The Ledger

Why Top Performers Are Shifting Their Finance Processes to Shared Services

A growing number of organizations are choosing to move their finance processes to a centralized shared services center, aiming to reduce cost while improving productivity and efficiency. The shared services metric is considered a key performance indicator for efficiency of finance processes. Across all major finance processes, top-performing finance shared services centers have significantly better staffing ratios than lower performers do. Becoming a top performer is about committing to best practices in process management, structure, governance, and technology.

Read More at CFO Magazine >

Achieving Proactive Continuous Accounting

“The concept of continuous accounting is all about getting access to financial data faster. One key concern with speeding up the financial close process is ensuring that you don’t sacrifice quality for speed.”

One key principle of continuous accounting is to use technology to distribute departmental workloads continuously across accounting periods. Modern financial management systems typically provide strong workflow, operational analytics, and rules to institute operational controls, and ensure that they can effectively be maintained in an environment of change.

Read More at The Digitalist by SAP >

A New Role For CFOs

The role of the CFO is ever-evolving along with the global economy, mostly in part due to digitalization. In order to protect from cyber-attacks, CFOs have had to transition to more of a “Cyber CFO”. Cyber risk is nothing new, however the magnitude of the disruption has grown exponentially, and the role of the CFO is changing accordingly. While the role of the CFO in this arena is still a moving target, many observers are calling for the finance chief—not the CIO or chief risk officer—to lead the charge in ensuring company data is safe and that investors are well informed about any risks related to cyber-attacks and data security.

Read More at Strategic Finance Magazine >

Predictive Analytics vs. Machine Learning

Although machine learning and predictive analytics are both centered on efficient data processing, the two are very different. Machine learning is a method of computational learning underlying most artificial intelligence applications and is considered a modern-day extension of predictive analytics. Predictive analytics can be defined as the procedure of condensing huge volumes of data into information that humans can understand and use. While businesses must understand the differences between machine learning and predictive analytics, it’s just as important to know how they are related.

Read More at The Digitalist by SAP >