The path to a Modern Data Architecture in Financial Services

© Hortonworks Inc. 2011 – 2016. All Rights Reserved

The Path to a Modern Data Architecture in Financial Services

Vamsi Chemitiganti GM for Banking & Financial Services,

Hortonworks @Vamsitalkstech


Lee Phillips Sr. Director, Product Management,

Attivio


Speakers Lee Phillips Sr. Director, Product Marketing Attivio

Vamsi Chemitiganti GM, Financial Services Hortonworks

Part of the Product Marketing team and responsible for analyst relations at Attivio, Lee brings over 35 years of experience in product, marketing, and business development in software and information solutions. His background includes MSE, management, and senior management positions for market innovators such as Lotus, Borland, Ziff-Davis, FAST, and NewsEdge.

Vamsi is responsible for driving Hortonwork's technology vision from a client business standpoint. The clients Vamsi engages with on a daily basis span marquee financial services names across major banking centers in Wall Street, Toronto, London & Asia, including businesses in capital markets, core banking, wealth management and IT operations.

Agenda

•  Introductions

•  Trends in Financial Services Risk & Compliance

•  Trends in the AML Space

•  Why Open Enterprise Apache Hadoop for Modern Data Architectures

•  Architectures & Work Streams

•  An AML Case Study

•  Q & A

Page 4 © Hortonworks Inc. 2011 – 2016. All Rights Reserved

Big Data in the Financial Services Industry


Hortonworks Key Focus Areas in Financial Services

Common Focus Areas Segments of Banking

Risk Mgmt Cyber Security

Fraud Detection

Predictive Analytics

Data

AML Compliance Digital Banking

360 degree view Customer Service

Capital Markets

Corporate Banking and Lending

Credit Cards & Payment Networks

Retail Banking

Wealth & Asset Management

Stock Exchanges & Hedge Funds

+

Demand drivers for Big Data in Retail Banking & Capital markets Catalyst Definition Example

Larger data sets Larger data sets allow analysts to query and conduct experiments with fewer iterations

Omnichannel data, Tickers, price, volume and longer time horizons. Social media/ third party data

New types of data New data types that need to be synthesized for traditional relational databases

Business process data, Social Data, Sensor & device data. OTC contracts and public filings.

Analytics and visualization

More powerful analytics and visualization tools to explain and explore patterns – Fraud, Compliance & Segmentation

Complex Event Processing (CEP), predictive analytics. Portfolio and risk management dashboards

Tools and lower-cost computing

Open source software tools. Lower server and enterprise storage costs

Hadoop, NoSQL. Commodity hardware. Elastic compute capacity.

Transformation

--- Maturity Stages à

Optimization Exploration Awareness

---

Mat

urit

y St

ages

à

Peer Competitive Scale

Standard among peer group

Common among peer group

Strategic among peer group

New Innovations

No Use Case Name

1 SingleViewofIns/tu/on

2 PredictRiskExposures

3 PredictCounterpartyDefault

4 Automa/onofClientDueDiligenceforconsumeronboarding

5 EnhancedTransac/onMonitoring

6 EnhanceSARAccuracy

7 CreditRiskCalcula/on

8a RegulatoryRiskCalcula/ons–BaselIII&CCAR

8b RegulatoryRiskCalcula/ons–BaselIII&CCAR

9a Calcula/ngVaRacrossmul/pletradingdesks

9b Calcula/ngVaRacrossmul/pletradingdesks

10 CalculatecreditrisksacrossavarietyofloanporRolios

11 InternalSurveillanceofTradeData

12 CAT(ConsolidatedAuditTrail)/OATSRepor/ng

13 EDWOffload

Corporate & IT Functions

Trading Desks

Retail Banking Use Cases are available at different levels of maturity

Surveillance

Security & Risk

2 8a

5

7 1

6

3

4

9a

10

11 12

8b 9b

13

©2015 Attivio, | Proprietary and Confidential

GOVERNANCE, RISK, AND COMPLIANCE TRENDS

REGULATORY PRESSURE, ENFORCEMENT SCRUTINY

Multiple frameworks increase the economic cost of monitoring

A CRISIS FOR DATA MANAGEMENT

Increasing volume, velocity, and variety of Risk & Compliance data

QUEST FOR EFFICIENCY & EFFECTIVENESS

Shift from manual to cognitive and automated processes


THE MOST COMMONLY CITED CHALLENGES

Global Inconsistency

Absence of uniformity across jurisdictions raises

regulatory scrutiny

Lack of Cognitive Understanding

Must make sense of an explosion in unstructured

information

Information Fragmentation

Multiple silos, solutions, and sources create expensive friction


Achieve Certain, Global Impact

A single-view of the transaction or entity,

across jurisdictions

Correlate Information for Understanding

Discovery of the structure inherent in unstructured

information

Unify Information

Virtual integration across multiple silos, solutions,

and sources

REQUIRED: A HOLISTIC, COGNITIVE SOLUTION


Illustrative Use Case – Anti Money Laundering


General Trends in AML

Trends •  Increasing levels of criminal sophistication

•  Illicit activities span geographies, products and accounts

•  Expert systems and rules-engine approaches are becoming less effective

•  Inefficient investigation tools and processes aren’t keeping up

Impacts for AML

•  Programs must evaluate multiple, varied data sources

•  Require a 360-degree view across much larger data sets

•  Automated, predictive approaches must replace manual, reactive programs

The Current State of AML Data Analysis

•  Investigators demand interactive, visually appealing user interfaces

•  Data discovery and predictive analytics can show deeper customer trends

•  Aging technologies and their supporting approaches should be retired

•  Companies are adopting advanced risk classification approaches

•  New technologies help reduce the number of “false positives”


ANALYTICS DRIVE COGNITIVE SEARCH

BEHAVIORAL ANALYTICS Surprise Factor

Improbability Scores

Outlier Detection

IMPROVED RISK SCORING

Rule Management

Alert Logic

Layered Scoring

STATISTICAL EXTRACTION

Stock Tickers

Credit Card Numbers

CUSIPS

RUNTIME ENHANCEMENTS

Extreme Scale

Rapid Document Processing

Immediate Rule Applications


How Current AML Solutions Fall Short


What We Have Seen at 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 Enterprise Risk Systems •  Multiple compliance systems by business line •  AML for Retail, AML for Commercial Lending, AML for Capital Markets… •  Lack of real time data processing, transaction monitoring and historical analytics

Proprietary vendor and in-house built solutions •  Acquisitions over the years have built up a significant technological debt

•  Unable to keep pace with the progress of technology

•  Move to combine Fraud (AML, Credit Card Fraud & InfoSec) into one platform

•  Issues with flexibility, cost and scalability


AML: STRENUOUS CHALLENGES

Speed, transparency, and auditability for each new

framework

Increased Expectations of Regulators

Complexity Integrating Application & Data Silos

Manual Process Wastes Millions in OpEx

“Overtime reviewers made more than our Execs…”

Chief Data Officer

Typical case reviews involve over 125 facts

from 20 sources

…And the Data Complexity Continues to Grow

•  Tens of point-to point feeds to each enterprise system from each transaction system

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

•  Business processes are complicated and error-prone

•  Reconciliation requires a large effort and has significant gaps

BookofRecordTransac/onSystems

EnterpriseRisk,ComplianceandFinanceSystems


CLASSIC CONFIGURATION OF DATA SOURCES


HOLISTIC COMBINATION OF DATA SOURCES

GRC DATA UNIFICATION PLATFORM


Illustrative AML Use Cases and Work Streams


Leading AML Use Cases

•  Large transfers across geographies

•  Single view of a customer with multiple accounts

•  Linked entity analysis

•  Watch-list monitoring and data mining

•  Credit card fraud detection

Major areas of activity around AML.. •  Automating Due Diligence around KYC data

–  Simple information collected during customer onboarding –  More complex information for certain entities –  Applying sophisticated analysis to such entities –  Automating Research across news feeds (LexisNexis, DB, TR, DJ,

Google etc)

•  Efficient Case Management

•  Applying Advanced Analytics (two sub Use Cases) –  Exploratory Data Science –  Advanced Transaction Intelligence

Stream Processing

Storm/Spark ML

Reference Architecture for Fraud/AML/Compliance

Stream

Flume

Sink to HDFS

Transform

Dashboard

UI Framework

ELT

Hive

Storage

HDFS/Spark ML

Stream

Kafka Stream to Kafka

Stream to Flume

Forward to Storm

Monitoring / KPI NoSQL

HBase Real-Time Index

Search

Solr

ELT

Pig

Batch Index

Alerts

Bolt to HDFS

Dashboard

Silk

JMS Alerts

Interactive

HiveServer

Visualization

Tableau/SAS/ETC

Reporting

BI Tools Batch Load

High Speed Real Time and Batch Ingest

Real-Time

Batch Interactive

Machine Learning Improved Models

Load to Hdfs

SOURCE DATA

Customer Account Data/

CRM/MDM

Transaction Data

Order Management

Data

Click Stream Log//Social Data

Documents

EDW

File

REST

HTTP

Streaming

RDBMS

Sqoop

JMS


AN EFFICIENT, SCALABLE, AND ANALYTIC ANSWER


ANTI-MONEY LAUNDERING Case Study

26


SOLUTION REQUIREMENTS

Generates automatic case summaries and narratives from all relevant R&C systems, providing a consistent, holistic view of suspect transactions:

•  Gathers relevant facts from every R&C solution or data source

•  Provides multi-lingual text analytics that support key phrase detection, entity extraction, and synonym expansion in unstructured content sources

•  Initiates alerts and triggers when specific words, phrases, or content are detected during processing

•  Provides –best-in-class search capabilities that power forensic investigation

Provides proactive monitoring and compliance across the

entire organization


“SINGLE-PANE” SOLUTIONS

Assignment

Investigation

Narrative


INTEGRATE & OPTIMIZE : RESOLVE CASES FASTER

Challenge – Achieve a productivity breakthrough to reduce compliance cost

Attivio Solution – Deliver all evidence to a single screen for review and reporting

Outcome – 75% reduction in MTTR for case investigations


INTEGRATE & CORRELATE : REDUCE “False Positives”

Challenge – Reduce ‘false positive’ costs without missing true positives

Attivio Solution – Deeper analytics adds risk scoring to violation screening

Outcome – Reduced ‘rules’ footprint and over 85% decrease in ‘false positives’


Achieve Global Impact Act With Certainty

Crush Your Deadline Transform Productivity

$27M to

$54M

Instantiate consistency and improve accuracy Confidently seize opportunities and mitigate risks by considering the right information in context

Unify and enrich all evidence silos to save time Immediately discover and provision new evidence, when needed, for timely insight

$2M to

$3M

$29M to

$34M

$8M to

$9M

THE VALUE : $66mm - $100mm ANNUALLY

•  Discover, profile and correlate all internal and external data for agile insight

§  Reduce time for Investigators to review, research and gather to close cases more quickly

•  Reduce reliance on IT to provision data

•  Connect or modify evidence sources as regulatory frameworks evolve

•  Use outcomes analysis to increasing alerting accuracy- reduce ‘false positives’

§  Protect the brand and reduce risk resulting due to inaccurate or delayed reporting of suspicious activity

§  Scale AML solution globally

§  Expedite access to case information to efficiently assign, research and close cases

§  Uniform risk-scoring §  Close all cases; eliminate sampling and

backlogs


PRINCIPAL BENEFITS

Increases investigation throughput by up to 300%

Transforms Investigator Productivity

Reduces Complexity by Integrating All Sources

Reduces Risk to Brand Value

Close 100% of cases, even the most complex

Provide all evidence on a ‘single-screen’

The Advantages of Big Data AML Solutions •  Hortonworks Data Platform (HDP) is a linearly scalable platform already in

use at many of the world’s largest financial services companies •  Hortonworks takes a 100% open-source approach to Connected Data

Platforms that manage data-in-motion and data-at-rest •  Partnering with an open source vendor gives banks more options than

choosing a proprietary software platform •  Regulators are streamlining their regulatory practices by adopting a Big Data

approach

Contact Hortonworks to discuss your journey to actionable intelligence for AML

Questions?


Thank You Vamsi Chemitiganti GM, Banking & Financial Services, @Vamsitalkstech Hortonworks hortonworks.com Lee Phillips Sr. Director, Product Marketing Attivio attivio.com


Technology

The path to a Modern Data Architecture in Financial Services