big data in financial services

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Big Data in Financial ServicesWe Do Hadoop


Global banking trends

Source: E&Y


Key focus areas within the financial services industry

Internal domains..

Please note that this is not a comprehensive list of deployed use-cases across the major domains, just the major areas in which industry shifts are occurring and where customers are looking to deploy enterprise Big Data in.

External Customer facing domains..

Risk MgmtCyber Security

Fraud Detection

Data

ComplianceDigital Banking

360 degree view

Capital Markets

Retail Banking and Lending

Credit Cards;Payment Networks

Wealth Mgmt

Corporate Banking

Asset Mgmt(Brokerage, MF and Stock exchanges)


My Experience in Risk & Compliance

Mike DeSanti

November 4th, 2015


• The move from proprietary to flow-based trading in Capital Markets is affecting revenue

• Fintech companies are taking over traditional revenue sources in the lending and payments spaces

• The millenials reliance on mobile technology and instant gratification• Increased regulatory spending is strangling discretionary spending• Having to deal with very dated and expensive IT infrastructure

Issues Affecting Most Banks


• Fragmented Book of Record Transaction systems– Lending systems along geographic and business lines– Trading systems along desk and geographic lines

• Fragmented enterprise systems– Multiple general ledgers– Multiple risk systems by risk function

• Credit limit management, traded credit, Basel capital systems, CVA, Market Risk VaR, Stress VaR, Market Risk reporting…

– Multiple compliance systems by business line by compliance initiative• AML for Retail, AML for Commercial Lending, AML for Capital Markets…

• They are full of proprietary vendor and in-house built solutions that have been acquired over the years

What I Have Seen at Banks


Leads to Current State Complexity• Thousands of point-to point feeds to each

enterprise system from each transaction system

• Data is independently sourced leading to timing and data lineage issues

• Close processes are complicated and error prone

• Reconciliation requires a large effort and has significant gaps

Book of Record Transaction Systems

Enterprise Risk, Compliance and Finance Systems


• Centralize business and operation functions• Incentivize technology to shrink not grow• Populate a data lake that with a set of canonical feeds from the transaction

systems• Create a linearly scalable platform to host enterprise applications on top of this

data lake, including hot, warm and cold computing zones• Develop a partnership with Hortonworks to evolve the platform to meet the

Banking Industry’s needs

How Do We Change?


• A free open source linearly scalable platform has only become available within the last few years

• Due to the amount of regulation over the last 15 years all bank enterprise compliance, risk and finance systems now function essentially the same way

• Banks partnering with an open source partner is very different from partnering with a vendor who develops proprietary software

• Proprietary software vendors will adopt the new standards since it is in their self interest to do so

• Regulators can now streamline their regulatory practices by adopting this platform

Why Will This Work Now?


Open Platform for Risk & Compliance David Lattimore-Gay (Eikos Partners)


What is OPRC ?

• Leveraging Open Source– Large Development Community– Innovation– Reuse not build

• Commodity hardware– Data Storage– In Memory Cache– Computing Power

• Light weight way for ingesting and storing data– Single Source Data– Multiple views on the same data with Schema on Read capability– Built in data lineage

• Unified development environment for analytics– Partnership between quants and IT developers that will allow IT to package analytics for deployment rather

than recoding them


Data Lake

OPRC Originating Source System

Source System

Canonical

L1Mapped

to Schema

CacheReference / Transaction Data / Market Data / Static Data / Meta Data

Reference Data

ValidationNormalization

PK/AK

What has changed ?Load

L0Raw Data

Reporting

Derived Data


Compute Service

Grid

Web Services

UI Framework

In Memory API

Data Fabric

Desktop Mobile

Data Access API

Task

Calculator Framework(Job Create/Monitor/Control)

Task

Task

TaskTask Task

Task

Task

Compute Service Strategy Engine

Task Dependency Scheduler

Task Dependency Scheduler

Task

Aggregation & Reporting Engine

Data Lake

Standardized Book of Record Transaction

(BORT) & Other Systems Feeds

ETL Framework

OPRC High Level Architecture


Technology ArchitectureNadeem Asghar


OPRC Functional Requirements


Proposed Solution based on Hortonworks Stack


OPRC Game Plan:Solution Based on IP(Above the line)/ Open Source(Below the Line)


Wrap up..


Hadoop is a Platform Decision

Adoption follows a consistent journeyData architecture efficiencies, new analytic apps, and ultimately to a “data lake”.

HDP: A centralized architecture built on YARNAny application, any data, anywhere.

HDP: A completely open data platformPlatforms are ultimately defined by open communities.

HDP subscription supports entire lifecycleWorld class experience to ensure success from architecture to production to expansion.


Cautionary Statement Regarding Forward-Looking StatementsThis presentation contains forward-looking statements involving risks and uncertainties. Such forward-looking statements in this presentation generally relate to future events, our ability to increase the number of support subscription customers, the growth in usage of the Hadoop framework, our ability to innovate and develop the various open source projects that will enhance the capabilities of the Hortonworks Data Platform, anticipated customer benefits and general business outlook. In some cases, you can identify forward-looking statements because they contain words such as “may,” “will,” “should,” “expects,” “plans,” “anticipates,” “could,” “intends,” “target,” “projects,” “contemplates,” “believes,” “estimates,” “predicts,” “potential” or “continue” or similar terms or expressions that concern our expectations, strategy, plans or intentions. You should not rely upon forward-looking statements as predictions of future events. We have based the forward-looking statements contained in this presentation primarily on our current expectations and projections about future events and trends that we believe may affect our business, financial condition and prospects. We cannot assure you that the results, events and circumstances reflected in the forward-looking statements will be achieved or occur, and actual results, events, or circumstances could differ materially from those described in the forward-looking statements.

The forward-looking statements made in this prospectus relate only to events as of the date on which the statements are made and we undertake no obligation to update any of the information in this presentation.

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