What are the uses of artificial intelligence in the finance sector?

77% of finance departments leverage artificial intelligence. Whether it’s to expedite processes, unearth opportunities, mitigate risks, or refine decision-making, AI has rapidly emerged as a crucial asset for finance professionals.

The adoption of artificial intelligence by financial services

Embraced by more than three-quarters of finance departments, artificial intelligence (AI) and machine learning (ML) facilitate enhanced decision-making across various dimensions: financial operations, customer service, risk management, budget forecasts, recruitment enhancements, procurement optimizations, fraud detection, opportunity discovery, and more.  In essence, any decision made by financial institutions can be streamlined through artificial intelligence. More significantly, these technologies also offer the boon of conserving invaluable time on numerous labor-intensive tasks, indicating a burgeoning role for AI within the financial domain. This implication is further supported by a report from Workday revealing these statistics:

• 39% of financial executives foresee AI disrupting the financial landscape.
• 40% view AI as a vehicle for imparting greater strategic value.
• 30% believe this technology can amplify their company’s revenue and profits.
• 26% of CFOs lament over the burden and volume of administrative tasks needing completion.
• 33% are discontent with how their role is perceived within their organization.

The advantages of AI for the finance sector

Often perceived as conservative, the finance sector can be innovative by massively adopting artificial intelligence. And for good reason, this technology allows them to reap numerous benefits.

1 – Making more informed decisions

Fraud, speculation, loss of revenue, inaccurate forecasts… The finance sector is fraught with myriad risks. Every decision taken can bear significant monetary implications. Therefore, the importance of relying on trustworthy and pertinent information cannot be overstated. This necessitates comprehensive data preparation efforts. AI is adept at processing, purifying, and converting vast amounts of financial data within a remarkably brief span. Similarly, predictive analytics enable the forecasting of various scenarios based on current events. Armed with this analysis, it’s easier to sidestep risks and/or pinpoint opportunities. Financial leaders, thus, can make more educated decisions and pursue a more coherent strategy.

2 – Enhancing operational efficiency

To minimize financial risks, an array of procedures are implemented. Yet, such processes significantly elevate the workload for finance professionals. AI can alleviate this load by automating the most burdensome tasks. This is especially true in accounting, for tasks such as processing invoices and quotations. Instead of manually keying in every invoice, AI technology can take charge within seconds. The algorithm is proficient in scanning a photo of the instance, extracting critical details (supplier name, items sold, cost, etc.), and automatically logging this information into the accounting system. Learning to use data in finance Allocating some of these repetitive tasks promises substantial time savings for teams. This enables them to concentrate on their primary duties, thereby injecting even greater value into the enterprise. According to a survey by OneStream, 80% of financial leaders assert that AI will bolster the productivity and efficiency of services.

3 – Customer retention

Similar to other industries, customer retention stands as a pivotal challenge within the finance sector. However, talent scarcities exacerbate this challenge. Spread thin, customer support teams can’t always respond to client inquiries effectively, breeding certain levels of disgruntlement. Artificial intelligence can bridge these gaps, particularly through the application of generative AI in finance. For example, deploying a chatbot to field customers’ most common questions allows teams to dedicate their efforts to resolving more intricate issues, thus escalating client satisfaction.

AI and finance – 3 application examples

Current applications of artificial intelligence span virtually every facet of the finance sector. Below are a few practical instances:

• Trading algorithms: capable of analyzing time-series data to promptly react to any swings within the stock markets.
• The “innovative land” mechanism: this leverages a combination of AI, data analysis, and aerial imagery from the National Institute of Geographic and Forestry Information (IGN) for property tax purposes. Consequently, tax enforcement agencies can more effortlessly spot fraud.
• Combatting money laundering: supervised classification algorithms, trained on examples of both normal and illicit transactions, can distinguish between them to swiftly identify money laundering activities.

While AI’s influence in finance escalates, its adoption equally demands distinct technical prowess. Professionals wedded to traditional methods might find these novel technologies daunting. This underscores the need for expertise in AI, machine learning, data analysis, and visualization, etc. At Liora, we specialize in cultivating these experts.