The big data phenomenon is reshaping how businesses operate and manage their data. The application of big data analytics has allowed organizations to gain new insights and make better decisions, which has helped them achieve better results. The accounting industry is no exception and is starting to reap the benefits of big data. This article will explore the trends and future of big data in accounting.
Big data is not a new concept and has been around for some time now. However, its application in accounting is still relatively new. The first use of big data in accounting was in the 1990s, when banks started using it to prevent money laundering (Gantz, 2013). Since then, big data has grown in popularity, and now organizations in different industries are reaping the benefits.
The development of big data analytics has played a significant role in its success. With the advancement of big data technology, businesses can now process and analyze large volumes of data more efficiently. This has allowed them to identify patterns and trends that would otherwise be undetectable.
According to a study by IDC, the big data market is expected to grow from $130 billion in 2016 to $203 billion by 2020. Experts attribute this growth to the increasing demand for big data solutions by businesses of all sizes. The following are some statistics on the development of big data in accounting:
We can divide the current role of big data in accounting into three main categories: prevention, detection, and correction.
Businesses are using big data to prevent financial crimes such as money laundering, fraud, and embezzlement. By identifying patterns and trends in the data, companies can detect these crimes before they happen. For example, the estimated amount of money laundered globally in one year is 2 – 5% of global GDP, or $800 billion – $2 trillion in current US dollars.
Money laundering is detrimental to many businesses. As a result, companies need to invest a massive sum of money in anti-money laundering software to counter money laundering. By 2025, experts project that the anti-money laundering software market will reach $2.77 billion.
In addition, most countries have enacted laws with harsh penalties and fines for businesses found guilty of money laundering. From 2008 to 2017, financial institutions lost about $321 billion globally through penalties for being non-compliant with standardized regulations, assuaging money laundering, funding terrorism, and manipulating the market.
Big data has been crucial in anti-money laundering compliance for financial institutions. It has enabled them to profile clients and transactions better to identify high-risk clients. In addition, big data analytics gives accountants real-time information. Therefore, they can deal with any threat before the authorities know about the transactions.
Another issue with anti-money laundering is false positives. This is where financial institutions falsely identify an innocent client as a high-risk client. The alerts created by most AML (Anti Money Laundering) programs are usually up to 90% false positives, which costs companies a great deal of time and money. Big data in accounting has improved the quality of data that companies analyze. Therefore, it has reduced the chances of false positives.
Companies are also using big data in accounting to detect financial irregularities such as accounting fraud. By analyzing large volumes of data, businesses can identify discrepancies and unusual activity that may indicate fraud. Unfortunately, fraud in companies remains at an alarmingly high rate. Typical business fraud schemes include inventory theft, payroll, financial statement, wire transfer, and check tampering. According to the Association of Certified Fraud Examiners, organizations lose five percent of revenue to fraud each year.
In addition, big data analytics identifies outliers in patterns and trends. These outliers are a signal that something may be wrong somewhere. For example, fraudulent employees may wire certain transactions to a specific bank account that standard accounting procedures cannot detect each month. Big data analytics, through data mining, can identify this trend as an outlier and signal the authorities.
Once companies use big data to detect financial irregularities, businesses use it to correct them. This involves investigating the root cause of the problem and taking corrective action. The disciplinary action may be seeking reimbursement for money lost, termination of employment, and court action. In addition, businesses protect themselves from future losses.
The trends and future of big data in accounting are promising. Here are some of the key trends that we are seeing:
The use of big data analytics will continue to grow: As businesses become more aware of the benefits of big data, the use of big data analytics will continue to grow.
The use of big data will become more widespread. Big data is no longer limited to large enterprises. Instead, small and medium-sized businesses are starting to adopt big data analytics to gain a competitive advantage.
The use of big data will become more complex. As the volume and variety of data increases, so will the complexity of big data analytics. As a result, businesses will need to invest in skilled professionals capable of handling big data.
The use of big data will become more automated. With the advancement of artificial intelligence (AI), businesses will be able to automate analyzing big data, which will speed up the decision-making process.
The use of big data will become more global. As businesses expand into new markets, they will need to adopt big data analytics to understand the local culture and customs.
The use of big data in accounting is growing rapidly and shows no signs of slowing down. As a result, businesses of all sizes are starting to adopt big data analytics to gain a competitive advantage.
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