duration Complexity

Extent of Bank

Resource

Involvement

Requirements Conduct Business Requirements

Study and create BRD

3 months High. DW vendors start from a

“blank sheet”

High

Analysis Map BRD to logical model, detailed

source system analysis, build data

requirements

6 months High. Use cases not

considered during data

sourcing.

High

Design Create extract specs, design data

quality process, create source

mappings to LDM, design

reconciliation process, design

metadata, design OLAP & BI

structures

7 months High. BI report deliverables

considered long after data

model design necessitating

multiple iterative changes.

High

Build Create ETL, create DQ checks and

process, create metadata repository,

build reports, build manual

adjustment process

6 months High. From -scratch build of

customer specific DQ,

metadata and adjustment

processes

Medium


Beginning With the End in Mind… Gets us the data we need

Corporate Credit Risk

Retail Credit Risk

Finance, Treasury

Marketing

Required Data Elements to

Ensure Relevant

Outputs Can be

Generated

Appropriate Data Quality

Checks to

Ensure Sanctity

of Data for

Outputs

Needed Reconciliation

Process to

Ensure Results

Tally to GL

Needed Value-add

Computations to Produce the

Outputs

Desired Use Cases (Outputs)

in a Given

Subject Area


Accelerating New Business Requirements The Oracle Advantage

Business

Solution

Requirement

New Data

Elements

Needed to

Address

Requirement

Data Elements

Already Sourced

& Needed for

New Solution

Net New

Incremental

Data Elements

Required

Advantage

Corporate

Credit Risk

Analytics

360 N/A N/A

Complete clarity up-front

on all data requirements to

address Business

Requirements

Retail Credit

Risk Analytics 450 180 270

40% reduction in Data

Sourcing Effort for new

Solution

Analytical

CRM/

Marketing

Analytics

1400 300 1100

25% reduction in Data

Sourcing Effort for new

solution


Typical Data Warehouse implementation vs

Oracle Financial Services Data Warehouse

-10 10 30 50 70

Oracle

Traditional

Months Phase 1 - Corporate Credit Risk Phase 2 - Retail Credit Risk Phase 3 - Marketing, Finance

Oracle Advantage: Bank can deploy future use cases

without Oracle involvement


What Could You Do? With Extreme Performance Like This

Liquidity Risk

• Address regulator requests in minutes rather than hours or days

• Understand impact of global economic events as they happen

• Tune contingency funding strategies as events unfold

• Run multiple stress scenarios to fully understand complex events

Regulatory Capital

• Collapse regulatory reporting run times to hours instead of days

• Turnaround regulatory resubmissions or adjustments in minutes instead of days

• Easily and practically perform advanced analytical practices like stress testing and what-if analyses

Profitability

• Capture a daily view of profitability and risk adjusted performance

• More proactively manage your business

• Quickly assess the profitability of new products and programs

• Run your institution based on today instead of last month


Alfonso Asaro Director Europe

Analytical Solutions Initiatives Group

+43.1.33777.247 (Office)

[email protected]

Documents

Flexing Data Muscles for New Challenges The Financial ... · Management and Finance functions resulting in fragmented views of bank health „Stovepipe‟ analytical solutions with