Generative AI reshapes financial landscape

The financial sector has, for a long time, experienced numerous technological disruptions. Generative Artificial Intelligence (AI), the latest of these, can sift through massive volumes of data to create distinctive insights.

Generative AI isn’t a new concept; it has been around since the 1960s. But with the entry of Generative Adversarial Networks (GANs) in 2014, it finally dawned on us how this technology could impact the world. This machine learning model has advanced beyond just creating authentic videos and images of people. In business, it has changed the approach to accounting and finance, allowing companies to soar to new heights in data insight, efficiency, and accuracy.

For financial analysts and accountants, the impact of this technology means several things, including redundancy. But as this technology brings about transformative change, our focus should be on using it to enhance human expertise rather than replace it.

AUTOMATED DATA EXTRACTION AND ENTRY
For a long time, the financial sector has relied on manually extracting and keying in data. Apart from being time-consuming, it’s susceptible to corruption and errors. Generative AI eliminates these challenges and gives accountants the time to complete complex duties that require human perception.

FRAUD DETECTION AND PREVENTION
If there’s one aspect of generative AI that executives can really appreciate, it’s deterring fraud. Statista, a platform that provides global data and insights for businesses, forecasts that the global economy will lose over $13 trillion due to fraud by 2028. This is 69.94% more than what was reported in 2023. To protect your hard-earned investments, you need to leverage the power of AI to combat cybercrime.

The financial sector can use generative AI to monitor crucial data about transactions, such as location, devices, and technologies used. It can flag anomalies or suspicious activities that don’t conform to the patterns expected. Financial analysts can then use their wits to filter the transactions and decide if they need to be investigated further.

FINANCIAL FORECASTING
Generative AI can learn from past financial data and make forecasts, as well as generate synthetic data. This technology can easily identify existing patterns in data, run simulations, and generate forecasts using theoretical scenarios. Using this forecast, financial planners and CFOs can evaluate various possibilities and plan the financial future of a firm.

OPTIMIZING EXPENSES
Repetitive tasks that don’t add significant value to the financial model of a company cost a lot of money. By identifying and performing these tasks using process intelligence capabilities, generative AI can optimize financial processes and cut costs.

REGULATION: A THREAT TO GENERATIVE AI?
The transformative changes brought about by generative AI cut across the board. Firms continue to benefit while experts are rendered redundant. There are also apprehensions and identified threats because of generative AI’s capabilities which have far-reaching implications, such as the ability to fabricate data that is practically impossible to distinguish from real data. The result of this is a revolt against this technology. In the past, we’ve seen legislatures suggesting regulating AI to create a balance.

From a regulatory standpoint, the Asia-Pacific (APAC) was the first to implement AI regulations as other regions like the EU rally to actuate their AI regulatory framework. For instance, China has taken on the mantle of regulating AI by implementing a series of laws. Its latest move on Aug. 20, 2023 was to enact measures governing the provision of generative AI services.

In what is termed GenAI Measures, China has set up strict regulations on entities, particularly individuals, that use this technology. One of the regulations imposed by the Cyberspace Administration of China (CAC) that could impact the finance sector is algorithm filing and local security assessment. Another restrictive measure is the ban on using generative AI algorithms and data to monopolize the market. Also, service providers must disclose if any content was created by or has generative AI influence.

Other key players in APAC, such as Singapore, Japan, Australia, and South Korea, are also starting to see the need to regulate AI. Some of the key areas raised by these nations revolve around data privacy, transparency, and ethics.

While generative AI surely needs some sort of regulation, it mustn’t be too restrictive. Otherwise, it could potentially affect not just the adoption of this technology, but also people’s trust in financial firms that utilize it. The increased oversight and scrutiny seen in China could lead to stricter regulatory requirements. The effect of this could be that firms invest more resources on compliance. Subsequently, it could lead the slowing of the pace at which the finance and accounting sectors adopt the innovation.

While AI is an unstoppable force cutting across every sector, it seems like a percentage of the global populace isn’t ready for it. Owing to the fact that generative AI can do more than 95% of tasks performed by experts, the public’s faith in AI is not yet sealed. For that reason, governments should create an avenue that promotes trust in AI. With China taking the lead as other APAC nations follow in AI regulation, it’s up to individual firms to coin an angle that promotes generative AI’s use in finance and accounting.

While the future of finance and accounting will undoubtedly be fueled by generative AI, its implementation requires a thoughtful approach. Implementation can be easier and more effective if decision-makers embrace it early enough. CEOs, CFOs, and executives who welcome this technology and adjust to the landscape dynamics will be well equipped to achieve unimaginable growth.