Journey of the Big Four: Accounting Firms Join the Cloud, Analytics & AI Bandwagon (Part Two)
Written by Malavika Rathore
During the early 2000’s, global corporations began to play around technologies which are now the founding pillars of innovation, i.e. data storage, cloud, and data analytics. These technologies will remain a part of the firms’ business model even in the coming future. Accounting firms are leveraging emerging technologies for their own and clients’ data for audits, risk management, fraud, loss prevention, to improve accuracy, error correction, and prevention, etc.
Traditional auditing processes involved the professionals analyzing audit papers and client documents in their office. With the help of cloud technology and automated workplace services, the concept of virtual offices was born. The paperwork was transferred and made available online and could be accessed from anyplace or device and could be worked on by multiple people simultaneously.
In the late 1990’s and early 21st century, artificial intelligence (AI) had started entering the corporate world. AI helped firms to extract data from documents using natural language processing. For instance, in 2016, KPMG revealed that its consultants will use IBM’s Watson to analyze financial data to detect anomalies. Deloitte deployed a machine-learning software and contract analysis tool, Kira. Since searching for keywords and patterns can consume considerable time and resources, by leveraging partner Kira Systems, Deloitte wanted to help organizations reduce their review time and redeploy talent for more strategic requirements.
How Are The New Technologies Being Used?
AI and machine learning, cloud, and big data and analytics help in providing insights to make informed decisions and achieve operational efficiency. Some of the uses are:
- Cloud/online storage of data helps in accessing the information at one place, which helps in reaching more efficient conclusions and trends. For instance, in 2014, KPMG and Xero Limited launched cloud-based accounting and tax services such as accounts, bookkeeping, payroll, VAT (value added tax), and corporate tax for small and medium-sized enterprises.
- When it comes to data security, observing the variation in codes can help to predict malware in files with increased accuracy. Based on how data in the cloud is accessed and used, machine learning algorithms are able to look for patterns and detect anomalies to thereby predict and prevent security breaches.
- Machine learning and statistical analysis understand how a business categorizes its invoices, based on which transactions are coded in accordance with accounts codes.
- For contracts review, systems leveraging AI can help reduce the task time from hours to seconds.
- Big data analytics helps by using the data collected from all the past audits and all the current financial, transactional, and operational data, to arrive at some key observations. What were the key focus points in past audits and what has changed now and do those old methods still work? If transactions are complying with standards or not can be easily identified.
- Changes such as financial and operating policies or mergers acquisitions can be noted down. This involves deploying a data mining solution inside the client’s accounting and operational systems, to send the findings to the auditors. This helps to monitor the complete financial data frequently instead of manually auditing samples (verification of account entries is done using sampling) once-a-year. The areas with higher risks of misreporting and fraud can be identified and focused on.
- Also, machine learning can be applied to entire databases, eliminating the need for sampling or test of controls. In the tax department, it can be used to interpret changes to the code and case law, and then identify clients that will be impacted by the changes.
- For analysis of unstructured data such as emails or contracts to correlate or validate audit findings, big data-based advanced text analytics is used to increase coverage of the documents analyzed and to reduce time by only flagging the anomalies for the human experts to probe further.
Challenges and Analysis
- The drawback of big data analytics is accessing all the data which can become overwhelming to make the system comprehensive.
- Besides the successfully implemented methods, due to the ambiguity surrounding the complete use of AI, accounting companies are of the opinion that they will follow how AI continues to develop in near future. Once they know what kind of data can be collected, they can arrive at a conclusion as to how to interpret it for their clients and their auditing needs as well.
- Automation and technology will create more time for professionals to focus on judgment-related areas. Rather than being replaced by machines, accountants will work with them to help clients figure out the constantly changing tech market to best leverage the opportunities. The job roles will change from a focus on hindsight to helping clients reach decisions and predict outcomes with increased precision. However, in the long term, professionals would be expected to bring some ROI (return on investment) to the company based on productivity since some areas are being replaced with automation. Auditors deal with a lot of gray areas such as evaluation of assets and calculation of tax liabilities. The current AI does not have those capabilities.
- How can companies execute their technology strategy is by having a clear vision, developing a plan, and training the employees with the required skillsets for improved advisory services. The companies explore emerging technologies by also involving clients and requesting them to share their system information to better produce solutions that could be integrated into the client’s infrastructure. The firms believe that positive participation from clients could help speed track the innovation process.
Accounting firms can be expected to continue to invest some portion of their top-line revenue in information technology as emerging technologies are becoming more and more part of our corporate lives. For instance, PwC has a CSR initiative wherein it provides training to students in the data analytics field. In FY 2017, Deloitte generated 23% of its revenues from innovative offerings and expects that number to increase to 30% by 2020. KPMG is continuing with a multi-year global investment program and in FY 2017 invested more than US$1 billion in new services, technology, alliances and acquisitions, focused particularly on cybersecurity, digital labor, and audit.
Part three of the blog to be released soon will discuss the impact of Blockchain technology on the Big Four.
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