White paper financial analytics

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 components, 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 1 FINANCIAL ADVANCED ANALYTICS READINESS 2 3 4 5 6 7 8 9 10 11 12 13 13 14 15

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 currently 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 performance 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 finance 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 question generates the final classification. Companies classified as Laggards often selected “below-average”. Of course, it is possible that they are actually further developed than the results indicate, or even sophisticated 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 sophisticated, 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 O OLS M ETHODOLOGY PEOPL E 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 journey and deliver first tangible results. These were used to train and educate the workforce 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 positions, 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 centralized, 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 T O OLS M ETHODOLOGY PEOPL E

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 standardization 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 O PERATI O N A L M ODEL DATA T O OLS M ETHODOLOGY PEOPL E

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 between 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 responsibilities 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 O PERATI O N A L M ODEL DATA T O OLS M ETHODOLOGY PEOPL E

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 constantly 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 organization to come up with new analytics ideas Foster data-driven collaboration across the entire organization 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 O PERATI O N A L M ODEL DATA T O OLS M ETHODOLOGY PEOPL E

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 understanding 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 (segment-of-one). The result was a potential of at least +2 to 3% bottom line. LAGGARDS ADOPTERS LEADERS 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 framework to control the application regularly Clearly defined roles and responsibilities along the development 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 O OLS M ETHODOLOGY PEOPL E

13. 11 Operational model Looking at Advanced Analytics as a one-off-shot is not enough. You need a well-defined operational model to ensure continuous sustainable business impact.The operational 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 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 04 THE SIX KEY COMPONENTS OF A HOLISTIC ANALYTICS STRATEGY CHANGE MANAGEMENT O PERATI O N A L M ODEL DATA T O OLS M ETHODOLOGY PEOPL E

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 responsibilities Decide on a centralized, decentralized 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 governance 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 framework to control the application regularly Clearly defined roles and responsibilities 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 urgency and explain the WHY Measure and constantly 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 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 integrate 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 department 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 create 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

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