Traditionally, the accounting function’s role is always viewed as that of a steward, the control centre for the organisation, rather than the catalyst for enterprise growth. The term ‘back office’ is often used to describe the operating nature of accounting function. So in today’s volatile global business environment, the key challenge for accounting function is how to lead the enterprise in its growth strategies while ensuring effective risk management and stewardship of the enterprise.
With complexity and data proliferation, increasingly the CEO and the board turn to accountants to help make sense of all the data, to help cut through this complexity, and to provide more informed analysis on the business and its operation. The opportunity for accounting function is if it can generate the insights that help make better corporate decision making, while continuing to ensure effective control of the enterprise, its reputation as a catalyst for growth will be guaranteed. To do so, traditional accounting departments may have to transform themselves into ‘intelligent accounting functions’.
Intelligent accounting functions run their operations as cost effectively as possible, leveraging technology to reduce finance operating costs; strengthen stewardship and control so as to establish a solid foundation to support growth. The biggest challenges, however, lie in creating the efficiencies needed to gather and process basic financial data and continue to deliver traditional finance outputs while at the same time redeploying their limited resources to enable higher-value business decision support activities.
This accounting analytics practicum which adopts SMU-X approach, focuses on a few key topics that are vital to establishing an intelligent accounting function (refer to the diagram below): finance strategy and transformation, lean finance and finance shared services, business intelligence analytics, and enterprise process management. In this experiential learning course, students from various disciplines learn what comprises a highly optimised accounting process, design an end-to-end process management and explore the underlying accounting IT systems and advanced data analytical applications. By working closely with instructors from accounting and information systems disciplines, together with an industry partner, students are expected to carry out design and development of an intelligent accounting function solution. The whole idea is to engage students in real-life application and to encourage students to creatively apply concepts to practical problems in their pursuit of solving real-world problems. Students will apply the concepts in real projects.
This course contributes to the development of the following learning goals:
LO2.1 Our students understand and can apply business concepts and principles.
LO2.2 Our students can communicate effectively in a business context.
LO2.3 Our students understand the principles of leadership and team building in
a business context.
The Deloitte project focused on exploring the changing roles of auditors and examined ways to simplify, streamline and harmonise essential audit process with Artificial Intelligence (AI). Students had to envision how the future of audit and the profession may change as the traditional roles are transformed with AI, to become more efficient, strategic and catalytic. Requiring audit, AI and business knowledge, coupled with active mentorship from the course instructors and Deloitte representatives, students researched and recommended accounting processes that could be simplified and streamlined, which include rethinking how structures and skills could evolve within the audit profession.
Students designed an efficient business intelligence tool that succinctly presents the company's key performance indexes and other relevant data points crucial to the organisation's management and the various business unit for better decision making.
Aspiring to expand the business further regionally with specific challenging targets, Seng Hua Hng (SHH) commissioned the SMU students to build in-house data analytics capability to help the company achieve its strategic goals. SHH previously would rely on ballpark estimates to make key strategic decisions such as to determine annual production capacity. The key challenge of utilizing ballpark estimates was the lack of accurate demand forecasting on a periodic basis and the absence of important financial information that would support the risk assessment of expansion into new foreign markets. Students also leveraged on predictive analytics to find valuable insights such as expansion plans for both domestics and overseas, new product introduction possibility and better estimation of future financial targets.
The SGX project is focused on the process of collection of data from both mainstream and social media sources, in efforts to help investors stay on top of the latest industry developments. Students devised a standard operating procedure that helped to reduce total man-hours and improve the process accuracy of SGX's systems over time.
Students recommended a suitable methodology to measure members' engagement score, using the existing data available and propose additional data points relevant to track engagement. Students also developed a predictive tool to identify the profile of their members who would be most prone to dropping out.