Artificial intelligence, machine learning, cognitive systems, and data analytics are all influencing auditing practices.

The rate of change is extraordinary, and for many, it's easy to imagine a future audit that is entirely automated and free of human intervention. We don't have to wait for tomorrow's new technology to start revolutionizing audits. In fact, it is accessible right now and can begin pushing you ahead, adding value, and assisting you in achieving the objectives that many people believe the future of auditing is all about.

Big Data

Big Data refers to massive amounts of data that can't be handled properly using ordinary software. The amount of data generated on a daily basis is expanding dramatically as a result of increasing use of mobile device and the internet of things.

A complete population of transactions is evaluated using a data analysis tool. It can process the details of millions of items in seconds and then show the information by supplier, transaction date, or amount. This enhances the likelihood of an auditor discovering questionable objects or trends, while also saving time and effort.

Robotic Process Automation (RPA)

Robotic Process Automation (RPA) can be used to automate rule-based tasks with well-defined rules and parameters, such as journal posting, ratio calculation, voucher documentation, and confirmation, so that routine tasks can be completed 24 hours a day, seven days a week without sacrificing efficiency or human involvement. EY, for example, has started utilizing bots to handle low-level activities like delivering bank confirmations and locating out-of-balance journal entries.

Artificial Intelligence (AI)

AI allows robots to do cognitive activities such as problem-solving and identification in the same way that humans do. Natural language, speech, and gesture detection and processing are among the features. With a reduced margin for error and increased efficiency, auditors may utilize the statistics to assess financial information, analyze financial ratios, and discover trends.

AI, like RPA, can be utilized to conduct everyday tasks as well as perform analytical functions. For example, AI can scan through 100 pages of contracts in seconds, extracting crucial information and producing a summary based on its understanding of various legal terms and phrases, which would take a human auditor hour to do.

Machine Learning & Deep Learning

Machine learning is a branch of artificial intelligence that involves algorithms that interpret data, learn from it, and then use what they've learned to make informed judgments. Machine Learning can be taught to detect patterns in large amounts of unstructured data, such as social media posts, emails, and conference call records. The computer will "learn" the problems and apply what it has learnt to the next collection of data. The result is then fed and supplied back to the computer the way to respond to similar situations in the future once the audit engagement team has decided on the irregularity. The more the "learning process" is used, the better the machine will be at detecting true abnormalities. Deep learning is a kind of machine learning in which algorithms are organized in layers to construct an "artificial neural network" that can learn and make decisions on its own. Deep learning differs from machine learning in that it can analyze a bigger quantity of data at once and use neural networks as a learning method, which enhances relationship complicity and data layers.

Blockchain Technologies

Every transaction involving value, like as money, products, property, employment, and even votes, is recorded on the blockchain, which is an open and decentralized database. Batches (blocks) of data are saved and connected in a chronological manner to make a continuous line (chain). When the blockchain is edited, a record is left behind, and the whole community must verify it. Blockchain operates as a worldwide decentralized source of credibility since every transaction will be recorded on a public and distributed ledger. In other words, the level of security is good enough that financial data can be stored on it.

PwC and EY brought in blockchain auditing into their auditing practices to improve their capacity to conduct in-depth reviews of bitcoin business transactions as well as validate data in blockchain.

Accounting and auditing both need a great deal of judgment. Machines are doing a lot of data scanning and data entry, but we're still a long way from being able to talk to "Jarvis" and have him pass accounting entries without human interaction. Data must still be prepared in a standardized manner and uploaded by a human.