Retail Banking

Customer Experience
Optional Subhead or Speaker Name
Hortonworks Inc. 2014. Nvent Data All Rights Reservedc
(maximum two lines)
Enabling The Data Driven Enterprise

© Hortonworks Inc. 2014. Nvent Data All Rights Reserved
Cross-Channel Analytics
Offers & responses not consistent across channels
Customer confused when presented identical offer on
one channel after declining on another
Product uptake low
Offers & responses across all channels are
stored in the data lake
Offer/response repository consulted before
making new offer
Consumer channel preferences determine how
next offer is made, per customer, e.g.
Retail merchant offers via phone
Food & beverage via ATM kiosks
Loans on Internet
Retail Banking
Problem Solution
Customer communications not uniﬁed
across channels
Cross-channel analytics determine
best offer for given channel
Large Multinational Bank
2Enabling The Data Driven Enterprise
apps

Each department has a limited view of customer
Limited collaboration within the bank when
servicing customer needs
Provide a user experience that rivals Facebook,
Twitter or Google
Employs 13,000 technologist at the bank
Based on an HDP data lake for all customer data
Provide a full picture of customer across all touch points
Offers and interactions vary
Problem Solution
No Comprehensive View of Customers
Single Customer View
Retail Banking
apps
Single Customer View

Traditional advertising by merchants is a
‘spray and pray’ exercise for the most part Analyze spending patterns
Transaction data for all customers and channels
is pooled into a Data Lake
A good example of using the same data lake
for multiple purposes fraud, risk modeling
and targeted offers.
Also used for Fraud detection
Revenue Increase Make discount offers on behalf of merchants
based on consumer spending patterns
Retail Banking
apps
Problem Solution
Spend Analysis

Optional Subhead or Speaker Name
(maximum two lines)
apps
Finance & Treasury

Storage costs mean only six months of data could be kept
Model accuracy affected
Simulations use only limited signals (data sources)
Portfolios at risk
Data kept indeﬁnitely
Augmented with data from other LoBs
Now simulating things like consumer demand and
macro-trends
HDP Based Operational Data StoreRisk simulations use limited data
Problem Solution
apps
Simulations Retail Banking
investment bank

Historical data kept on tape
Limited data & time frames
HDP augments Oracle solution for historical reporting
and deep analytics
Tableau for reporting
Reduces costs & compliance risks
‘warm archive’ in HDPReporting Data in Hard to Access
‘cold storage’
Problem Solution
apps
Capital Markets
Large NA Bank
Liquidity Risk

(maximum two lines)
apps
IT & Operations
OPTIONALSubhead Speaker Name
or

Ticker plant daily ingest of 50GB server log data from
10,000 feeds on stock trades
4X daily, this data is pushed into DB2, where it is queried 35K
times per second
Current architecture can only hold 10 years trading data
and growing volume puts performance
at risk of missing SLAs
70% of queries are for data <1 year old, 30% for >1 year old
HBase provides super-fast lookups over millions of
columns and billions of rows
Offloading ETW to Hadoop allows longer data retention
at an economical cost, without jeopardizing
fast response times
Low latency HBase table provides
trading data to customers, well
within SLA targets
Major financial data company offers
ticker plant, challenged to meet
12 millisecond SLA
Problem Solution
apps
Capital Markets
Financial software, data and media company
EDW Offload

Investment bank generates valuable data assets which
are unavailable in aggregate
Current EDW solutions that work for some data jobs are too
expensive for others
Short retention hampers analysis of price history
and performance analysis
ETL offload will save money for Hadoop-appropriate
workloads
Longer data retention enriches analysis of stock
price histories
Joined data sets also provide insight into customer
acquisition costs
Accumulo enforces security with read permissions
down to individual data cells
Financial log data is difficult to aggregate and
analyze at scale
Why Hadoop? Data Enrichment
Multi-tenant Hadoop cluster merges
server log data across groups to
uncover trends across assets, traders
and customers
Server log data unavailable for
broad market analysis
Problem Solution
apps
Capital MarketsETL Offload Global investments company

The newly formed big data innovation center struggled
to understand understand the 3 V’s of data pouring
into the organization
Data was fragmented in LoB silos and not accessible
to analysts
Data volumes growing at 100%/annum and getting faster.
Storage costs spiraling out of control
101 generic compute nodes
20 real-time analytic nodes
32 master nodes
July 2015 will be > 1000 nodes
An Enterprise Wide Data Lake
started in 2014:
Data Innovation Center could not
locate and process data
apps
Problem Solution
Data Lake Retail/Investment Banking/Insurance
> $80 billion revenue > 250K employees

RISK & COMPLIANCE
(MAXIMUM TWO LINES)
apps
OPTIONAL SUBHEAD
or
SPEAKER NAME

Problem Solution
apps
Multi-Channel Fraud Detection
Proper holistic view of customer requires
non-monetary events
Storage and platform limitations prevent rule simulations
at scale on historical data
Fraud Detection Only Considering
Monetary Events
Retail Banking
Streaming analysis of transactions
Rule simulations tested against historical active archive
Machine learning runs across historical active archive for
further analysis of fraudulent activity (i.e. outliers)
Analysis Monetary and Non-Monetary
Events to allow Closed Loop Analytics
for continually improving detection

Multi Channel Fraud Architecture
Transactions
Monetary and
Non-Monetary
apps
Scoring Engine
RT Batch
Monetary
Hive
HDFS
Non-Monetary
Closed Loop

Problem Solution
Investment Firm Enhances Trade
Surveillance
Trading services for millions of client accounts
> $16B in assets
> 4000 advisors
HDP accelerates time-to-insight
and extends data retention, for
superior protection from fraudETL slowness hampers intraday risk analysis
Investment firm processes 15M transactions and 300K
trades every day
Trading data not available for risk analysis until end of
day, which hampers intraday risk analytics and creates
a time window of unacceptable exposure to fraud
Operational data available to risk analysts on the
same day as trades
Trading risk group captures more position, execution
and balance data and retains that data for five years
Hadoop enables ingest of data from recent acquisitions
despite disparate data definitions and infrastructures
Storage limitations required archiving which limits
data availability
apps
Capital Markets

Problem Solution
Real-time Data Ingest & Transformation of Trade Information
Bank wanted to make it easier & cheaper to comply
with audits & exams
Data retrieval was taking so long executives had little
time to prepare response to audit queries
Create an ‘active archive’ that could be queried by
business analysts with SQL
eDiscovery platform decreases time to retrieve and
analyse trade data
Banking
One of the largest US banks
Data ﬂows into one data lake with
centralized security policies,
reducing storage costs
High costs of retrieval for trade
events
apps

LINE OF BUSINESS
apps

Problem Solution
Bank wanted to monetize data on macro-
economic trends
IT challenges made joining data and maintaining
anonymity prohibitively difficult
Multiple data sets extracted from source platforms with
single point of security & privacy for de-identification,
masking, encryption, authentication and
access control
All departments have access to the same cross-sell data
Seamless integration with Hortonworks partners SAS, R,
RedHat & Splunk
Data sets were fragmented in legacy silos
controlled by LoBs
Regulations and company policies protect
customer privacy
New Product Development Banking
Data flows into one data lake with
centralized security policies,
reducing storage costs
Big data from banking operations
was fragmented
apps

Problem Solution
Merchant offers need to be timely, and made to
nearby customers
Capture credit & debit card PoS and merge
with merchant data
Determine location of PoS and customers
Generate next-best-offer on behalf of merchant
Used closed-loop-analytics to reﬁne offer model
Retail Banking
Global bank
Enriched customer proﬁles with
geo and PoS data
Increase Revenue
Use merchant data to provide
location based services
apps
Payment Analytics

Problem Solution
Credit Decisions Retail Banking
Global bank
Augment credit decisions with
alternative scoringCredit default risk models use limited
sources of data
Inaccurate Credit Risk Models
Three credit agencies using nearly the same methods
Little room for variance in credit decisions
-Length of time spent reading terms & conditions
-Spelling, grammar, corrections
-Number of times application was saved before being
submitted
-Many more
Capture all details of customers interactions for use in
credit decisions for example:
Payment histories are incomplete for potentially proﬁtable
population segments
apps

Problem Solution
Products do not fit consumers at the edges of
pricing bands well, so
Combine all known data to deliver an
individualized product:
Consumers likely to move to bank that more accurately
prices their risk
-Bank liquidity picture
-Customer risk profile
-Macro economic forecasts
-Etc.
-Bank risk exposure
Risk Based Pricing Banking
Price products according to
individual risk profile
Inaccurate Product Pricing
Most products are created for broad
‘bands’ of consumers
apps

TECHNOLOGY
apps

Retail Banking

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