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The Impact of AI and Machine Learning on Finance Hiring

Impact of AI and Machine Learning on Finance Hiring

Artificial intelligence (AI) is revolutionizing every industry and affecting the job market. The integration of data, algorithms, and use cases is driving the development of AI, which has resulted in reduced positions, increased work efficiency, and changes to the nature of work. However, the impact of AI and machine learning on finance hiring process has also created new jobs and opportunities.

Although AI has the potential to benefit the job market in the future, there are concerns about its impact on human workers and the possibility of replacing them. Specifically, AI technology will transform the business world in three ways: automation, intelligence, and creation. In the financial sector, for example, AI may lead to the redundancy of specific jobs while simultaneously increasing efficiency and creating new job opportunities.

Opportunities and Challenges in the Emerging AI Landscape

The advancements in AI technology bring about opportunities and challenges that must be acknowledged and dealt with. On the one hand, AI has the potential to increase automation, support intelligent analysis and decision-making, and create new business models and industries. On the other hand, AI carries significant risks that need to be carefully managed.

For instance, the financial industry has potential micro-financial and macro-financial stability risks. The former can destabilize markets and cause turmoil, while the latter can trigger risks associated with market concentration, loopholes, connectivity, and technology.

Machine vision and speech recognition provide machines with cognitive skills, allowing AI to be applied in real-world contexts and transforming all aspects of society.

AI improves workflows by processing structured and unstructured data, including language and image information, to deliver new products and services and provide data or physical feedback. This has the potential to enhance productivity and efficiency across multiple sectors significantly, but it also requires careful management of potential risks. Industry users typically understand AI from three dimensions: data, processes, and actions.

Rebuilding the Value Chain of the Financial Industry through Automation, Intelligence, and Innovation

The financial industry is transforming due to automation, intelligence, and innovation advancements. The proliferation of big data and deep learning has ushered in a new era of AI that is revolutionizing various application-layer technologies, leading to fundamental changes in how businesses operate.

The impact of this change is threefold: First, there is a growing trend towards increasing automation as financial institutions seek to streamline their operations and reduce costs. Second, AI-powered intelligent analysis and decision-making tools are being developed to help financial professionals make more informed decisions and provide better client insights. Finally, this transformation is creating new business models and industries, paving the way for innovative financial products and services that were previously unimaginable.

Impact of AI and Machine Learning on Finance Hiring: Replacing, Enhancing, and Generating Opportunities

Based on the applications of AI in different business value chains of the financial sector, we observe three significant impacts that AI has on the financial job market: job cuts, increased efficiency, and job creation. While job cuts and increased efficiency will impact existing positions, job creation reflects the potential incremental impact of AI on the job market.

The distinction between job cuts and increased efficiency is based on whether the manual work being replaced by AI is considered the core value-creating activity of the position. If AI can perform the core value-creation activity of a job post instead of manual work, then the situation can be eliminated due to AI. On the other hand, if AI is only playing an auxiliary role in completing non-core tasks and cannot replace the core value-creation activity of a position, then this is considered an increase in efficiency.

According to projections, by 2027, 23% of jobs in China’s financial sector will either be eliminated by AI or transformed into new roles. The remaining 77% of jobs will not be replaced, but their efficiency will increase with the integration of AI. AI is expected to return jobs that involve routine and repetitive tasks. In the financial sector, around 2.3 million employees, 23% of the workforce, are projected to be affected by these changes. However, the remaining 7.6 million people, who are involved in tasks that require problem-solving skills, emotional intelligence, or adaptability to changing environments, will not be replaced by AI. Instead, they can leverage AI to improve their work efficiency and effectiveness.

Transformations in Employment Structure in the AI Era

The impact of AI on the job market and talent requirements has been more pronounced in developed countries and leading companies. Rapid technological progress and automation have led to a decline in manual and cognitive tasks in the US, particularly in the production line, operations workers, and office clerks. However, the demand for professional service talent focusing on communication, logic, creativity, and technical skills has continued to grow.

Similar changes in employment demand and talent requirements have been observed in China. While AI technology is still in its early stages in China, posts relying on creativity and communication will continue to grow. Presently, changes in employment demand in AI are mainly reflected in the market for essential technical talent. Companies are placing greater emphasis on technical skills, particularly in basic AI.

As AI technology evolves, the demand for talent is also changing. Computer software is gradually taking over manual labor in tasks that involve repetitive and highly standardized activities, thanks to the advancements in AI technology.

This is resulting in a decline in demand for these positions. However, there will be an increased demand for talents skilled in AI technologies, both on a technical and application level, with solid and soft skills. The development of AI will amplify the need for creativity, emotional communication, and complex problem-solving.

In summary, while the rise of AI may lead to job displacement, it also opens doors for emerging talents who can create, apply, and optimize AI technologies. The demand for workers with strong communication abilities who can use their high-level technical achievements in production and daily life will continue to grow.

Impact of AI in Finance Sector

The application of AI in the financial sector is changing the way companies provide services to their customers. Banking companies can now offer round-the-clock financial services and assistance using robotics and chatbots. These technologies can go beyond just accounting money and facilitating payments. They can also be utilized for customer support, addressing customer grievances, and providing investment advice based on diversified investment strategies that suit customers’ constraints and resources.

AI has also become a valuable tool for insurance companies. It allows them to solve unstructured problems related to insurance holders’ behavior, investment patterns, lock-in periods, maturity periods, and growth. It helps in the risk assessment of investment portfolios, making the process more efficient. E-commerce companies like Amazon, Flipkart, and eBay use AI to update their stocks quickly and cater to consumer preferences based on previous behavior patterns. However, this raises concerns about consumer privacy and data protection.

AI and machine intelligence systems also play a significant role in stock exchanges by collecting and processing data and providing grievance solutions. This increases the speed and efficiency of these systems. On the other hand, financial consultancy is an area where AI has yet to take over entirely. This is because every consultancy involves customized and semi-structured problems that human expertise can only solve. These tasks may be mundane, but they require the unique problem-solving abilities of human brains.

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