YOUR GUIDE TO LEVERAGING DATA & ANALYTICS IN FINANCE

1. Financial Advanced
Analytics Readiness
YOUR GUIDE TO LEVERAGING DATA & ANALYTICS
IN FINANCE
WHITE PAPER
OCTOBER 2018
finance.arvato.com

2. ABSTRACT FINANCIAL ADVANCED ANALYTICS READINESS
Advanced Analytics in finance is
a complex field which conceals
more than $1 trillion potential
for businesses worldwide.1
Advanced Analytics in finance is a complex field which conceals more than $1 trillion
potential for businesses worldwide.1
Identifying new opportunities and optimizing
data-driven decision-making will allow you to unlock this potential. However, market
maturity differs greatly across industries and companies and, interestingly, doesn’t
correlate with the amount of investments made in this area. This leads to the conclu-
sion that accelerating Advanced Analytics Readiness is a complex process requiring
balancing efforts across a range of different fields, for example data management,
change management and human resources.
This report provides expert advice for finance executives and analytics thought
leaders who want to leverage the power of Advanced Analytics within the revenue
life cycle (e.g. credit, fraud, accounting, collections) and want to move forward on
their analytics journey, no matter at what stage they are currently. The topics dis-
cussed in the Financial Advanced Analytics Readiness Survey form the foundation of
this report. If you have not yet taken the survey, you can do so by clicking here. Based
on survey answers, respondents are classified into one of three universal adaptation
stages (Laggard, Adopter or Leader). These indicate the current Financial Advanced
Analytics Readiness of their companies. Through the examination of six key compo-
nents, the report delivers actionable insights for each phase of the Advanced Analytics
journey and offers:
An overview of the key components of an Advanced Analytics strategy
Easily-digestible and finance-related insights on each key component
A checklist providing free expert advice, rather than theoretical explanations,
no matter whether your company is a Laggard, an Adopter or a Leader on the
analytics journey
Abstract

3. 01 Executive summary
02 The rise of Advanced Analytics
03 The analytics adaption stages
04 The six key components of a holistic analytics strategy
People
Tools
Data
Change management
Models and methodology
Operational model
05 Next steps at a glance
06 Get in touch with our experts
07 About the author
08 Sources
09 About Arvato Financial Solutions
CONTENT
Content
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FINANCIAL ADVANCED ANALYTICS READINESS
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4. The desire to reach the best decision possible has long been the goal of finance
managers all around the world.Their ability to do just that lies in uncovering insights
through detailed analysis, combined with a precise prediction about the future. Recent
technological advancements like Big Data, Artificial Intelligence and Machine Learning,
as well as a significant increase in computational power and cost-effective data storage,
have pushed analytics to a whole new level.This is referred to as Advanced Analytics.2
Rather than just analyzing correlations, analytical models can precisely predict future
business outcomes and enable data-driven decisions.This means that a combination
of business intelligence-dashboards as well as predictive and prescriptive models is the
new standard.
Today, Advanced Analytics is still a long way from being a widely applied practice –
although everybody is talking about it. A recent survey of 304 CFOs and other senior
financial leaders from companies with revenues between $100 million and $20 billion
revealed that 24% have currently implemented Advanced Analytics. At the same time,
another 50% aim to implement Advanced Analytics in the next two years. According
to the survey, the top automated processes within finance are accounts payable (35%),
financial reporting (33%) and treasury (27%).3
These relatively low numbers underline
the huge potential waiting to be leveraged within the finance arena.
Those who have already invested (heavily) in this area are struggling to create business
value. According to a recent Harvard Business Review Survey4
, less than one-fifth of
744 business executives surveyed around the world from a variety of industries are
receiving a sufficient return on their investments. Our experience as financial analytics
consultants unveiled a wide range of Advanced Analytics use cases along the revenue
life cycle, which are very well positioned to demonstrate immediate ROI – for both B2C
and B2B business models. However, success depends on many factors, which need to
be considered according to your individual situation, as well as your company’s strategy
and objectives.The prerequisite for a successful project implementation is to assess
Advanced Analytics Readiness using the key components we outline in this report.
Only when Advanced Analytics Readiness is assessed properly, can a suitable scope
and ROI-impact for each project be derived. Investing only in a software tool might
not be enough.
Participating in our Financial Advanced Analytics Readiness Survey gave you an initial
idea about where your company stands in this process. Now, based on our long-stand-
ing consulting experience in the field, we invite you to discover concrete measures that
you can take to further advance on your journey.
If you see potential for improvement in how your organization currently leverages Data
Analytics, you should keep reading on and become the champion of change in your
organization.
Enjoy the report!
Joerg Brendemuehl
Vice President, Analytics Consulting Services
Arvato Financial Solutions
Executive summary01
01 EXECUTIVE SUMMARY
2
FINANCIAL ADVANCED ANALYTICS READINESS
DefinitionofAdvancedAnalytics
Advanced
Analytics
Prescriptive What actions to be taken?
Predictive What will happen?
Diagnostic Why did it happen?
Descriptive What happened?

5. 02 THE RISE OF ADVANCED ANALYTICS
3
FINANCIAL ADVANCED ANALYTICS READINESS
Just another technology hype? Whether we’re talking about the Internet around the
2000s or cryptocurrencies in recent years, most technological breakthroughs feature
a so-called Hype Cycle in their life cycle at some point.Technological breakthroughs
often create a huge buzz at the peak of inflated expectations and then fail to create real
business value once they reach their plateau of productivity. While Advanced Analyt-
ics didn’t create the same sized buzz as cryptocurrencies for example, we, along with
Gartner, the publisher of the Hype Cycle, and many others believe that Advanced Ana-
lytics will still sustainably change the way we design and operate business processes.5
Advanced Analytics is still perceived as a relatively new technological advancement
that offers a competitive advantage for the few companies that actually apply it across
all their business functions. But, as Analytics use cases for businesses become more
tangible, we must assume there will be a tipping point in the nearer future which will
change this perception fundamentally. Once Advanced Analytics reaches the Plateau
of Productivity, it will lose its competitive advantage and companies will no longer
differentiate themselves solely through it.This is why the current journey on the Slope
of Enlightenment is a “last call” for businesses to jump on the bandwagon and prevent
the competition from moving ahead of them. However, take your time and align your
data analytics strategy carefully because the fear of missing out can easily lead to
extremely poor decisions. Your strategy should cover at least six key components – and
we will outline these later in the report.
Advanced Analytics - the key to the sustainable transformation of finance
CFOs are confronted with a changing business role profile and finance leaders are cur-
rently figuring out how to implement the transformation of finance departments from
meredata suppliersto consultative business partners.7
The business partner role profile
calls for the combination of data, business and financial know-how. Advanced Analytics
can make a significant difference by connecting these three attributes. While financial
analysts have a specific analytical skillset for finance and for identifying critical perfor-
mance indicators, Advanced Analytics leverages the data of existing data warehouses
and transactional data, as well as additional semi- and unstructured data, to provide
operational insights beyond mere performance indicators.Those insights will help fi-
nance executives build on their existing skillset, solve even more complex problems and
increase the degree of automation as well as their ability to give powerful recommen-
dations to the entire organization. In doing so, they will fulfil their role as a consultative
business partner.They will also be able to detect risks and opportunities before they
arise, and react immediately in the best way possible when required. Advanced Analyt-
ics can help ensure companies become best-in-class in all their financial processes along
the revenue life cycle - from order to cash. McKinsey expects companies will leverage
about $1.1trillion through Advanced Analytics in Risk Finance worldwide.8
Let’s explore how you can get your share of the cake.
The rise of Advanced Analytics02
Visibility
Time
1
2
3
4
5
How the Hype Cycle works
An Innovation Trigger (1) kicks
the Hype Cycle off towards the
Peak of Inflated Expectations
(2) which leads into a Trough
of Disillusionment (3). From
there on, the hype works its
way up the Slope of Enlighten-
ment (4) until it reaches the
Plateau of Productivity (5). The
model illustrates nicely how
the perception of a technology
changes in each phase.6

6. 03 THE ANALYTICS ADAPTION STAGES
4
FINANCIAL ADVANCED ANALYTICS READINESS
Using the results from the answers you gave in the “Financial Advanced Analytics Read-
iness” survey we classified your company into one of the following groups – laggard,
adopter or leader. If you have not taken the survey, you can do so by clicking here.
Before diving into the six key components, we will clarify what each of the adaption
stages means and how they differ. Each question in the survey offered multiple-choice
answers that included the following options: below-average (lagging), advanced
(adopting) and sophisticated (leading).The compilation of the answers for each ques-
tion generates the final classification.
Companies classified as Laggards often selected “below-average”. Of course, it is possi-
ble that they are actually further developed than the results indicate, or even sophisti-
cated in terms of the adoption level of certain components. Still, the unbalanced level
of readiness across the components justifies the classification as Laggard. Companies
in this classification are often yet to decide anything about their analytics strategy and
consequently lack a clear guide for building up the necessary resources within each
component. However, they might have already started first initiatives.
Those classified Adopters frequently selected “advanced” and, for nearly every
below-average component, there is another which can be considered as sophisticat-
ed, which justifies the classification as Adopter. Companies in this classification have
usually already successfully delivered their first Advanced Analytics pilot projects.They
are paving the way to increase the level of maturity of each component, using the
momentum of their first pilot projects to address additional use cases.
Leaders have already made substantial progress in their analytics efforts. While they
might still lag behind in one component, the average response was sophisticated,
leading to their classification as Leader.The challenges leaders face often become more
complex compared to those less mature peers face – such as scaling results across more
use cases or talent management.
The analytics adaption stages03

7. We have used insights gained from past Advanced Analytics projects to devise six key
components that together provide a suitable environment from which to successfully
deliver analytics projects.
Data is the central component of the environment and is surrounded by People,Tools
and Methodology.These are the factors that influence the primary interaction with
data.The Operational Model and Change Management are the two organizational
factors that influence how analytics is effectively integrated and executed across the
organization.The operational model is particularly crucial when it comes to the “last
mile” – from Proof-of-Concept through to production.
Let’s deep dive into each component and explore what companies can do at every
adoption stage to strategically improve components’ capabilities and Advanced Analyt-
ics readiness.
04 THE SIX KEY COMPONENTS OF A HOLISTIC ANALYTICS STRATEGY
Six key componentsofAdvancedAnalytics
FINANCIAL ADVANCED ANALYTICS READINESS
CHANGE MANAGEMENT
O
PERATI O N A L M ODEL
DATA
T
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ETHODOLOGY
PEOPL
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Building a holistic Financial
Advanced Analytics strategy
The six key components of a holistic analytics strategy04
5

8. 6
Insight: A big TELCO injected
external experience in order to
kick start the analytics jour-
ney and deliver first tangible
results. These were used to
train and educate the work-
force and management team.
They were enabled to make
and also explain data-driven
decisions internally towards
sales, marketing and product
management, which led to an
alignment of annual targets
and substantial growth.
FINANCIAL ADVANCED ANALYTICS READINESS04 THE SIX KEY COMPONENTS OF A HOLISTIC ANALYTICS STRATEGY
6
People
Good teamwork splits the tasks to multiply success.This is also true for analytics
projects where cross-functional cooperation is key to reaching your strategic goals. A
good place to start is to create cross-functional pilot teams that investigate Advanced
Analytics applications and are able to bridge all siloes – not just data.
People with data-driven profiles need to be proactive team players who are able
to apply their skills to help solve the team’s challenges and help others understand
the company’s data mechanics. At the same time, the existing workforce needs to
be on-boarded in data analytics. Finance managers need to truly understand what
data-driven decision-making means for their job and how they can best use the data to
generate insights.This is why it is necessary to train the existing financial workforce as
well as hiring data scientists.
When building up data science resources you also need to assess what skills are needed
temporarily and long-term. Once you identify roles, you can then assess which roles are
best kept in-house, and which should be supplied by external partners.
Leaders in this area have already built up their data science resources and are working
on implementing a data-science organization. Many have also introduced C-level posi-
tions, such as Chief Data and/or Chief Analytics Officer, to make sure the topic attracts
the necessary management attention.
LAGGARDS ADOPTERS LEADERS
Setup a cross-
functional pilot team
(internal/external)
and encourage strong
collaboration
Adapt hiring strategy
to attract data talent
Educate workforce
about advanced
analytics
Create C-Level data
analytics positions in
order to foster
responsibilities
Decide on a centra-
lized, decentralized
or hybrid approach
for your data science
organization
Setup and integrate
a data science
organization
Next steps/ checklist
CHANGE MANAGEMENT
O
PERATI O N A L M ODEL
DATA
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9. 7
Tools
Tools are a bicycle for the mind.To leverage analytics in finance, you need to ensure
everyone has the knowledge to use the tools in place.
While spreadsheet software is great when it comes to usability, there are certain limita-
tions to what spreadsheets can do.That is why leading adopters of Advanced Analytics
use dedicated software that enables their workforce to perform Advanced Analytics as a
self-service without having to learn script languages like R or Python.
During early stages it makes complete sense to test different tools and evaluate what
each can do best. Once you outline a strategic approach, the definition of a fixed toolset
enables the scalability needed for company-wide adaptation. Keep in mind that data
scientists will require different functionalities to functional experts.The more people get
involved with analytics, the more technologies like augmented analytics must enable
self-service data visualization. Similarly, collaborative platforms must enable mutual
cooperation between finance and analytics professionals. Collaboration and
leveraging synergies with standardization (re-use) will become even more important
as the number of users grows. In the end, two different approaches could also lead
to a hybrid – ideal tools for each analytics step (mainly a combination of open source
tools) or a comprehensive enterprise platform, which is able to cover all steps from data
ingestion to model execution.
Next steps/ checklist
FINANCIAL ADVANCED ANALYTICS READINESS
LAGGARDS ADOPTERS LEADERS
Explore and test
different tools –
don’t force strategic
decision yet
Standardize toolset
and environment
Start tool-specific
training
Adapt coherent
toolset to integrate
them into the
productive and
staging environment
Enable citizen and
collaborative data
science
Start gaining synergies
through standardizati-
on and re-use
04 THE SIX KEY COMPONENTS OF A HOLISTIC ANALYTICS STRATEGY
Insight: At Arvato, we have
observed massive performance
differences (up to 25%) in
terms of the predictive power
of the final model across
different platforms using the
same algorithm.*
*Based on testing five major platforms
CHANGE MANAGEMENT
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10. 8
Data
Just like oil, data has to be discovered. Finance processes generate an immense
amount of data – both structured and unstructured – thanks to the need for traceability,
accountability and security. Still, according to data protection and recovery provider
Veritas, approximately 50% of all data stored and processed by enterprises globally is
today what is called “dark data”. Its value is unknown.
Raw data needs to be complemented with metadata that puts the information in
context and describes its source.To give data meaning, good data management must
be incorporated into production systems where data is generated.
The generation of consumable and interpretable data is key to applying Advanced
Analytics. Deciding how data is collected and made available requires transparency be-
tween departments’ data silos and knowledge about how data correlate to each other.
Beyond data volume and veracity, you should also consider the velocity at which it can
be processed for real-time decision-making.The last aspect is especially important in
finance contexts where decisions (such as accept/reject/refer a transaction) need to be
made immediately.
Next steps/ checklist
FINANCIAL ADVANCED ANALYTICS READINESS
LAGGARDS ADOPTERS LEADERS
Initiate company-
wide data exploration
in order to gain
transparency about
the “world of data”
Setup a centralized
data hub (incl.
non-finance data such
as CRM, supply chain)
which enables finance
to carry out powerful
analysis
Unify metadata
management
Establish a mature
data governance
framework, including
local data responsi-
bilities
Constantly evaluate
the value of available
internal and external
data
Design systems and
processes to produce
consumable data for
corporate advanced
analytics initiatives
Insight: A large utility company
reduced its write-offs for
certain segments by up to 4%
by leveraging power consump-
tion trends combined with CRM
and AR-data for a pre-delin-
quency early warning system.
04 THE SIX KEY COMPONENTS OF A HOLISTIC ANALYTICS STRATEGY
CHANGE MANAGEMENT
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PERATI O N A L M ODEL
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11. 9
Change Management
Align change initiatives with culture and strategy. On the strategic level, Advanced
Analytics promoters must demonstrate how the efforts will have a positive impact on
the company’s strategic goals. Analytics thought leaders will only gain leadership sup-
port when efforts are backed by an attractive business plan.This is true for the short-,
mid- and long-term.To initiate a strategic approach towards Advanced Analytics,
the leadership must be convinced the long-term value justifies adapting the resource
planning process.
The cultural aspect of change management in the implementation and deployment
of financial Advanced Analytics is probably one of the most overlooked aspects in the
process. During analytics projects, data scientists have contact not only with finance
experts, but also software engineers and operations managers – which means lots of
interests have to be considered.To achieve cross-functional cooperation, companies
need to map department goals and create win-win situations.This will help ensure
different departments work together towards mutual goals. As data is often siloed in
departments within large organizations, you need to foster a culture of collaboration to
maximize its benefits at a corporate level.
FINANCIAL ADVANCED ANALYTICS READINESS
LAGGARDS ADOPTERS LEADERS
Create a (positive)
sense of urgency and
explain the WHY
Measure and cons-
tantly communicate
business value and
impact aligned to
your data analytics
strategy
Link your data and
analytics strategy
to your corporate
strategy and explain
the necessity
Increase employee
involvement through
idea management etc.
Encourage the orga-
nization to come up
with new analytics
ideas
Foster data-driven
collaboration across
the entire organiza-
tion
Next steps/ checklist
Insight: An eCommerce
company established an idea
management platform. This
uncovered more ideas and use
cases which could benefit from
Advanced Analytics. As a result,
data-driven momentum was
created across the entire
organization. Employees
developed a data-driven
mindset and were talking
data analytics across
departments.
04 THE SIX KEY COMPONENTS OF A HOLISTIC ANALYTICS STRATEGY
CHANGE MANAGEMENT
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PERATI O N A L M ODEL
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12. 10
Models and methodology
Understanding is everything. Any methodology applied to the implementation and
operation of financial Advanced Analytics needs to be based on a thorough understand-
ing of your business and goals, of the concrete use case, the potential incremental ROI,
and the corresponding data.
Another important aspect is the model building itself. While testing and comparing
models to ensure you choose the one that performs best when it comes to sensi-
tive areas like finance, it is common practice not to rely on the prediction of just one
algorithm. Ensemble learning combines the predictions of individual algorithms into
a joint model and this is a great way to increase predictive performance and increase
the quality of the final model. More and more advanced modeling techniques can be
enhanced with self-learning approaches.These models use labeled data (= performance
of past decisions) to automatically adapt to changing circumstances and, in the long
term, require less operative maintenance than static models. However, explainability
and traceability are mandatory for certain use cases and therefore not suitable for every
algorithm, model or approach, such as automated feature engineering.
FINANCIAL ADVANCED ANALYTICS READINESS
Next steps/ checklist
Insight: We applied best
practice for Expected Loss
prediction from the banking
sector to a telecommunications
client. The goal was to improve
application decisions and go
from a segment view down to
an individual application (seg-
ment-of-one). The result was a
potential of at least +2 to 3%
bottom line.
LAGGARDS ADOPTERS LEADERS
Apply and test
different methodolo-
gies and algorithms
in pilot projects
Apply a standardized
approach for each
project in order to
make them repeatable
and comparable
Create a decision ma-
trix with an algorithm
and approach that
suits specific use cases
and explain why
Adapt methodologies
to your specific needs
and leverage for each
initiative
Use a mature
governance
framework to
control the applica-
tion regularly
Clearly defined roles
and responsibilities
along the develop-
ment process with
quality gates and
approval processes
04 THE SIX KEY COMPONENTS OF A HOLISTIC ANALYTICS STRATEGY
CHANGE MANAGEMENT
O
PERATI O N A L M ODEL
DATA
T
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M
ETHODOLOGY
PEOPL
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13. 11
Operational model
Looking at Advanced Analytics as a one-off-shot is not enough. You need a well-de-
fined operational model to ensure continuous sustainable business impact.The oper-
ational model is actually part of the implementation phase of certain methodologies,
such as CRISP-DM, however our experience shows that this factor is critically important
and merits separate discussion.There are a couple of factors to consider that can affect
the effectiveness of the model.The first is failing to plan effectively for deployment.
As with change management, communication plays a key role in ensuring that people
across the organization are onboard with the implementation and deployment – both in
terms of what to expect and why.
Another factor that can limit an operation model’s effectiveness is the failure to monitor,
maintain and update the model over time, as well as not being able to track model
history and versioning. A static model cannot remain truly relevant with changing market
conditions, consumer/client behaviors, portfolio structure and legislation.This is why
establishing a sustainable monitoring framework is mandatory, as it will ensure that
decisions remain accurate over time. Interestingly in this regard, non-regulated businesses
sometimes forget this step, whereas regulated businesses are very mature by nature.
FINANCIAL ADVANCED ANALYTICS READINESS
Next steps/ checklist
Insight: An operational
machine learning monitoring
framework triggered an alert
for a comprehensive manual
model validation process as the
portfolio structure changed
rapidly within a few weeks.
The validation resulted in an
increase of €2 million to the
bottom line. In other words,
they were able to prevent
themselves from rejecting
healthy business.
LAGGARDS ADOPTERS LEADERS
Design monitoring
framework and define
responsibilities
Standardize the
monitoring frame-
work
Automate anomaly
detection and provide
potential root causes
automatically
Introduce and test
adaptive/self-learning
techniques
Constantly run
champion-challenger
Clearly defined set of
KPIs for model quality
including thresholds
04 THE SIX KEY COMPONENTS OF A HOLISTIC ANALYTICS STRATEGY
CHANGE MANAGEMENT
O
PERATI O N A L M ODEL
DATA
T
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M
ETHODOLOGY
PEOPL
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14. 12
Too busy to read the entire report?Take a look at the next steps and discover what you can do with each key element to leverage
Data Analytics in your company to ensure you create a successful financial Advanced Analytics strategy.
PEOPLEDATA
MODELSAND
METHODOLOGY
OPERATIONAL
MODEL
CHANGE
MANAGEMENT
TOOLS
Next steps at a glance05
LAGGARDS ADOPTERS LEADERS
Setup a cross-functional pilot
team (internal/ external) and
encourage strong collaboration
Adapt hiring strategy to attract
data talent
Educate workforce about
advanced analytics
Create C-Level data analytics
positions in order to foster res-
ponsibilities
Decide on a centralized, decentra-
lized or hybrid approach for your
data science organization
Setup and integrate a data
science organization
Initiate company-wide data
exploration in order to gain
transparency about the
“world of data”
Setup a centralized data hub (incl.
non-finance data such as CRM,
supply chain) which enables
finance to carry out powerful
analysis
Unify metadata management
Establish a mature data gover-
nance framework, including local
data responsibilities
Constantly evaluate the value of
available internal and external
data
Design systems and processes
to produce consumable data for
corporate advanced analytics
initiatives
Apply and test different
methodologies and algorithms
in pilot projects
Apply a standardized approach
for each project in order to make
them repeatable and comparable
Create a decision matrix with an
algorithm and approach that suits
specific use cases and explain
why
Adapt methodologies to your
specific needs and leverage for
each initiative
Use a mature governance frame-
work to control the application
regularly
Clearly defined roles and respon-
sibilities along the development
process with quality gates and
approval processes
Design monitoring framework
and define responsibilities
Standardize the monitoring
framework
Automate anomaly detection
and provide potential root causes
automatically
Introduce and test adaptive/self-
learning techniques
Constantly run champion-
challenger
Clearly defined set of KPIs
for model quality including
thresholds
Create a (positive) sense of ur-
gency and explain the WHY
Measure and constantly commu-
nicate business value and impact
aligned to your data analytics
strategy
Link your data and analytics stra-
tegy to your corporate strategy
and explain the necessity
Increase employee involvement
through idea management etc.
Encourage the organization to
come up with new analytics ideas
Foster data-driven collaboration
across the entire organization
Explore and test different tools –
don’t force strategic decision yet
Standardize toolset and
environment
Start tool-specific training
Adapt coherent toolset to integ-
rate them into the productive and
staging environment
Enable citizen and collaborative
data science
Start gaining synergies through
standardization and re-use
FINANCIAL ADVANCED ANALYTICS READINESS05 NEXT STEPS AT A GLANCE

15. We hope this report outlines a practical discussion framework for your finance de-
partment and your fellow analytics gurus. Are you keen to evaluate your company’s
current status and identify the next steps to releasing the potential Advanced Analytics
present?Then we suggest you share this report with your colleagues to kick off the
discussion.
You might have encountered some arguments and views in this report that do not
correspond to the picture in your company – and you may be right. As every company’s
revenue life cycle is individual, general abstractions like those we made in this report
fail to reflect individual cases. We strongly believe that every finance department has to
be examined on its own and that an Advanced Analytics strategy has to take individual
properties and environments into account. If you would like to discuss your individual
situation with our experts, please don’t hesitate to get in touch!
Get in touch with our experts06
06 GET IN TOUCH WITH OUR EXPERTS 07 ABOUT THE AUTHOR
13
FINANCIAL ADVANCED ANALYTICS READINESS
Discuss your individual
situation with our experts!
“Every project is unique and
provides new insights that can
be connected with experiences
gained from previous, partly
cross-industry projects to cre-
ate added value. I’m inspired by
the discussions I have with cus-
tomers in order to identify the
best possible solution for them.
Our standardized approach re-
duces complexity and ensures
success without losing sight of
individual conditions.”
Joerg Brendemuehl isVice President Analytics Consulting Services at Arvato Financial
Solutions. Data, BI and Analytics are passions he has been pursuing for over 15 years and
he drives these subjects globally across all industries.
Joerg Brendemuehl
Vice President Analytics Consulting Services,
Arvato Financial Solutions
Contact:
joerg.brendemuehl@arvato.com
About the author07

16. 1. McKinsey Global Institute: “Notes from the AI frontier”, (08.10.2018).
2. Gartner IT Glossary: “Advanced Analytics”, (08.10.2018).
3. GrantThornton: “Investment in new technologies reaches the finance function”,
(08.10.2018).
4. Harvard Business Review Analytics Survey: Uncovering the Keys to BecomingTruly
Analytics-Driven, (08.10.2018).
5. Gartner: “Beyond yesteryear‘s hard-coded algorithms and manual data science
activities, machine learning (ML) promises to transform business processes […]”,
(08.10.2018).
6. Gartner: “Beyond yesteryear‘s hard-coded algorithms and manual data science
activities, machine learning (ML) promises to transform business processes […]”,
(08.10.2018).
7. Harvard Business Review: “Advanced Analytics and the CFO”, (08.10.2018).
8. McKinsey Global Institute: “Notes from the AI frontier”, (08.10.2018).
Sources08
08 SOURCES
14
FINANCIAL ADVANCED ANALYTICS READINESS

17. Arvato Financial Solutions –
Your backbone for growth.
finance.arvato.com
Arvato Financial Solutions provides professional financial services to renowned inter-
national brands as well as respected local businesses — allowing them to leave their
credit management to a professional, so they can focus on what matters most for their
business. Our services center around cash flow in all segments of the customer lifecycle:
from identity, fraud and credit risk management, to payment and financing services and
debt collection.
The Arvato Financial Solutions team is made up of proven and reliable experts in around
20 countries, including 7,500 IT, analytics, process and legal specialists, dedicated
to revealing the advantages of big data, advanced foresight, predictive analytics and
strategic consultancy. All employees share one common goal: to make client’s credit
management run effortlessly and effectively, enabling optimized financial performance.
We provide consulting services for Business Intelligence and Applied Advanced
Analytics within the finance domain with focus on:
- Business Intelligence – such as connecting and visualizing relevant data
- Advanced Analytics – such as building machine learning models
- Finance Process Advisory – such as ensuring results are operationalized
Visit our homepage to find out how Arvato Financial Solutions can give businesses the
best possible platform for growth.
US accounts:
Julian Kleindiek - julian.kleindiek@arvato.com
Global European accounts:
Joerg Brendemuehl - joerg.brendemuehl@arvato.com
About Arvato Financial Solutions09
09 ABOUT ARVATO FINANCIAL SOLUTIONS FINANCIAL ADVANCED ANALYTICS READINESS