Read a report from Accenture Capital Markets on insightful analytics and leveraging data explosion for business optimization.

1. Insightful Analytics:
Leveraging the data explosion
for business optimisation
Top Ten Challenges for
Investment Banks 2015
09
InsightfulAnalytics:
Leveragingthedataexplosion
forbusinessoptimisation

2. 09
Insightful Analytics:
Leveraging the data explosion
for business optimisation
The wealth of information and data available to investment
banks continues to increase exponentially and, while the
scale and frequency of creation presents a challenge for
effective data management, it also provides an enormous
opportunity for insightful analytics.
Analytics can be used to help banks
respond to the main drivers currently
shaping the industry: regulatory reform,
margin pressure, and operational efficiency.
Value-adding analytical tools that make
greater use of existing big data solutions
represent a significant competitive
advantage for those banks that can deploy
them effectively.
The challenge is how to develop effective
analytical tools that enable the extraction
and visualisation of meaningful conclusions
to inform wider business strategy and
support day-to-day processing. This also
involves getting the right information in
front of the right consumers across the
organisation, on a timely basis.
The data explosion
The explosive growth in both structured
and unstructured data has captured the
attention of capital market firms and
vendors, given the variety of valuable
information that can be extracted and the
myriad of potential uses. Celent estimates
that big data spending in capital markets
will reach US$1.2 billion in 2013, growing
to $2.4 billion by 2015 as firms invest in an
effort to leverage the increasingly large
data sets for ever more complex solutions.i
To date many banks and vendors have
focused on existing, underleveraged (mostly
historical) data sets, and the first challenge
for many is how best to collate and
integrate the huge amounts of data now
available (see Fig.1):
2
i Big Data in Capital Markets: Expanding the
Search for Big Ideas, Celent, 2013

3. The right data preparation is therefore vital,
but it is only the first step. It is critical that
analysts have access to the right tools to
cut through the noise in large data sets and
identify actionable ideas quickly and often.
Demand is now moving beyond platforms
designed for routine, industrialised data
collection and number crunching, to true
data discovery platforms designed to find
and visualise the “unknown unknowns” –
the hidden insights contained in these
increasingly large data sets. Big data must
drive big outcomes (see Fig.2).
Smart data – cultivated data that can be
easily managed and manipulated by
business users – is one approach that
addresses the demand for greater insight.
The aim of smart data is simple: to provide
quick insight and leverage opportunities to
increase business generation proactively, by
providing guidance on clients’ needs based
on prior experience and market
conditions. By carrying its lineage and
context with it, smart data can also be
reused in multiple business contexts.
Indeed, it is vital that the analytical,
data-driven conclusions gleaned from
multiple data sets are made available to
all areas of the bank that can potentially
use and benefit from them. Certain data
sets are in particular demand for banks in
response to a specific regulatory pressure
or cost-reduction imperative, but the
potential applications for business
optimisation can range widely depending
on the type of data, user groups and the
particular business goal (see Fig.3).
Adding meaningful insight across
the business
Within the investment bank, therefore,
the potential applications of analytics to
support process optimisation and
strategic decision making are
wide-ranging and varied. While the use
of analytics is readily established in some
areas, the potential for future
developments is significant (see Fig.4).
3
Integrate
Consolidate data
from multiple sources
Discover
Identify records within
a large data store
Curate
Evaluate and improve
quality,trustworthyness
accuracy
Align
Map data schemes and
individual records to a
common model
Figure 2: From Big Data to Big Outcomes
Figure 1: Data Preparation PrinciplesTagging
Filtering
From
BigData
Testing
Casual Analysis
Patterns
Big Data
Discovery
Analytics
Automation
Factors
Insights
ToBigOu
tcomes
Execution
Decision
Simulation
MachineLe
arning
Source: Accenture Research
Source: Accenture Analytics

4. From a regulatory perspective, garnering
and scrutinising greater quantities of data
on individuals’ behaviour with smarter
algorithms will empower a much more
proactive and effective approach to
detecting misconduct in the front office.
Advancements in technologies such as
text mining, computational linguistics and
Complex-Event Processing (CEP) are also
enabling real-time management of
conduct risk on the trading floor, often
before a prospective “rogue” event.
Compliance departments can use these
tools to monitor firm activities and uncover
violations. Similarly, new ways of managing
and mining data are optimising the Know
Your Client (KYC) and anti-money
laundering (AML) controls for detection of
fraud and malpractice by clients.
The aggregation of market and position
data, collateral agreements and risk
calculations opens up the possibility for
real time what-if analysis to identify the
cheapest assets to deliver for collateral
management purposes. In addition, new
suites of advanced visualisation tools will
support better decision-making for
collateral allocation and inform the front
office with richer information on the cost
of collateralisation for optimal order and
execution management.
Advanced data visualisation tools are also
providing additional business intelligence
by drawing on data from multiple sources
and providing a single interface with new
levels of granularity in areas of the bank
that have previously been opaque. If banks
4
It is critical that analysts
have access to the right
tools to cut through the
noise in large data sets and
identify actionable ideas
quickly and often.
The aggregation of
market and position data,
collateral agreements and
risk calculations opens up
the possibility for real time
what-if analysis to identify
the cheapest assets to
deliver for collateral
management purposes.
want to predict how prices of
securities will evolve, how demand
and supply will change for securities
(intraday, weekly, monthly, quarterly
etc.) or how risk is correlated, they
must find the hidden or often
unknown correlations between
hundreds and thousands of external
data streams. Further examples
include post-trade analytics on the
Source: Accenture Capital Markets
OperationsFront Office
ManagementRegulatorsClients
Reporting
Sales
Efficiency
Strategy
Regulatory
Customer
Market
Transaction
Pricing
Risk
Figure 3: Data usage in the investment bank

5. 5
cost to serve for securities processing, and
deeper insights on the risk and PL trends
of individual trading books.
At the same time, market leaders are
making the most of the opportunity to
create richer interactions with their clients
by providing data and analytics for their
trading activity, and portfolio “health
checks” in the wealth management space.
The capital markets industry has vast
amounts of data and a vast appetite for it.
Although a real challenge, the integration
of data analytics into investment bank
operating models is the future state, and
has the potential to bring insight into
everyday business-decision making as
well as long-term strategic planning. If
not yet a market-wide standard, being
able to draw factual, data-driven
conclusions in order to support
analysis of business sentiment or
showcase operating trends is
becoming a prerequisite for the
leaders in the industry.
Although a real challenge,
the integration of data
analytics into investment
bank operating models is
the future state
Figure 4: From Big Data to Big Outcomes
Analytics Application Example Use Cases Users
Trade Surveillance
Financial Crime
Discovery and detection of
aberrant behaviours for targeted
investigation to mitigate conduct
risk and potential financial
penalties
• Rogue trading
• Unauthorised trading
• Benchmark rigging (i.e. LIBOR/FX rate manipulation)
• Commodities price fixing (e.g gold prices)
• Fraud in dark pools
• Background checks/analysis for client onboarding/KYC
Compliance, Onboarding
Funding Capital
Management
Real-time/On-demand capital
allocation and optimisation and
liquidity management
• Decision Suport for the middle Office
• Complex event processing
• Client Facing Technology for the Buy-Side
• Pre-Trade Analytics for the Front Office
• Real-time Stress Testing
Middle Office, Treasury/ALM
Business-Led
Intelligence
Next-generation data
warehousing but with a
business-led approach
Institutional Customer:
• Pricing for advisors
• Trade Profitability
• Client lifetime value / 360° View
• Cross-sell / Up-sell opportunities
• Client Retention / Acquisition
Sales Trading, Middle
Office, Operations, Risk
Institutional Customer: Management Information:
• Post-Trade Securities Processing
• What-if Analysis of Portfolio Risk
• PL Trending of Traders Books
Source: Accenture Capital Markets

6. About Accenture
Accenture is a global management
consulting, technology services and
outsourcing company, with more
than 305,000 people serving clients in
more than 120 countries. Combining
unparalleled experience, comprehensive
capabilities across all industries and
business functions, and extensive
research on the world’s most successful
companies, Accenture collaborates
with clients to help them become
high-performance businesses and
governments. The company generated
net revenues of US$30.0 billion for the
fiscal year ended Aug. 31, 2014. Its home
page is www.accenture.com.
Accenture Experts
To discuss any of the ideas presented in
this paper please contact:
Matt Long
Managing Director,
Capital Markets, London
matthew.j.long@accenture.com
+44 20 7844 3136
Edwin Van der Ouderaa
Managing Director and Digital Lead
Financial Services Europe,
Africa Latin America
edwin.vanderouderaa@accenture.com
+32 22 267 312
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