Staying on top of your financial closing process

In practice we are not quite there yet. The full potential of digital solutions is not achieved and a mixture of IT-solutions and manual work – read: many process and system breaks – still prevails. This means a considerable source of inefficiencies and presents great potential for errors in many companies. To avoid this, all processes, and systems, as well as the organizational structure, should be coordinated.

When looking at the processes in the financial statements, we can’t overemphasize the importance of having an end-to-end perspective to eliminate the weaknesses successfully. Technology and digital enablers allow companies to centralize their entire record to record (R2R) scope, from transactional processing to data reconciliation.

Organizations have more data than ever before. To turn data into truly value-driven reporting and to produce financial information even faster, finance teams should focus on utilizing digital solutions for closing the accounts (automation and digitization) and building trust in data analytics (data and process analysis). The digital transformation also implies to think differently about the people finance teams recruit.

The closing revolution

More automation requires standardization. In this regard, Process Analysis will tackle the challenges at their source. It helps to discover the standardized processes. Gaining a common understanding of the processes is the start to identify areas for improvement and further implementation of technological support. Process Mining enables users to analyze the processes and compare them to the processes theoretically and to detect inefficiencies and possible anomalies.

Cloud-based solutions offer the possibility to process key accounting processes, even in very heterogeneous system environments. This processing replaces the complex and error-prone work in different spreadsheets or systems on different servers and enables the finance departments to build up extensive transparency of the closing process and trust in real-time data. Supporting the entire closing process across the company also enables the audit trail to be tracked without interruption. 

Finance can use machine learning and artificial intelligence to build prediction models, improve outcomes and look for underlying patterns. These models can ‘learn’ from the data and predict outcomes to completely new cases.

EY has developed a machine learning anomaly detector for journal entries (JE’s). This develops a model that predicts the number of debits and credits posting to each general ledger account each day. For each day that has a significant variance for a given general ledger account, all of the journal entries for that day related to that account will move to anomaly detection. It identifies the outlier entries within that population.

Robotics can be deployed in several areas. The software can mimic human interaction with core systems, web, and desktop applications to execute processes. It combines structured and unstructured data in reports. Robotics can also automate report distribution and collection.

Transforming the finance workforce

The finance function benefits from team members with new capabilities beyond traditional finance and accounting skills, including strategic awareness of new technologies such as AI, RPA and knowledge in disciplines as data science and advanced statistics. This should include both the hard skills required to utilize new technologies and data, and soft interpersonal and strategic skills.

In addition to looking at the required competencies, finance leaders should also consider their future operating model. As their organization’s strategic priorities will likely change fast, finance and reporting should have the agility to change along with them. Flexible models, such as managed services, are one way to address this. Tools and data allow finance teams to work more closely with external vendors to deal with these challenges.