Nowadays advanced analytics is transforming business processes in every conceivable industry. As a result, the hype surrounding big data and advanced analytics is reaching fever pitch. Here, Jörg Brendemühl, Vice President Analytics & Consulting Services at Arvato Financial Solutions, sifts through the complexity of today’s financial world and outlines how advanced analytics is enabling business leaders to successfully drive the digital transformation of their financial processes.
What awaits you in this article:
What is the success rate of Big Data / Advanced Analytics initiatives and what is the current application rate of Advanced Analytics methods in financial processes?
Which decisions can be supported by Advanced Analytics and what role does change management play in this?
Why particular focus on financial processes can be profitable?
What role does the human factor play in the development of advanced analytics models?
Click around online and you’ll notice most mainstream media outlets, such as Wired and The Financial Times have published stories in recent months espousing the virtues of analytics. Inevitably, such articles recount how data scientists have grappled with complex datasets and successfully unlocked insights which have gone on to make — or save — their company billions of dollars.
Of course, that’s not always the case.
I’m exaggerating a little to help illustrate a wider point. Namely, that advanced analytics is extremely popular and at the same time, for many, not very tangible. But does it deserve the attention it’s getting?
Having worked in this industry for my entire career, I can confirm that the benefits of advanced analytics in the finance space are as real as it gets. If you’re not already leveraging advanced analytics for finance processes, it might be time to jump onboard.
But be forewarned, careful planning of advanced analytics projects is imperative. If you choose the wrong approach you risk wasting time and losing money – according to Gartner analyst Nick Heudecker the failure rate is close to 85 percent.
Helping successful leaders manage their business during uncertain times
Thanks to the widespread buzz surrounding advanced analytics, I expect that most C-level executives already feel an immense pressure from key stakeholders and the market to find ways to introduce and foster advanced analytics into their business – at least in order to demonstrate any progress. With that said, the adoption for finance functions is not as high as you might think. According to a Grant Thornton survey, advanced analytics is around 24 percent and Machine Learning, in particular, is around 8 percent – with significant expected growth in the next one to five years. So, what is it that appeals to these leaders?
Advanced analytics in a nutshell
Advanced analytics describes the collection and examination of data using powerful techniques and tools, typically beyond those of traditional Business Intelligence. It is used to discover deep insights that allow for the identification of business-improving and value-generating actions by leveraging predictive and prescriptive analytics. Examples of advanced analytic techniques include those such as data/text mining, machine learning, pattern matching, sentiment analysis, and forecasting, to name a few.
One reason for the great interest is that executives have recognized the huge potential of advanced analytics for more efficient operational decision-making and as a basis for optimizing internal processes. Improving performance, boosting revenue, increasing profits, and enhancing customer satisfaction and retention are business goals that advanced analytics can help achieve.
It can also facilitate the automation and prioritization of processes internally, thereby improving efficiency and slashing costs — other goals also close to every CEO, CFO and shareholder’s heart.
I think it’s also important to note that advanced analytics can deliver major benefits beyond the operational level; Data & Analytics combined with successful change management methods enable enterprise-level transformation and empower business management at the highest strategic level and help mitigate investment risks.
Why financial advanced analytics?
Recently, one of our major international clients said: “One of the major problems of data science initiatives is being able to show ROI. Finance is very well positioned to show ROI of the data science/advanced analytics projects in which it participates.”
Optimizing cash flow at every point across the customer journey is our area of expertise. This provides a 360-degree view of the customer life cycle, helping to drive customer value across all touchpoints.
Process efficiency and cost savings, as well as incremental revenues, are three of the main benefits of financial advanced analytics. We have many great examples with immediate ROI and the positive impact financial advanced analytics has had on a client’s business by leveraging the vast amount of data in combination with our advanced analytics techniques, finance process experience, IT implementation capabilities and change management expertise.
Just one click away?
The hype surrounding analytics and artificial intelligence (AI) implies that everything can be automated with just one click. It can’t. This oversimplifies the complex work required to extrapolate insights from data, never mind the expertise required to interpret it and measure business impact. This also applies to the latest trends like automated feature engineering which offers additional support to data scientists but won’t replace them – at least not in the near future.
Algorithms play an important supporting role in the data-crunching process, but data scientists still need to develop advanced analytical models based on their insights and experiences. Some people fear the rise of the machines, but a human overview and a strategic mindset are essential, especially when context matters. In other words, it’s not possible to click one button and expect to get all the answers required.
At least not yet anyway.
Adopting financial advanced analytics into your business processes might feel like a daunting task. That’s why it’s important to partner with a provider with a deep understanding of the challenges and opportunities you’re facing, as well as your market and industry. The aim is to define clear use cases with concrete goals, and then start with the first successful case studies that create the foundation for efficient change.
Find out more about how our Financial Advanced Analytics can streamline, automate and optimize your business processes.
Over the coming series of expert insights, we’ll dig deeper into the ways we’re utilizing financial advanced analytics to transform businesses. Look forward to use cases from practice, such as:
– Opportunities and challenges in the use of automatically generated variables, using the example of a fraud prevention model
– Which algorithm is most suitable for which application in the context of financial processes?
– Identical algorithms in different development environments: but what does the selectivity say? (Focus: Debt Collection Segmentation)
– Optimization of dunning processes using internal and external information in combination with Advanced Analytics methods.
If you’d like to learn more about our Financial Advanced Analytics insights series, or if you have questions and wish to get in direct contact with our experts, send us an email here.