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© Jerome Kehrli @ niceideas.ch
Big Data in Private Banking
Opportunities and how to get them ?

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1. What is Big Data ?
2. Opportunities in Wealth Management ?
1. Real time performance and risk metrics
2. Portfolio optimization and simulation
3. Leveraging customer data
3. How to get there ?
Agenda

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1. What is Big Data ?

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An evolution of society
(Souce:http://www.businessinsider.fr/us/vatican-square-
2005-and-2013-2013-3)

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The era of power

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Today
In 2018, over 4 billion
people are connected and
share data on a continues
time and everywhere
Mobile
First !!!

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Tomorrow
« The Internet of Things is the next Big Thing » The Economist

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Data deluge !
Data deluge
5 exabytes of data
(5 billions of gigabytes)
Were generated
since the first
measurements
until 2003,
In 2011, this
quantity was
generated in
2 days
In 2018, this
quantity was
generated in
2 minutes
Source:https://www.emc.com/collateral/analyst-reports/idc-the-digital-universe-in-2020.pdf

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All the time !All the time !

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Everywhere !

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1 minute on Internet

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Technical capability evolution
For the 40 years, the IT component capabilties grew exponentially
CPU, RAM, Disk … follow the Moore law!
Source :
http://radar.oreilly.com/2011/08/building-data-startups.html

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Storage cost evolution
While the unit cost decreasing…
Unit capability increasing and cost decreasing, the vertical scalability is always the easiest solution,
really?
0.01 $
0.10 $
1.00 $
10.00 $
100.00 $
1,000.00 $
10,000.00 $
100,000.00 $
1,000,000.00 $
10,000,000.00 $
1975 1980 1985 1990 1995 2000 2005 2010 2015
Hard Drive
RAM
Source :http://www.mkomo.com/cost-per-gigabyte
5 $
/GB
5 M$
/GB

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Disk throughput evolution
Issue : The throughput evolution is always lower than the capacity evolution
How read/write more and more data through an always thicker pipe?
Gain : x100 000
Capacity:
Gain : x 10’000
In 10 years
Throughput
Gain : x 50
In 10 years

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Origins of Big Data : the web giants !

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How to process data that cannot fit in memory anymore ?
The Web Giants have been the first to face the limits of traditional architectures
New architectures and paradigms
The massive computations are the reason why we need new architetcures and paradigms
such as Hadoop or No / New SQL
Idea #1 : Run transaction and computation in parallel
Idea #2 : Scale at the multi-datacenter level the grid of CPU, DRAM and Disk
Idea #3 : Move the code to the computing node, not the data (tier layer revolution)
RAM
How to read thousands of files of billions of rows
in a limited time?I / O
How to process trillions of operations in a limited time?CPU

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So what is big data?
Defining big Data is actually beyond the formal definition.
It’s all together a technology evolution anticipated by the Big Consulting companies and a business
opportunity !
Big
Data
More and
different
data
Evolution of
Datascience
More
computing
capacities
New
technologies
and
architectures

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Big data represents the information
assets characterized by such a high
volume, velocity and variety to require
specific technology and analytical
methods for its transformation into value
Big Data : definition
https://en.wikipedia.org/wiki/Big_data#Definition

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Existing Architectures Limits
IO Limit
Applications are heavily storage oriented
CPU Limit
Applications are heavily computation oriented
TPS Limit (Transaction Per Second)
Applications are heavily high-troughput oriented
Transactional Applications / Operational Information Systems
EPS Limit (Event Per Second)
Applications are heavily ultra low-latency oriented
Event Flow applications

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Existing Architectures Limits
IO Limit
Applications are heavily storage oriented
CPU Limit
Applications are heavily computation oriented
TPS Limit (Transaction Per Second)
Applications are heavily high-troughput oriented
Transactional Applications / Operational Information Systems
EPS Limit (Event Per Second)
Applications are heavily ultra low-latency oriented
Event Flow applications
Over 10 Tb, « classical »
architectures requires
huge software and
hardware adaptations.
Over 1 000 transactions /
second, « classical »
architectures requires
huge software and
hardware adaptations.
Over 1 000 events /
second, « classical »
architectures requires
huge software and
hardware adaptations.
Over 10 threads/Core
CPU, sequential
programming reach its
limits (IO).

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Traditional and Specialized Architectures
IO Limit
Applications are heavily storage oriented
CPU Limit
Applications are heavily computation oriented
TPS Limit (Transaction Per Second)
Applications are heavily high-troughput oriented
Transactional Applications / Operational Information Systems
EPS Limit (Event Per Second)
Applications are heavily ultra low-latency oriented
Event Flow applications
Storage Grids
Distributed Storage /
Share nothing grids
Transaction Grids
XTP
Stream Grids
Event Stream
Processing
Computation Grids
Parallel Processing
Traditional Architectures
RDBMS, Application
servers, ETLs, ESBs, etc.

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Traditional Architectures
RDBMS, Application
servers, ETLs, ESBs, etc.
New Architectures
IO Limit
Applications are heavily storage oriented
CPU Limit
Applications are heavily computation oriented
TPS Limit (Transaction Per Second)
Applications are heavily high-troughput oriented
Transactional Applications / Operational Information Systems
EPS Limit (Event Per Second)
Applications are heavily ultra low-latency oriented
Event Flow applications
Hadoop
Usual hadoop
ecosystem
NoSQL /
NewSQL
In-memory
Analytics
Other
Big
Data
Processing
Engines
Streaming
Solution
CPU
Grids GPU
Grids

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Some examples
IO Limit
Applications are heavily storage oriented
CPU Limit
Applications are heavily computation oriented
TPS Limit (Transaction Per Second)
Applications are heavily high-troughput oriented
Transactional Applications / Operational Information Systems
EPS Limit (Event Per Second)
Applications are heavily ultra low-latency oriented
Event Flow applications
RabbitMQ,
Zero MQ
Apache Kafka
Quartet
ActivePivot
Sqoop
Exalytics
HDFS
Exadata
EMC
Teradata
SQLFire
Giga Spaces
HBase
Cassandra,
MongoDB,
CouchDB
Voldemort
Hana
Redis
MapR
Esper
Hama Igraph
Spark /
Spark Streaming
Hive
Pig

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2. Opportunities in Wealth Management ?

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Big Data in Wealth Management
Investment Research
Data discovery / market research
Development of Investment ideas
Testing of investment strategies
Portfolio Management
Trading
Risk Management
Aggregation of position Data
Position monitoring
Risk dashboards
Portfolio Management
Customer knowledge
Unified / Consolidated customer view
Customer profiling and analysis
Know Your Customer / External Data
Analysis of unstructured data
Client knowledge
Investment advisory
Compliance and Monitoring
Pre / Post-Trade
Fraud detection and prevention
Anti Money Laundering
Communication channels monitoring
State of
the Art
use cases
1
2
3

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Portfolio optimization
and simulation
Focus on some use cases
Real-time performance
and risk metrics
Intra-day positions and trades
Risk dashboards
Solvency ratios for credit approval
Fraud prevention / Anti-Money
laundering
What if we had taken this or that
investment decision ?
What about this investment strategy ?
Large scale portfolio optimization
Leveraging Customer data
Customer profiling / classification
Personalized investment advices
Better / deeper fraud detection
Marketing campaigns

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2.1 Real time performance and risk
metrics

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Portfolio metrics
Portfolio metrics
Performance
Contributions / Exposure
Risk metrics
Variance
Sharp Ratio
Volatility
Value at Risk
breakdown by
Region / Country / Currency / …
Sector / Industry / …
Global metrics
Same as portfolio …
breakdown by Office, LoB, Contract, Customer, etc.
also breakdown by Country, Currency, Sector, Industry, etc.
Portfolio metrics in private banking institutions
Val / perf : night computation batches for
Portfolio risk metrics : WE batches
Global metrics : quarterly batches

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Make it real-time …
The disastrous global financial crisis put a spotlight on the need to get rapid feedback after
market events.
Banks are trying to obtain an array of risk metrics in more real time, released multiple times
during the day
Reduce time to result as much as possible
Take intraday positions and trades into account
Get immediate feedback on intraday market events
Using latest quotes and other metrics

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The good ol’way
1. Night / WE / Quarterly computation batches
Missing intraday positions / live quotes
Far from real time
2. Intraday calculation within the Operational IS
Everything in the RDBMS
Load / reload / compute / re-compute again
Heavy load on Operational IS / slow computation
Very long response time (several minutes) / crash …
3. Efficient off-Operational IS computation (rare …)
Distributed cache (Jboss Cache)
Reload time / Huge operating costs
Computing Grids (Terradata, …)
Huge licensing cost

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Commodity hardware
Reduce TCO
Scale Out
Open-source software stack
No licensing Cost
Ease of operation (Web giants initiated)
From Pull to Push
Computing Portfolio or global Perf or VaR in real-time is difficult
Processing market events and updating metrics in real-time is straightforward
We have the technology
The Big Data Way

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2.2 Portfolio optimization and
simulation

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Portfolio optimization and simulation
Portfolio Optimization
Markovitz Mean-Variance (MPT)
Mean-CVaR
Custom investment constraints
Etc.
Portfolio simulation
What if we had taken this or that
investment decision ?
What about this investment strategy ?
What if we try such policy or
regulation approach ?
Most PMS software support simulation and some optimization models out of the box.
What about yours ? Do you use these features ?
What about large scale portfolio optimization ?

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Portfolio optim. – bactkesting and stress testing
Portfolio backtesting / stresstesting
o Backtest over past periods
o This is Markowitz
o Test optimization parameters
o Stress-test over market events
o A lot of computations …
Stress-testing and back-testing are a little less common …
What do you have ?
What about large scale backtesting ?

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Even further : compute portfolio efficient frontier
Efficient Frontier calculation
o Sharp ratio
o Heuristic computation
+ Lots of computations
o With respect to constraints
o Dynamic constraints
o Compute result weights
Very rare in most financial companies
What do you have ?
What about large scale sharp ratio optimization ?

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The good ol’way
1. Excel – Quantitative analysts
Use extract from Operational IS … loaded In Excel
Simulations / rebalancings are run from Excel
Slow / inadapted / buggy
2. Calc. Program - Quantitative analysts
Use extract from operational IS … loaded In Matlab
Simulations / rebalancings are run from Matlab
Steady learning curve
Poor User Interface
3. Proprietary/ specialized software (Reuters, BB, …)
To be integrated on Operational IS
Tricky / Risky
Expensive…
Inflexible

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The Big Data Way
> Findings …
+ Quantitative research tooling is sub-optimal in most private banking institutions
+ Some needs are more or less covered
+ Yet we are far from a large scale and systemic approach to portfolio optimization
and back-testing
Increase computing capacities with eXtreme parallelization / distribution
Large scale distributed systems
Move the computing code to the data
Reduce TCO
Open source software stacks
Hadoop, Cassandra, Infinispan, Python SciKit, R, …
Commodity Hardware

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2.3 Leveraging customer data

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Leveraging Customer data
• Customer personal data and their changes
• Investment profiles
Customer profiles
• All customer and prospects contacts
• Documents / Phone calls / emails / …
CRM activity
• Financial transactions / account activity
• Orders and confirmations
Transactions and activity
• Bank online systems / ebanking
• Sometimes even firewall traces
Online activity
• What if we add external data ?
• Twitter / facebook activity
External data
Private banking institutions are keeping tracks of a lot of data
This huge amount of data is kept but rarely used
Can we make something of it ?

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Opportunities can be examined from two perspectives:
Customer Data : new opportunities ?
Big Data as a
« Cost killer »
or an enhancer
Big Data as a way to
« widen the field of
possibilities »

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What can we do with it anyway ?
Fraud detection and AML
> Real-time transaction monitoring
+ Identify patterns and outliers
> Communication channels monitoring
+ Mining of emails, calls, logs, …
> Web-site / Firewall monitoring
Customer analysis
> Customer segmentation
+ Growth potential / risk metrics / …
> Customer profiling
+ Investment profiles
+ Peer groups.
> Prospection analysis
> Marketing campaigns
> Invest on the most promising
customers
> Personalized investment advices
> Benchmark customers
> Match firewall / website breach
attempts to account activity
> Better / deeper fraud detection
> Anty-Money Laundering

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What can we do with it anyway ? (cont’d)
Online banking software
> Advanced searched on transactions
+ Multi-criterias : date, name of shop,
revenues, full-text search
> Annotate transactions, shops,
compare, get advises, etc.
 Long term logs of all transactions
Know Your Customer
> Customer 360 view …
+ Everything with contacts, transactions,
performance, risk metrics, …
> Customer identification program
+ Matching with Web and social
networks
> User Experience Revolution
> Marketing / Digital Experience
> User-engineering
> Fast access to all customer data
> Unified view of the customer
> Real-time construction / display

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The Big Data Way
Manipulate and consolidate very large volumes of data efficiently
HDFS and Other No/NewSQL storages
Highly distributed computing
Reduce TCO
Open source software stacks
Hadoop, Cassandra, Infinispan, Python SciKit-Learn, R, …
Commodity Hardware
Manipulating, consolidating and mining all the data related to customer and/or
users activities resulting from heterogeneous sources is difficult
Some initiatives already exist in most institutions.
 They are however most of the time limited to small and specific sets of data
 Or implemented on expensive technologies such as Terradata

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3. How to get there ?

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Improve Analytics System
Reduce TCO / Cost Killer !
Extend the field of possibilities
Consider use cases so far reserved to investment banking institutions
Large scale portfolio optimization, simulation, rebalancing, back-testing, etc
Real-time metrics
Widen the field of possibilities
User Experience Revolution
Marketing / Digital Experience
User-engineering
Customer 360 view
KYC
Deeper depth of analysis
Take Away

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Architecture
Data acquisition Collection and Analysis
InfiniSpan
(DataGrid)
Live Market
Data
• Instruments
• Quotes
• Index values
Storm
(Real-time)
Portfolios
• Simulation
• Optimization
• Rebalancing
• Backtesting
• Stresstesting
Historical
Market
Data
• Instruments
• Quotes
• Index values
Cassandra
(DB)
RestitutionExternal Data
• Web API
• Web searches
• Social networks
(PULL mode)
Operational Data
• Transactions
• Acccount Activity
• Mails / Calls
• Logs
• PMS :
Portfolios/
Positions
• Trades /
• Accounts / Structure
• Reference Data
Real-time and
batch metrics
• Portfolio perf.
• Risk metrics
• Real-time
Dashboards
Hadoop
/ HDFS
/ Hbase
(Data Storage
And
Analysis)
Tulip /
Hive /
Pig
(Querying / Data Viz /
Results Reporting)
Analysis
• Customer
knowledge
• Fraud
detection
Live Mkt. Data
Market Data
Accounting
Reference
PMS Data
(Unstructured
Data)
Account Act.
Transactions
Big Data Deployment
In Private Banking Institutions
Expert System
(Portfolio simulation,
optimization, etc.)

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1. Identify your potential use cases
2. Prioritize use cases with business experts / representatives
3. Identify technological leads / opportunities
4. Implement Proof Of Concepts
Incremental / Iterative way :
How to get there ?
• One time
extract
• Scheduled
extractors
Import Data
within target
Technology
• Pig / Hive
• DataViz
• Storm,
Infinispan
Discover Data
and technology • New analysis
• Reporting
• Automation
Automate /
Analyze Data

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Thanks for listening