How Adavanced Analytics will transform Banking in Luxembourg

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Tommy LEHNERT
How Advanced Analytics will transform
Banking in Luxembourg

Dedication
This work is dedicated to all the women and men working in the Luxembourgish
banking and finance sector for their constant commitment of rendering the Luxembourgish
market interesting for investors and competitive amongst the other important financial
centres throughout the world.

Acknowledgements
I would like to pass on my thanks to each and every person that throughout the last
two years supported me and for all the interesting conversations we had.
Particularly and most of all, I thank my family, my friends and my partner in life who
put up with me neglecting them as I spent time on studying and working.

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Table of Contents
Introduction ....................................................................................................................................................... 6
Part 1 - Industry challenges ............................................................................................................................... 8
CHAPTER 1 – BANKING LANDSCAPE ............................................................................................................ 9
RETAIL BANKING ..................................................................................................................................... 9
RETAIL BANKING IN LUXEMBOURG .......................................................................................................... 9
PRIORITIES FOR REVENUE GROWTH ........................................................................................................ 10
BUSINESS DRIVERS AND STRATEGIC RESPONSES ..................................................................................... 11
PRIVATE BANKING ................................................................................................................................. 12
PRIVATE BANKING IN LUXEMBOURG ...................................................................................................... 12
PRIORITIES FOR REVENUE GROWTH ........................................................................................................ 14
BUSINESS DRIVERS AND STRATEGIC RESPONSES ..................................................................................... 14
CHAPTER 2 – STRUCTURAL IMPACT ......................................................................................................... 16
THE DATA MANAGEMENT CHALLENGE .................................................................................................. 16
THE DATA MANAGEMENT CONCEPT ....................................................................................................... 17
DATA INTEGRATION ............................................................................................................................... 17
DATA QUALITY ...................................................................................................................................... 17
DATA MANAGEMENT AND MASTER DATA MANAGEMENT ..................................................................... 18
ENTERPRISE DATA ACCESS .................................................................................................................... 18
INFORMATION MANAGEMENT ................................................................................................................ 18
GOVERNANCE AND ROLES ...................................................................................................................... 19
CHAPTER 3 – A JOURNEY INTO A DIGITAL, OMNI-CHANNEL CUSTOMER EXPERIENCE ........................... 21
DIGITALIZATION ..................................................................................................................................... 21
CUSTOMER CENTRICITY ......................................................................................................................... 22
THE FIVE C’S OF MARKETING AND CUSTOMER INTELLIGENCE ............................................................... 23
CUSTOMER INTELLIGENCE IN BANKING ................................................................................................. 24
BANK 3.0 ................................................................................................................................................ 25
CLIENT EXPECTATIONS ........................................................................................................................... 25
EXPOSURE TO FRAUDSTERS .................................................................................................................... 26
SUCCESFUL FRAUD DETECTION ............................................................................................................... 26

Part 2 - Advanced Analytics in Banking ......................................................................................................... 28
CHAPTER 4 – ADVANCED ANALYTICS ....................................................................................................... 29
DEFINING ADVANCED ANALYTICS ......................................................................................................... 29
MULTIPLE SETS OF POSSIBILITIES ........................................................................................................... 32
BUILDING A CENTRE OF ANALYTICAL COMPETENCIES ........................................................................... 34
ANALYTICS CULTURE ............................................................................................................................. 34
ADVANCED ANALYTICS AT WORK .......................................................................................................... 35
PROACTIVE CLIENT ENGAGEMENT .......................................................................................................... 35
CHAPTER 5 – ANALYTICS IN BANKING REDEFINED .................................................................................. 37
THE DECISION HUB ................................................................................................................................ 37
WHERE THE DECISION HUB COMES INTO PLAY ....................................................................................... 37
WHY WILL THE DECISION HUB HELP BANKS IN THEIR TRANSFORMATION?............................................. 38
EXAMPLE OF SUCCESSFUL TRANSFORMATION ........................................................................................ 39
HIGH-PERFORMANCE ANALYTICS .......................................................................................................... 40
IT’S ALL ABOUT SPEED ............................................................................................................................ 40
A VISUAL REVOLUTION? ........................................................................................................................ 41
Conclusion ....................................................................................................................................................... 44
Bibliography .................................................................................................................................................... 45
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Where is the wisdom we have lost in knowledge?
Where is the knowledge we have lost in information?
T.S. Eliot
Introduction
Over the last 35 years, Banks have always been a forerunner in investing and relying
on performant IT systems and virtually they have transformed every single process in the
bank. Applying IT to different business processes from a cost-efficiency standpoint, from a
revenue-generation standpoint and from a profit-driven standpoint, has been an essential
accelerator for banks especially when it comes to transforming or reinventing their business.
During the 1990’s and in the beginning of the 21st century, early adopters of ATMs
and online banking created a competitive advantage for a few years, just to mention two
examples out of many. Historically seen, banks have not only been managers of money but
also, and in much larger volumes, they have been managers and gatekeepers of data and
information.
The sheer amount of data and information that has been stored and processed over
the time by the banks, represented and represents today and will represent even more in the
future, a vital source in risk management and marketing. These disciplines have historically
used data and information pretty well for their needs in terms of credit risk assessment and
lead-mining models for marketing campaigns.
Although most of the data is not used to be transformed into valuable information
and processed in order to get insights, if not knowledge, out of that information. Most of the
data is simply stored and is a bearer of cost in capital even if today storage of data is
becoming increasingly cheaper. The bottleneck of this cost reduction is the fact that the data
volume is increasing exponentially and thus this reduction in costs for storage has no
significant impact on the balance sheet as the saving is used to add storage space.

In the after-crisis era, banks have made significant efforts to stabilize their balance
sheets by the substantial increase in capital base and despite many other efforts, performance
has deteriorated. Return on equity fell well below previous average earnings and the investor
confidence remains low due to reduced expectations of a quick recovery and doubts over the
sustainability of business models. The burden of tight regulation becomes increasingly heavy
and complex especially during times of low interest rates while the macroeconomic volatility
adds to gloom. New technologies challenge the traditional business model and are
accelerating the possibilities for the new generation of customers to change behaviour and
in consequence the ease of changing bank. Amongst all these challenges, banks face fierce
competition between each other but also from new players, delivering banking services
without having so strict regulatory and capital requirements.
As banking and financial services represent a mayor stake in the Luxembourgish
economy it is even more crucial that these local and global institutions here in Luxembourg
keep up the pace in remaining centres of excellence in banking and financial services. The
regulatory, political and economic environment, such as the markets place expertise are
positive aspects to consider as an advantage and asset of the Luxembourgish financial sector.
Nevertheless, will this be enough to preserve a competitive edge in todays’ rapidly
changing world and the previously described challenges? Fortunately, Luxembourg is
building up a strong ICT sector and the link between banking and technology can be
tightened in order to open up new opportunities for them and accelerate their economic
transformation.
Can the banks keep up with technological revolution and gain a competitive
advantage? How can banks leverage their data in order to transform it into meaningful
insights and how can banks use advanced analytics in reinventing their, slowly but for sure,
becoming obsolete business model?
In the following chapters you will get a closer look at the Luxembourgish banking
landscape and how todays banking can be tighten up in the digital world and advanced
analytics. You will find ideas of a new banking model and especially how advanced analytics
can be key to address the banks challenges.
In the future it will be very interesting to see who will be the innovators gaining a
competitive advantage by using extensively Advanced Analytics.
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Part 1
-
Industry challenges
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Chapter 1 – Banking landscape
Retail Banking
Current and near-term market conditions offer little hope that retail banks will be
buoyed back to profitability by external factors. Thus banks must pursue change from the
inside, aggressively reworking the business model to boost their performance within the
current banking environment.
The rise of digital banking and the proliferation of access channels also result in an
increase in the frequency with which customers perform simple bank transactions. Digital
channels don’t just displace, but also supplement, in-person banking interactions.
Unfortunately, frequent interaction does not necessarily deepen engagement. Banks must
determine how to translate the growth in customer touch points into true relationship growth.
Bank strategies should shift from focusing on digital adoption to achieving digital
engagement to ensure that digital channels, now the primary determinants of customer
experience, drive loyalty and sales as effectively as the branch.
There are numerous examples of compliance impacting strategy at both the national
and global level. Globally, financial institutions are facing multiple year implementations
for Basel III. Increased regulatory capital charges for riskier loan products and operations
are causing European institutions to sell certain lines of business and loan assets. Taken
together, regulatory changes, uncertainty, and long implementation timelines will keep
compliance near the top of every financial institution’s business strategy and technology
investment priority list.
Retail Banking in Luxembourg
Since 3 years the assets in Luxembourg banks are decreasing. Fixed income
portfolios have been reduced but placements at the European Central Bank increased. In
times where the ECB tries to incentive banks, and especially retail banks, to provide more
substance to the economic stimulus there are some alarming figures which show exactly a
contrary evolution.
Loans and advances between banks increased by 14 billion whereas the deposits from
banks decreased by 22 billion versus a decrease of 5 billion in customer loans and advances

whereas the deposits from customers increased by 16 billion1. So banks lend between each
other but are reluctant from increasing the allocation of loans to private or corporate
customers. Several reasons contribute to this factor, as on one hand the ECB strengthens
capital requirements, regulation and increases risk management but on the other hand they
expect banks to release more capital into the economic environment.
Eligible own funds rose by 5% to € 47.4 billion. This was supported by a 5% decrease
in risk weighted assets having a significant impact on the aggregate capital ratio, which
increased from 17.7 to 19.7. The solvency ratio for the industry, however, remained more
than twice the required minimum of 8%.
Luxembourg’s few local retail banks still rely heavily on their cost intensive branch
business. It is very likely that this business model will no longer be sustainable in the future.
Therefore some good initiatives have been undertaken in terms of digital and mobile
banking. Another pain point is the fact of not having the critical mass of customers for
turning to a full digital transformation. For future growth, banks need to drive their business
model transformation.
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Priorities for revenue growth
If banks want to drive revenue growth, two top priorities should be considered:
differentiating client experience and having the right focus on product mix.
A differentiated and improved client experience can be achieved by optimizing the
bank’s branch structure and by unifying mobile and branch channels. Enhanced client
segmentation, improved data infrastructure and analytics will bolster the banks cross and up
selling as a result of the before mentioned efforts. Essentially will also be the right product
mix by focusing on fee-based products revised pricing strategies. It is likely that in the future
some components and features of mobile banking will become fee-liable and that clients
might get charged on how extensively they use the banks digital infrastructures for
mentioning only two possibilities.
1 Figure based on the CSSF annual report 2013.

Business drivers and strategic responses
The branch business model is under threat from persistent economic challenges and
dramatic changes in customer behaviour are causing digital channels to rapidly displace
personal bank interactions. External innovation and competition is disrupting the industry
and threatening banks with disintermediation. Furthermore, the information security risks
are complicated by the rise of mobility and by recent media attention and compliance
requirements are growing as regulatory regimes accelerate rule-making.
To address these business drivers with strategic responses, retail banks will have to
reduce costs in personal channels and increase revenue in digital channels. Client experience
needs to be repositioned as a fundamental driver of business transformation. Banks do need
to proactively manage new and emerging risks and compliance requirements and from a
technological perspective, banks need to reach increased technology scalability through
sourcing and flexible computing capabilities.
Persistent profitability challenges, changes in the way customers “do business” with
their banks, and disruptive innovation and competition will force banks to take drastic steps
to reduce costs and identify new sources of revenue across channels. They will need to
restructure branch technology in order to enhance advisory and sales interactions.
The focus on customer experience will drive investments in Omni-channel and
digital marketing which will improve customer satisfaction, increase share of wallet through
cross-sell and up-sell, and in addition will reduce cost to serve compared to in-person
channels like the branch. A developed tailored digital marketing will boost sales in digital
channels. This improved digital service and support will help to deepen the client
engagement and the integrated client communication across all channels help to create a
consistent client experience.
The technology infrastructure in banks will also change, driven by the need to reduce
non-interest expense for which the main drivers are technology and personnel. Technology
will become much more cloud-enabled (internal and external) so that demand, supply and
cost can flex with the changing needs of bank businesses.
Data management processes as well as business processes will have an increased
focus to increase speed and decrease errors in operational processes as well as increase
security to protect both bank and customer information.
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Data will continue as a focus area hand in hand with analytics to create insights from
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both internal and external data.
Risk and compliance will continue to drive expenditures because they are “must do”
projects for regulators. Risk data aggregation continues to be a challenge for banks in order
to calculate regulatory capital for Basel III and perform stress testing (CCAR, DFAST, etc.)
which will continue to increase in frequency. Automated compliance processes could reduce
the costs and risks associated with regulatory reform and the improved data process
management can bolster ongoing security and compliance efforts.
Private Banking
The introduction of new regulations and non-traditional competitors will force wealth
management firms to anticipate changes to their business models and create flexibility today
in preparation for the future.
The financial services industry spent much of 2013 watching governments resolve
pending political disputes and move slowly through their wealth management regulatory
agenda. This gridlock, likely to extend into 2014, affects wealth management because of its
impact on the economy and investor sentiment. Furthermore, delays in regulatory clarity
keep firms from making long-term decisions with confidence.
Clarity on wealth management regulation takes time, making it difficult for wealth
firms to budget appropriately for compliance-related costs. In a recent CEB Tower Group
Agenda Poll, 94% of wealth firm executives surveyed said that preparing systems for
upcoming regulatory deadlines was of high or critical importance for the coming year, and
only 41% had high or complete confidence in their ability to execute on their goals.
Private Banking in Luxembourg
Private banking has incredibly changed during the last five years. Private bankers
were the envy of many other bank employees. Their day-to-day work mostly consisted of
relationship management with limited time spent on technical matters. The collapse of
Lehman Brothers completely changed this paradigm.
Private bankers of today work in a more challenging climate, made up of a difficult
economic environment, high market volatility, cost pressure, lower profit margins and
regulatory changes. The situation would be acceptable, were it not be for private bankers
having to face investors’ scepticism. Where in the past clients were listening to every word

their adviser was telling them, today they raise questions and are very well informed.
Restoring investor confidence has become critical for the industry. Last but not least, one of
the main reasons that has led to many foreign residents opening an account in Luxembourg
in previous years has probably disappeared. The industry’s commitment is now clear: private
bankers are no longer willing to open accounts for clients who are not transparent with their
country of residence’s tax administration. We are shifting from an “off-shore” to an “on-shore”
model. Faced with such a predicament, it has become harder to compete with the
client’s “home-country bank”. You need to demonstrate very solid arguments for asking
your client to visit you abroad. Private bankers now really need to proactively hunt for new
prospects while remembering that the “farming mode” was the motto in previous years. On
the one hand, private bankers in Geneva or in Zurich are facing the same challenges as their
Luxembourg colleagues. On the other hand, there are differences between the two countries.
When analysing the importance of the industry in the respective countries, it becomes
clear that the global Assets under Management (“AUM”) in Switzerland are probably 8 to
10 times bigger than AUM in Luxembourg. Size matters. It gives rise to economies of scale,
allowing private banks to invest strategically in all operational, IT and regulatory projects.
This investment is likely to lead to increased profitability. It is therefore highly likely that
smaller banks will undergo a consolidation process, similar to what we saw in Luxembourg
during 2012. Some of the players could also decide to drop their banking license and pursue
their business under an Asset Management regulated status (a so-called financial sector
professional or “PSF”), using a third-party bank as their depositary bank.
All Luxembourg private bankers will seriously have to monitor their costs and
consider whether it is necessary to outsource some IT or operational parts of the business to
a third party, a so-called “Support PSF”. The second major difference between Luxembourg
and Swiss private banks is the origin of the clients: Luxembourg attracts more continental
clients whereas Swiss banks’ clients are truly international. In both cases, bankers who want
to grow their AUM will have to tailor their business development in order to target a very
specific client segment in a limited number of key target countries. Furthermore, the CEO’s
of private banks are fully aware of the complexity of developing business relationships in
other countries whilst still respecting the legal, tax and social environment of these countries.
Luxembourg has developed a unique expertise in investment funds and has over the
last 25 years become the second largest centre in the world in terms of AUM (after the U.S.)
for domiciling investment funds. Luxembourg is by far the number one domicile (85% of
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the funds world-wide) used by the most important asset managers in the world (including
the Swiss asset managers) for cross-border fund distribution. All the technical expertise
related to asset structuring and asset servicing that has been developed for large institutional
clients can be re-directed to private banking. In a tax transparent world, the need to structure
the global wealth of High Net Worth Individuals and in particular Ultra High Net Worth
Individuals is becoming crucial. Luxembourg’s private bankers can bring in the right
financial engineering expertise to structure assets of such clients. It is a matter of fact that
there will be more challenges and complex situations in the future for the private banking
industry.
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Priorities for revenue growth
The priorities for revenue growth of Private Banks do not defer that much from the
previously described priorities for Retail Banks. As the clients’ attitude towards financial
advice changes and as consumer technology adaption outpaces many banks capabilities,
Private Banks should consider the information and technology enablement that they could
offer their clients. In private banking it has always been very hard to standardize and
industrialize business processes especially within their client interaction. Today and in the
future this will become much easier to achieve with the given changes described earlier.
What if a Private Bank could offer, fee-liable, first class financial information and online
advisory service to their clients? What if a private banking client could also profit from the
excellence in services within digital channels and interactions with their bank? Why not
improving client experience by rethinking cost-intensive approaches?
Analytics will for sure play a very important role within the future Private Banks
when it comes to analyse client behaviour, risk aggregation, fraud detection and enhancing
the overall client experience.
Business drivers and strategic responses
As gadget-embracing clients and advisors become increasingly important users of
wealth management technology, firms will have to update their offerings to meet the needs
of these new constituents.
Historically, full digital client engagement is the preference of “do it yourself”
investors and active traders, with most clients creating financial plans and making portfolio
decisions with a personal advisor. The availability of sophisticated online advice and

professional advisors as a back-up challenges the current and future state of delivering
wealth management products and services.
In the past five years, wealthy customers went from having access to the Internet only
on computers to having constant access on multiple devices and platforms, ranging from
smartphones to tablets and e-readers. This proliferation of devices, many of which are run
on disparate and rapidly changing operating systems, has made it difficult for wealth
management firms to provide cutting-edge tools to meet the needs of their increasingly
savvy, device-wielding clientele.
According to a 2013 CEB Tower Group survey, more than half of high-net-worth
clients own both a smartphone and a tablet, and only 14% had neither device. However, that
same client experience survey indicates that clients do not see a reason to increase their level
of online and mobile engagement. Currently, 67% of wealthy clients do not use a mobile
application from any financial services provider, indicating that the problem is not limited
to wealth management. When asked why they do not use mobile apps, 65% of high-net-worth
clients said they saw no reason to, showing that wealth firms need to promote the
benefits of their mobile capabilities to their clients.
Identified business drivers for Private Banks are resumed in political gridlock and
uncertainty where attitudes towards financial advice from an aging workforce are changing.
Fierce competition is to expect from non-traditional wealth management firms and consumer
technology adoption outpaces industry capabilities. Strategic responses to these drivers are
defined hereafter: building a high impact team sales and advisory model, increasing the scale
of the service model through multichannel tools, proving the value of advice to HNWI and
unlocking the potential of client data.
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Chapter 2 – Structural Impact
In order to respond to the question of what would be the structural impact by
embracing the proposed banking model, we need to highlight first the biggest challenges and
some of the most crucial components of modern banking structures and why innovative
information management is required.
The Data Management Challenge
Below are only a few of the statements that each organisation could recognize as they
are very common challenges within the data management area.
To understand the challenges companies face in managing data, one must understand
the dimensions of data.
Volume - Many factors contribute to the increase in data volume – transaction-based
data stored through the years, text data constantly streaming in from social media, increasing
amounts of sensor data being collected, etc. In the past, excessive data volume created a
storage issue. But with today's decreasing storage costs, other issues emerge.
The next dimension is Velocity - According to analysts, velocity refers to how fast
data is being produced and how fast the data must be processed to meet demand. Reacting
quickly enough to deal with velocity is a challenge to most organizations.

Another dimension is Variety - Data comes in all types of formats – from traditional
databases to hierarchical data stores created by end users and OLAP systems, to text
documents, email, meter-collected data, video, audio, stock ticker data and financial
transactions. By some estimates, 80 percent of an organization's data is not numeric! But it
still must be included in analyses and decision making.
Organisations should consider two additional dimensions of Data: Variability and
Complexity. Variability refers to the inconsistent peaks in data loads which occur on a daily,
seasonal, or event-triggered basis. Complexity refers to the need to cleanse, manage,
correlate, and analyze large amounts of data coming from multiple, disparate sources.
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The Data Management concept
A Data Management landscape includes: Data Integration, Data Quality, Master Data
Management, Enterprise Data Access and Data Governance.
Data Integration
Data Integration is the process of collecting or extracting data from one or more
sources, transforming and integrating this disparate data into a common data model. Then
the integrated data is loaded into a target database, application, or file.
This also referred to as the data warehousing process which can be executed in batch
or real-time modes, and which may be used for both operational and decision support use.
Data Quality
Data Quality is the process of profiling, cleansing, augmenting, and integrating
customer and business data.
Data profiling is done to categorize and segment data to assess its relative quality and
identify nuances, discrepancies, and inaccuracies in data records which need to be resolved.
Data cleansing is the process of eliminating or reducing identified inconsistencies by
either excluding, accepting, correcting, or inserting data as needed.
Augmentation refers to the process of adding unrelated external data to the existing
data records in order to gain further insights.
Through integration one identifies and combines common data regarding the same
customer (or product) from multiple sources.

Data Management and Master Data Management
Master Data is the key information to the operation of a business, such as data about
customers, products, employees, materials, or suppliers. It may be used by several functional
groups and stored in different data systems across an organization, and it may or may not be
referenced centrally. It can contain duplicate and/or inaccurate data.
Master Data Management, or MDM, refers to the framework of processes and
technologies used to create a master record to be used across the enterprise, as the single
version of the truth. MDM ensures a complete, consistent, and clean view of an
organization’s master data by creating rules on that data’s use.
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Enterprise Data Access
Enterprise Data Access refers to the ability to provide transparent access to data
stored on a variety of platforms and formats. Data Access Engines and Data Surveyors allow
you to read, write, and update data regardless of its native database or platform. These
engines could provide access to data warehouse appliances, enterprise applications,
mainframes (nonrelational data sources), PC files, relational databases, and Hadoop
Distributed File System.
Data Federation tools provide a single point of real-time data access across the
enterprise. Using a Data Federation Server, organizations can provide multiple users the
ability to view data from multiple sources through integrated virtual views. Users can see
integrated data while it remains stored in its source application, without physically moving
it.
A Service Oriented Architecture and Messaging Support enables improved flow of
information across the entire organization. Integration Technologies provide integration of
asynchronous business processes via message based connectivity. Data from unrelated
systems can be gathered, stored, analysed and distributed in a simple and timely manner.
Information Management
Information Management doesn’t refer so much to a product, as it does as to a
concept.
If the below diagram represents an organization’s information continuum, then
Information Management manages that entire continuum through unified technology

solutions, as well as through strategy and implementation services that span data, analytics
and decision management.
It is an environment that enables businesses to strategically manage and govern their
data as a valued corporate asset, driving both core operational processes and fact-based
decision making.
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Governance and Roles
Successfully managing an enterprise’s data as a valuable asset requires an
overarching strategy and executive oversight. According to industry specialists, Data
Governance refers to the organizing framework for establishing strategy, objectives, and
policies for corporate data.
With the people and process requirements scoped out and assigned to the appropriate
business and IT stakeholders, an effective Data Governance structure provides the essential
next step to an organization’s data governance program.
Data governance encompasses two aspects: firstly, data stewardship to streamline the
collaboration between the business and the IT and secondly, the best practices involved in
orchestrating people, processes and technologies to align data management initiatives to the
corporate business objectives.

Chapter 3 – A journey into a digital, Omni-channel customer
experience
Through the digital channels, today’s generation of customers is truly empowered.
The customer is no longer king but rather dictator. It is the customer who decides when,
where, through which channel and what for he wishes to be addressed. Customer behaviour
changed dramatically and companies need to take up the challenge with this change but also
with the explosion of data.
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Digitalization
Digitalization describes the act of converting from analogue to digital. But in today’s
business terms it refers to an emerging business model of the integration of digital
technologies, like electronic channels, content and transactions, into everyday life by the
digitalisation of everything that can be digitized. So speaking it symbolizes a broad shift
towards Internet-based business and consumer software. Leading analyst firms call this trend
the "digitalization" of business. Despite the unwieldy terminology, they highlight an
important point: cost cutting and improving efficiency are critical goals for IT, but are no
longer the absolute measures of IT success. For example: Gartner calls the digitalization of
business a "third era of enterprise IT," following a period in which IT strived to standardize
processes and deliver services efficiently. The following diagram, illustrates the progression
toward a world in which IT innovation supersedes efficiency as the primary metric:

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Customer Centricity
The concept of customer centricity refers to the concept of putting the customer and
his experience at the centre of each business process by creating a positive experience before,
during and after the sale.
A customer-centric approach can add value to a company by enabling it to
differentiate itself from competitors who do not offer the same experience.
Today’s customers expect far more than e-commerce or even a multichannel
presence. They expect an authentic, relevant experience across various channels. They
expect companies to manage and integrate all their data so that they get an immersive
experience – regardless of the channel where they engage with the company. Success in
today’s business environment demands an obsession with customer experience that is not
only memorable and consistent, but also relevant and timely – especially from digital fronts.
It’s not just about the experience of interacting with marketing, but every touch point
across the entire organization. The experience needs to be both positive and consistent
wherever it happens. To meet those customer expectations, companies need to:
Use customer analytics to gain insights from both the physical and the digital selling
worlds to achieve an informed business strategy centred on the customer,
Access transactional, behavioural, social and other data from multiple channels,
Align strategy with the customer’s expectation of one seamless experience across all
channels,
Find answers in customer data to pinpoint the best opportunities, map out the best
marketing actions and then maximize cross-business impact.
In summary, when you think about Omni-channel strategy, think of it as one strategy
across all media, focused on the customer and context by aligning the marketing process to
the customer journey and constructing the marketing process. It is required within the
interaction with clients, not only to optimize results from a customer perspective, but also
from operational and financial standpoints.
Given all the shifts in customer expectations and cross-channel opportunities how
should a modern concept look like? The answer emerged in a framework based on the “five
Cs” of marketing. With the so-called 5 C’s, a way has been developed to put customer-centricity
and cross-channel concepts in context.

The five C’s of Marketing and Customer Intelligence
Content is all of the information about products and lifestyle that companies can use
to help educate customers. Early in the sales process, this is category-level information that
helps customers understand general attributes of the purchase decision. Later, it is product-specific
information that guides them to a selection, especially for technical products.
Community is the collective set of opinions and influencers that guides a client’s
purchase decision. This community now includes many voices the customer trusts but does
not know and will never meet, such as online reviewers and passionate brand advocates who
are actively engaged with the company. With the advent of social media, the transparency
of opinions and the power of social influence, the control of a company’s brand is slowly
moving to the market – not to the marketing department. Reputation needs to be thought as
being a proxy for brand value. Marketers need to understand and respond to how customer
experiences are being voiced – mitigating reputational risk where sentiment is negative, and
leveraging, echoing or amplifying where it is positive and all in real time.
Commerce is all the shopping power a company has available to turn an interaction
into a transaction – from price and offer to the digital shopping cart – in whatever form it is
presented to the customer . It’s all about the ‘Buy’ button, now that customers can click to
purchase online, from their mobile phone or from a digital kiosk in a public place, the point
of purchase became more conceptual than physical.
Context is understanding where the shopper is on the path to purchase and
conforming to the customer’s specific needs and wants at that point.
Customer insights are a necessary precursor to context. Context is gleaned from the
gigabytes and exabytes of data collected about customers and alongside all this data, it is the
ability to analyse it in order to get insights into real behaviour, rather than educated guesses
based on simple measures such as demographics. Marketing is increasingly being expected
to provide insights and analytics (across the organization) about their customers, to better
inform strategy and identify opportunities and threats with greater precision and speed. The
same analytics are expected to optimize marketing investments so that they can do more
with less, at the right time, in the right channel with the most appropriate customers.
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Customer Intelligence in Banking
Consider the astounding volumes of financial transactions that banks have managed
for years - combined with vast customer, operational and regulatory data surging from
multiple sources. It’s no wonder that 92 percent of the cost of business for financial services
firms is data.
What needs to be done with all that data? Clearly, operating from day to day requires
banks to acquire, distribute, process, store, retrieve and deliver data that’s spread across
multiple formats and locations.
But going forward, banks must move well beyond those basics. Soon, Banks will
need to be able to quickly and effectively tap into and analyse every bit of available data,
structured and unstructured alike, to make the right decisions that strengthen and advance
their business. More specifically, they need to understand behaviour and risk exposure at the
customer level, across all touch points. A modern bank needs to find the optimal channel
mix for their customers and replace or supplement traditional revenues with enticing new
products and improve operational efficiencies. Additionally, they need to adhere to a
multitude of new regulatory requirements.
There is no doubt about it. In banking, big data equals big challenges. Fortunately,
banks can meet these challenges with confidence, by using analytics to turn their big data
into pertinent new business insights.
Transforming the raw data into structured inputs, eliminating duplicates and
unwanted data elements and deriving intelligent insights based on customers’ information
and banking behaviour forms the crux of analytics. Analytics open up the door to deeper
client understanding and help in building lasting customer relationships by devising the right
sell strategies, rolling out successful marketing campaigns and in reducing the risk of fraud.
Statistical models and advanced calculation methods applied to client data form the
backbone of customer intelligence. Different types of analytics serve different purposes in
gaining intelligence about banking clients. Here are some of the most relevant:
Customer analytics, customer segmentation, attrition analysis, profitability analysis
Marketing analytics, analyses on success rates of marketing campaigns
24
Fraud analytics, detection analysis
Risk analytics, credit risk analysis

By gaining the right level of customer intelligence with the newest analytical
methods, banks can obtain a considerable advantage in revenue generation processes and
client retention.
25
Bank 3.0
The customers of the information age have been empowered by greater choice and
access, by better, faster and more efficient modes of delivery and service. Two major factors
in creating behavioural change or disruption are the psychological impact of the internet age
and the associated innovative technologies. Each of the factors contribute to create a
paradigm shift in the way banking needs to be considered today. The four phases of
behavioural disruption can be summarized as follows:
Phase one was the era of the rise of internet and social media providing control and
choice to users. The second phase is occurring right now and it concerns the intense use of
screens and smartphones giving the user the possibility to be connected anytime and
anywhere, also for their banking usage. In phase three the shift to mobile wallets will take
place, the user becomes cardless and cashless by using his devise of choice for payments.
Finally the fourth phase will enable the user to be pervasive and ubiquitous as anyone is a
bank. (Here meant as concept)
Now the concept of Bank 3.0 and its future evolution could be described and
discussed on miriades of pages but this work does not intend to dive deeper in this subject,
important to know although is that within a business model transformation, also, the trends
and behavioural evolutions need to be taken into account.
Client expectations
Within Maslow’s hierarchy of needs, todays’ modern and hyper connected consumer
finds full self-actualisation in the technological and competitive choices that are given to
them. Self-actualisation is the highest state that human beings wish to achieve on a
psychological level.
What are the different psychological estates and feelings that a customer expects
today to achieve when he buys? The client is in control, if the proposal does not meet his
expectations, he walks away to another bank. He has the abundances of choice, as he is
better informed due to extensive informational resources. He gets better deals because
banks have to work harder to get him as customer and he saves money as the margins have

been squeezed to fit his expectations. In the end, the client gets better-quality solutions
because they fit more precisely his needs than previous packaged one-size-fits-all solutions.
Banks who do not consider these drivers of choice and selection and if they are not
able to offer the desired flexibility and level of control and empowerment will be penalised
by their clients.
26
Exposure to fraudsters
The need to improve customer experience has led banks to increase demands on fraud
detection. Addressing “Gen-Y” demands will put at risk the traditional fraud and risk
controls. This further need consists in protecting the bank in real-time against online and
smart phone transactions and to be able to respond to malware attacks. Banks also need to
assess risk in near real-time applications so that good customers can be give credit instantly,
but with increased accuracy.
Financial criminals do not operate in silos like financial institutions are organized.
So change is essential to keep pace with the threats and to reduce risk and cost. Criminals
do not segment themselves by product or service or geography. What they are actually doing
when committing fraud or laundering money is taking advantage of a weakness of the
system. Silo approaches, limited use of analytics, separate and redundant case management
systems – are all limitations of legacy systems.
Fraud, financial crime and security risks are top concerns across multiple industry
sectors, but traditional approaches to dealing with such risks are proving to be insufficient.
What is needed is an enterprise wide strategy that puts analytics at the foundation to
unify how organizations deal with all security-related matters and enable more successful
detection, prevention and investigation efforts.
Financial institutions must begin to look at national and public security trends
holistically across the enterprise in order to identify large-scale threats early in their
development while there is still time to mount effective countermeasures that deliver
maximum impact.
Successful fraud detection
An end-to-end technology infrastructure for detecting, preventing and managing anti-fraud,
compliance and security efforts across various business lines would be most effective.

This framework should include components for detection, alert and case management, along
with category-specific workflow, content management and advanced analytics.
The long-term goal to persuade, is to establish a framework for enterprise-wide
deployment of resources, including both material and human assets. This framework should
make it possible to gather and cross-match relevant data from all product lines,
organizational units and geographic regions of the organization and then analyze that data to
“connect the dots” and spot large-scale fraud attacks early in their life cycle. The framework
needs to plan and execute focused countermeasures to combat large-scale attacks.
There are two key business drivers that are causing organizations to give serious
27
attention to an enterprise-wide strategy.
One is increased effectiveness, which is the ability to look at the issues holistically
across the enterprise and identify large-scale threats early in their development and mount
effective countermeasures while there is still time for them to have maximum impact.
The other one is increased efficiency, which is the ability to leverage investments in
data, tools and staff in an economic environment where every organization and function is
being asked to “do more with less.”
In order to combat and detect fraud effectively and efficiently, a hybrid approach for
fraud detection is essential. Only when banks combine several analytics and detection
processes, the alert generation process can deliver its full value. As a fact, the hybrid
approach combines automated business rules with anomaly detection, predictive modelling,
network generation and social network analytics, entity matching and text mining. Which
are also used in Advanced Analytics. And again, Advanced Analytics, configurability, data
management and reporting/dashboards are key differentiators to help addressing these
business drivers.
When it comes to financial crime, the speed of detection is crucial. Identifying initial
fraud attempts by criminals helps save considerable sums of money. By unifying the
databases, the solutions allow for faster, more effective detection of attempted fraud. The
systems also stand out for their flexibility and scalability by making use of collected data
and trends regarding potential fraud.
Several large financial institutions around the globe are already using the described
hybrid approach in order to successfully detect and combat fraud attacks and they have been
able to reduce considerably the fraud losses that impacted the bottom line revenues.

Part 2
-
Advanced Analytics in Banking

29
Chapter 4 – Advanced Analytics
Analytics is a word used in different ways, by different people. So then, what is
analytics?
Defining Advanced Analytics
Analytics refer to the range of statistical techniques and processes. It is the use of
quantitative methods for diagnosing the past to predict the future and gain data-driven insight
for better business decisions.
It can also be described as a process encompassing a range of techniques dealing with
the collection, classification, analysis, and interpretation of data to gain insight, reveal
patterns, anomalies, key variables and relationships.
Analytics supports continuous learning and improvement.
Ultimately, the purpose of analytics is to help create value for businesses looking to
increase their revenues and improve their bottom line.
Predictive and prescriptive analytics, also referred to as advanced analytics, drive
proactive business decisions. Companies can accelerate their analytics processes, and better
leverage significant value from their data, using High-Performance Analytics.
The value derived by companies using analytics results from the answers discovered
to a broad range of questions regarding their business. Descriptive analytics can help answer
questions such as:
What happened?
Where exactly is the problem?
How many, how often, where – did a particular event occur?
What actions are needed in response to the information obtained’?

Do you notice a pattern regarding these questions and their potential answers?
Answers to these questions tell companies what has already happened in the past. At best,
this type of discovery can identify what actions are needed in response to events which have
already occurred – it places companies in a reactive decision-making mode.
A closer look at these questions reveal a different discovery process; one that is
30
forward-looking:
Why is this happening?
What will happen next?
What if these trends continue? And
What is the best that can happen?

One can analyse past data to reveal previously undetermined patterns, anomalies, key
variables and relationships, which can then be used to model and predict future events, and
determine the best course of action moving forward. Predictive and prescriptive analytics
help executives become more proactive in their decision-making, optimizing their
probability for business success.
These reactive and proactive discovery questions align with a broad range of
analytics capabilities that provide varying degrees of value to organizations. Descriptive
analytic capabilities shown in green at the bottom of the below graph, do provide value for
businesses.
But not as much value and competitive advantage as the advanced analytics shown
31
in blue at the top of the graph.

Advanced analytics go beyond statistics and include data mining, forecasting, text
32
analytics and optimization.
Multiple sets of possibilities
Historically, business intelligence systems have relied primarily on business rules.
This has been good for identifying reoccurrences of lessons that have already been learned.
But there are three main issues with utilizing only this methodology. First, business rules
create a lot of noise. Legitimate customers constantly do things that are not consistent with
their profile. For example, deposit a check greater than average, submit a claim, change their
address, add a bill pay to their online banking. Inadequate client segmentation takes time to
triage and result in operational inefficiency. Second, business rules become common
knowledge to fraudsters. Either by trial and error or worse, infiltration of the organization,
business rules become known. Which results in a risk to the organization, which results in
constant tweaking of money thresholds, which result in more operational inefficiency. And
third, business rules aren’t forward looking. They aren’t there to catch tomorrow’s
opportunity or for instance fraud.
What a hybrid approach offers, what an approach utilizing advanced analytics offers,
is a methodology that helps counter the problems that a business rule only approach fails to
address. Using the concept of risk factors we can begin to move into a world where we are

money amount agnostic. Organizations shouldn’t be forced to only try to analyze behavior
and transactions over a certain amount of money. In today’s economic climate every Euro
counts. Secondly, a hybrid approach delivers true insight in information. And finally,
Advanced Analytics bring new opportunities and visualization possibilities to the table. It is
about discovering previously hidden relationships and patterns that are meaningful to an
organization.
Within the predictive modeling, companies can perform knowledge discovery, data
mining, predictive assessment based on previous disposition of alerts and cases. Neural
Networks, decision trees, generalized linear models, econometric models and gradient
boosting to mention only some of them.
Banks can unlock the power of unstructured data within reports, staff notes, and
websites with text mining tools including anomaly detection, like identifying individual and
aggregate abnormal patterns that exist within the data. Some statistically used measures are:
mean, standard deviation, percentiles, univariate and multivariate regression, clustering,
sequence analysis and peer group analysis.
In the digital era of social networks, another powerful method is the social network
analysis which establishes connections between people and businesses through associative
linkage analysis. E.g. Social network + linkage analysis + community detection + advanced
analytics.
In the below shown table, the increase in efficiency and effectiveness in fraud
detection, resulting from the extensive usage of advanced analytics is visualized.
33

Building a Centre of Analytical Competencies
Now that the challenges and possibilities have been described, the concept of
Advanced Analytics is not working on its own but it requires the right capabilities to put it
at work. As Advanced Analytics are embedded in technology and unleash their power within
the business purposes and processes, the technology is not intended to be only operated by
IT but it needs to be included into a collaborative structure.
IT will become a true business enabler by putting at disposal to the business the right
technology in the right measure and the right access. The business needs to be able to access
the necessary data sources with that right technology at any time. This access to data and the
right technological tools can only be effective and efficient if the users have the right
capabilities and competencies to understand the business and the data that needs to be
analysed but also how to correctly address these analysis.
Business analysts and IT only are not anymore enough today in order to build up
analytical competencies within an organisation. New job positions are created such as data
analysts, data scientists and visualization specialists. A modern analytics unit within a bank
should become a common standard in order to build up a centre of analytical competencies
where IT capabilities, digital content and technology capabilities and strong analytical
capabilities could perfectly merge into each other and create an analytics culture.
34
Analytics culture
An analytics culture unites business and technology around a common goal through
a set of behaviours, values, decision-making norms and outcomes. As companies tend to
have different analytics cultures within the same organization and many companies facing a
skills gap just as they are pressured to up their analytical competencies, every major project
could be managed by a cross-functional team that includes IT, product developers and data
analysts. Therefore banks should expand their analytics programs and « democratize » data
and analytics throughout the entire organisation.
The components of an analytics culture should reflect following approaches:
The integration of Information Management and analytics into strategy, the
promotion of analytics best practices and a collaborative use of the data across all company
lines, the planned investments in analytical technology including new talent acquisition and
training and the pressure from senior management to become more data-driven and

analytical. Data should be treated as a core asset and analytical insights should guide the
future strategy as analytics will change the way business is conducted and it causes a power
shift in the organisation.
35
Advanced Analytics at work
In order to illustrate the described topics, I would like to provide a true life example
where Advanced Analytics have been used by a bank to increase customer experience and
revenue. All relevant confidential data has been anonymized.
Proactive client engagement
Bank X was looking to increase customer experience and revenue and therefore they
changed their traditional branch business model towards a modern multi-channel,
analytically-oriented business organisation. The bank invested in the necessary
competencies and technology and empowered the organisation with an analytics culture.
When they started to make extensive use of advanced analytics, they discovered
hidden patterns in their customer data and so they used this newly gained insight. Actually
they discovered that many of their retail customers applied for a smaller, 3 years loan
approximatively every six years. Most of them occurred end of January, beginning of
February and over 90% were destined to purchase a new car.
By analysing the customers’ account inflows, they also discovered that in January,
inflows increased and that those were end-of-year bonus payments from the company they
worked for. When they analysed the customers’ interaction behaviour, they noticed that a
lot of these customers used mainly the online channel to interact with the bank.
Every year, during a certain period, car resellers offer special rates when customers
buy a new car during this short period. In the past, the bank did a marketing campaign just
before that period in order to attract customers to subscribe the loan for a new car with the
bank. These flyers have been sent out via postal mail to each and every customer of the bank.
When they analysed the effectiveness of that campaign and the return on investment
of it, they discovered that the bank invested every year a considerable amount in a campaign
that resulted in a low ROI and a quite important lack of effectiveness.
Once that their analytics unit got involved, the bank started to address the issue in a
much different approach. Proactively, the bank campaigned, through the adequate channel,
their customers by proposing tailored loans at the right moment and the next year, they

accounted an increase of 25% of new loans. The “online banking” customers experienced
that the bank addressed them through their channel of preference with a tailor-made offer
and in consequence, many of them did not wait 6 years to purchase a new car, but already
purchased one the next year that their former 3 years loan has been fully paid back.
By putting Advanced Analytics at work, the customer experience has been increased,
customer loyalty has been increased, marketing expenses have been lowered and revenues
have been boosted up.
36

Chapter 5 – Analytics in Banking redefined
What is the current “state of play” in the marketplace? What is the impact on banks?
Organisations see a radical change in how their customers are behaving - they not only see
this in the volume of contacts through different channels - online AND offline.
They also see it in how much more difficult it is to maintain existing sales revenues
and to develop new ones. The changes are not just about Gen X or Gen Y. Customer
expectation has increased exponentially - across all major segments.
37
The Decision Hub
The Decision Hub can render the access to information quite easy and affordable for
companies of any size. It accelerates planning, monitoring and analysis while increasing
process accuracy with immediate access to a variety of trusted data sources. It helps in
making better informed decisions using analytical indicators to anticipate changes and
opportunities within the bank’s environment. The Decision Hub combines a variety of data
sources representing thousands of data points and indicators and automatically also
incorporates external data into one single technology. This reduces the amount of time banks
spend by manually finding and importing data, ultimately allowing them to quickly focus on
gained insights and knowledge with combined internal and external information for a more
accurate picture.
Where the Decision Hub comes into play
Many organisations have already invested millions in trying to improve their
customer marketing programs - and they have indeed seen some benefits. Typically these
benefits tend to be in the area of improved efficiency. They can do more customer marketing
campaigns and use more channels. Sometimes this results in piecemeal “tactical” projects to
try to improve results in a certain product line; or through a specific channel (web, email or
mobile) etc. …. It’s inconclusive.
The major challenge now is to improve marketing effectiveness - since the
competitive battleground is moving toward the impact at the individual customer level.
The downside of efficiency only is: banks have the ability to do bad marketing even
more efficiently.

To be more effective means also to broaden the organisational need for “getting it
right” beyond marketing. Other organisational disciplines e.g. Service and Risk departments,
are now regarded as being intrinsically linked to the customer sales & marketing effort.
By recognizing what a customer tells the bank what he wants may not be (and is
38
almost certainly not) what he needs.
This has the effect of driving sales behaviour away from focus on specific product(s)
sales - and much more towards trying to understand what the implicit needs are. Banks can
differentiate themselves on HOW they sell not with WHAT they sell and by fixing the
overall business effectiveness topic issue - not just efficiency.
Why will the Decision Hub help banks in their transformation?
Because they need it. The Decision Hub concept focuses on how to achieve truly
transformative impact on their business and on how they can generate and measure value
out of Digitalization.
Big Data, Analytics and Digitalization are the buzzwords which are top of mind for
many banking leaders. Nearly every organization has already spent money in these areas.
But its relatively small money for small and tactical projects like Social Media or A/B
testing, and similar. They do it, mostly because they want to learn and find out what could
work for them. The market is still in a try-and-test mode.
According to a recent McKinsey survey, most organizations are struggling to
recognize value from their current digital investment. Only 7% say their organizations
understand the exact value from digital, and only 4% report high returns of that investment.
It’s not about tools or features, it is about business value. Digitalization must be an
integrated part of overall business processes. Focus must be on organization-wide impact. It
is not digital only, it needs to be digital and “traditional” in order to improve effectiveness
and return on investment.
Organizations need to merge Digital and Omni-Channel with Big Data and Analytics
and their existing processes and assets. The Decision Hub solution, a channel-independent
decision logic infused with value-based marketing, is exactly addressing this point. Value
comes with the right decisions on what to do with which customer and how to address the
client.

39
Example of a solution concept:
Example of successful transformation
A leading company detected a need to improve the capability to cross-and upsell
products to its customer base. Standard customer base campaigns did not sufficiently take
into account the individual context of today’s customers. Especially the product usage and
the client interaction behaviour could not be processed and analysed in (near) real-time on
an individual customer level. Additionally, the company was not able to execute decisions
and campaign fulfilment in (near) real-time.
In implementing and using the Decision Hub concept, they have been able to
decrease the gathering of client information from one day down to near time. They have
been able to present individualized offers to their clients through real-time analytics and they
have been able to identify the clients to be contacted straight away after a marketing
campaign by using campaign analytics. This resulted in an increase of 25% in campaign
revenue and an increase of 20% of their margin.

40
High-Performance Analytics
Proven analytics infrastructures provide superior performance, scalability and
reliability.
High Performance Analytics (or HPA) enhances that environment by significantly
accelerating calculation-intensive processes that look at all of the data, not just a sample.
This can be executed in seconds or minutes, rather than hours or days.
The result: decision makers can efficiently run, and re-run calculations to assess
numerous scenarios and make high-stakes decisions with greater confidence.
Thus, companies can leverage significant value from their data using High-
Performance Analytics. The key components of a HPA environment should include:
Grid Computing - which enables organizations to create a managed, shared parallel
computing environment to process large volumes of data and analytic programs more
efficiently.
In-Database technology, which enables companies to run analytics inside the
database, as opposed to a data warehouse or data mart, thereby avoiding time-consuming
data movement and conversion. For decision makers, this means faster access to analytical
results and more agile, accurate decisions, and
In-Memory Analytics – which divides analytics processes into easily manageable
pieces and distributes responsibility for parallel computations across a set of blade servers.
It solves complex problems in near-real-time with highly accurate insights by allowing
analytical computations to be processed in-memory and distributed across a dedicated set of
nodes.
It’s all about speed
At the pace that decisions need to be taken, it is of outmost importance to be able to
take decisions when facts occur or before they will occur and not only once they already
have impacted the banks business. Even if banks get the most accurate insights out of an
analytical culture, this knowledge can only bring its full effectiveness if it is infused with
speed, with High-Performance. Reducing the time-to-market is another essential point in
increasing customer experience. Customers do not want to wait anymore until they receive
an answer from their bank concerning a loan request, a service request or a simple account
enquiry.

By using High-Performance Analytics, banks are able to achieve much faster their
set goals in terms of operational efficiency and time-to-market decisions while reducing IT
spending. They can differentiate and innovate to stand out in their market segment.
41
A Visual Revolution?
Another important set in modern analytics is the graphical representation of the
computed calculations and statistical results. Until recently, companies needed to develop
cubes and code on IT-side in order to create graphics that represented the results of their
analysis or they used, and many still do, standard Excel files to create those charts.
Data Visualization is a quick way to gain rapid information from data that is often
very descriptive in nature. For example, exploration of customer data would show counts
related to number of males versus females, number of customers in specific areas or
geographies, number of sales of boots to men versus women, etc. By using some basic bar
charting techniques, one could easily spot some interesting trends but it will still remain only
descriptive and reactive.
The developments and the coding required IT ressources and capabilities whereas
the business defined the needs and matrixes of these reporting tools. Collaboration is work-intensive
and somewhat time-consuming for both sides and changes in the analysis such as
the insight in information is only possible in a reactive approach. Time-to-market decisions
are nearly impossible to achieve in this mode in addition to a high operational risk by using
Excel.
The good news is that nowadays some tools exist where the creation of Olap-cubes
and coding is becoming obsolete and the business analysts have the possibility to work in a
self-service manner when it comes to access the necessary data and that visual
representations can be done by an intuitive and user-friendly “click and point” approach.
The new approach defines Analytics for everyone: easy to use without programming.
Statistical analysis and results are not easy to translate into meaningful analytic
visualizations like correlations, regressions, forecasts, scenario analysis, decision trees and
text analytics organized in word clouds and content categorization.
The benefits that are provided by a visual analytics software to the business are many,
they span business intelligence benefits like: providing self service capabilities,
collaboration, ease of use, mobile reporting, easy report designing and information

dissemination as well as providing easy to use analytics in support of fueling an analytics
based culture within any organization.
Analytic visualizations like forecasting, scenario analysis and others provide critical
insight for decision making. It is an easy-to-use, yet sophisticated way to support the
democratization of analytics, it can help answer complex questions faster, enhancing
contributions from analytic talent and expanding the use of analytics to more business users.
The view of much more data, at detail levels, instead of samples and summaries
improve quickly the understanding what is happening in ‘data’ and companies are able to
see patterns that they haven’t been able to see before. However, Analytic Visualizations
provide more interesting details that result in rapid insight and even foresight. For example,
an analytic visualization of customer data would show that there is a strong relationship (high
correlation) between women and a particular type of boot sold in a specific state. Another
analytic visualization would predict the future revenue of boots in a particular geography,
and help determine growth.
Analytic visualizations are critical for being able to truly gain insight from the data
and ultimately allow users to share and distribute that information with others that convey
more insight and foresight than hindsight.
Standard reporting tools for decision makers are becoming less efficient as the
requirements in terms of interactivity are increasing and in some companies that visual
revolution is already taking place. Some examples of powerful visualisations are shown here
below.
42

It is critical for companies to display data in ways that leverage the human visual
capabilities and empower people to discover predictive insights from data. As the human
being is more likely to focus on visual representation than on plain text, companies and the
market is only starting to use and explore in that area but this concept is meant to remain and
to revolutionize the way decisions will be taken in the future.
43

44
Conclusion
After decades of consistent success, banks face a period of historic change. Many of
the profitable mechanisms developed in the years leading up to the financial crisis are now
obsolete and unlikely to be revived any-time soon. The banking business model is under
pressure from a combination or regulation, technological change and customer
empowerment. While banks strengthen their balance sheets in the recent period, there has
been little progress towards sustainable growth.
The transformation towards a sustainable business model will rely on the banks
capability to perform a transformation in culture, in technology and business model in order
to drive revenue growth.
Over the last year, whenever I met with practitioners, IT and/or decision makers I
listened to their pain points in addressing business challenges and their future visions on how
affecting positively their business environment. The challenges are huge and it seems like
they will not decrease in the future but nevertheless, the commitment of all these people
working in the financial industry here in Luxembourg, and abroad, provides a sense of
positive outlook and is encouraging.
This work was intended to provide a high-level overview of some of the challenges
that especially banks face today and how the technological possibilities might and will
support them in driving impact on their business and how Advanced Analytics can be key in
the transformation process to achieve a sustainable business model.
The use of analytics is still developing at an early stage as many companies are
struggling to figure out how, where and when to use analytics. The intention to pursue in
their approach to adopt analytics is clearly stated throughout the market but very few can
nowadays report that they are using analytics intensively throughout their entire
organisation. The analytical innovators are for sure more likely able to create a competitive
advantage from analytics than their counterparts. Especially banks, which break up with
traditional, obsolete business models, can reboot banking by embracing the new analytical
culture and capabilities.
I hope that this work delivers a first hindsight of what could be achieved with
Advanced Analytics and that it could yield in benefits for the Luxembourgish market players
and that the raised quote of T.S. Elliot from the introduction found a few answers.

45
Bibliography
King, B., Marshall Cavendish, (2013). Bank 3.0 – Why banking is no longer
somewhere you go, but something you do
Baesens, B., Wiley, (2014). Analytics in a big data world – The essential guide to
data science and its applications
King B., Wiley, (2014). Breaking Banks – The innovators, rogues and strategists –
Rebooting Banking
Simon, P., Wiley, (2014). The visual organisation – data visualization, big data and
the quest for better decisions
Fraunhofer, IAO, Fraunhofer Verlag, (2013). Trendstudie Bank & Zukunft 2013
McKinsey & Company reports
CSSF annual report 2013
LuxembourgforFinance reports
SAS Library
MITSloan reports

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