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Three key takeaways from our Data & Analytics in Finance event in Atlanta

A wide range of financial challenges can be tackled with Advanced Analytics

Multiple Atlanta-based Finance & Analytics executives accepted our invitation to learn more about how Data & Analytics transform financial processes, making our networking event in Atlanta a great success. It fostered the creation of an active community for Financial Advanced Analytics and provided participants with insight into how they can tackle current analytics-related challenges in their businesses. For those who were unable to attend, I have put together the main takeaways from our sessions and the discussions between the participants.

1. Data Visualization is an essential part of Advanced Analytics projects

Advanced Analytics can provide actionable insights into your business – but how do you best present them? The challenge is to make something complex appear easy, because in the end you are presenting more than just basic information. Insights gained through BI and visualization allow you to recognize why things are happening and show how you can apply the new knowledge to improve your business processes. If your business is looking at rolling out a results-driven Advanced Analytics project, you can follow a universal six-step process: framing the problem, collecting, processing and exploring the data, performing in-depth analyses and, finally, communicating results. Tony Brown, Senior Consultant at Arvato Financial Solutions, walked the participants through this process.

2. Focus on crucial groundwork before implementing AI & ML in finance

Another point that Finance & Analytics executives are extremely interested in is the application of AI & ML (Artificial Intelligence and Machine Learning). They are facing huge challenges in the implementation phase due to problems with data management and legacy systems. Dan Sarkar, Senior Data Science Manager at Arvato Financial Solutions, focused on the groundwork that is crucial for the successful adoption of AI in a financial context. There is a lot of data and information available, but it needs to be structured and managed at high-volume, -velocity and -veracity levels to enable AI & ML in a business context.

3. A wide range of financial challenges can be tackled with Advanced Analytics

Jörg Brendemühl, VP Analytics and Consulting Services, looked at AI from a practitioner’s perspective and discussed real-world business challenges within the revenue lifecycle that can be solved with Advanced Analytics. Examples ranged from automated-feature engineering used for fraud prevention to collection optimization using unstructured data for decision-making.

We are pleased that the audience was very engaged, and we will continue with this interactive format for future events in order to provide the participants with maximum value. Our next event is scheduled for September 27.

If you would like to register for the event now, please click here to email Julian Kleindiek, Business Development Manager at our Atlanta site.

Would you like to learn more in the meantime? Click here to read the latest post about the six key elements of a holistic Financial Advanced Analytics strategy.

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