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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

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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

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.

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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

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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

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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.

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.

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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

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

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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.

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

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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.

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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.

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.

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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.

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

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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.

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.

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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.

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.

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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

Fraud analytics, detection analysis

Risk analytics, credit risk analysis

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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.

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

26

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.

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.

27

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

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

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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’?

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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

forward-looking:

Why is this happening?

What will happen next?

What if these trends continue? And

What is the best that can happen?

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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

in blue at the top of the graph.

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Advanced analytics go beyond statistics and include data mining, forecasting, text

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

33

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.

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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.

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

35

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.

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

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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.

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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.

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.

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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

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.

41

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.

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

42

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.

43

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.

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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