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New ways the financial sector is using AI in 2024

18 January, 2024 ·AI thought leadership
New ways the financial sector is using AI in 2024
Discover the different ways that AI is being used in the financial sector in 2024 and how it can boost existing financial applications.

From early machine learning to today's advanced neural networks, AI has been a game-changer in shaping financial strategies and operations. Its roles in predictive analysis, risk management, and enhancing customer experiences highlight its lasting impact. The financial world has been quietly revolutionizing with AI for years, and it's not slowing down in 2024. 

Here are some of the main ways this industry will progress in the coming year.

New uses of AI in the financial world

As we dive into 2024, NVIDIA's survey of nearly 500 financial professionals reveals three key areas where AI is set to make waves: cutting costs, improving customer relationships, and addressing talent shortages. Let's break down these transformative uses of AI in finance.

1. Cutting costs and adding value

AI isn't just a fancy tool; it's a cost-saver. A significant portion of survey respondents acknowledged AI's role in boosting operational efficiencies and reducing ownership costs by up to 20% in 2023. 

These cost reductions primarily come from enhanced computing power. Traditional computing can't keep up with AI demands, but innovative tech solutions like NVIDIA's new GPU and software library are making AI more efficient and affordable. 

AI can also cut costs through improved workload portability. Next-gen hybrid cloud infrastructures introduce automation into processes, eliminating redundancies and reducing errors from managing spreadsheets. Recent research suggests embedding automation systems can reduce costs by up to $10 per invoice. This represents savings of 66% compared to manual AP processing. 

2. Enhancing customer relationships

Almost half of the surveyed professionals noticed improved customer experience thanks to AI. With advanced language models, chatbots are becoming more effective, mirroring the seamless experience customers get in retail e-commerce. 

AI's ability to sift through vast amounts of data helps financial institutions uncover patterns and insights, ultimately delivering more value to their customers. In particular, AI can extract insights from unstructured data to enable financial institutions to provide hyper-personalized customer service. In a recent survey of U.S. retail banking satisfaction, 78% of respondents would continue using their bank if they received personalized support. 

3. Tackling talent shortages 

The labor shortage in finance is real. Over 84% of Chief Financial Officers (CFOs) in the U.S. and U.K. admit to struggling with the scarcity of skilled professionals in their finance departments. 

AI can offer a solution. Leveraging machine learning as a personalized teacher for employee training and upskilling can bridge gaps and attract talent. This approach can tackle current labor challenges and prepare the workforce for future requirements in the financial industry. 

To that end, 80% of CFOs say they’re ready to spend more on AI over the next two years, according to Gartner. 

AI will enhance existing financial applications in 2024

The demand for AI grew by over 500% in 2023, which is unsurprising following its potential for delivering more personalized experiences With its ability to automate vast amounts of data processing, AI enhances data-driven decision making, which is crucial in a dynamic environment. 

This feature is transforming a number of real-world applications in finance, including: 

Credit scoring and underwriting

AI is transforming credit scoring and underwriting. Machine learning algorithms allow financial institutions to automate or semi-automate some processes by pulling together data sources and coming up with a[credit score. Experian and FICO already use AI-powered systems to incorporate alternative data sources for more comprehensive creditworthiness assessments.

Risk management and fraud detection 

AI-powered fraud detection systems leveraging machine learning algorithms can better detect highly complex fraudulent activities that traditional systems may miss. This helps institutions improve risk assessment models and fraud detection capabilities. For example, Mastercard utilizes AI deep learning models to monitor and analyze 75 billion transactions across 45 million locations yearly for fraudulent activities and false transactions. 

Predictive modeling

Predictive analytics uses AI algorithms and statistical models to evaluate historical and real-time data to forecast future events or behavior. For example, investors can automate data aggregation from a wide variety of sources and ask the AI to make sense of all the information. They then use the output to predict stock market trends, potential credit risks, market sentiment, and the like. 

Biometric payments

The rise of Internet of Things (IoT) devices is bringing about a future where wearables and biometric transactions will become more than a trend. Payment friction remains a key challenge in finance. AI-powered wearables can facilitate digital biometric identities to drive seamless and engaging transactional experiences at scale. 

Regulatory compliance

AI aids financial institutions in ensuring regulatory compliance. Advanced algorithms help automate compliance processes, reducing the risk of errors and ensuring that institutions adhere to evolving regulatory standards on artificial intelligence. Europe has taken the lead globally on AI governance with its EU AI Act released in December 2023. 

AI and financial ethics

AI in finance isn't just about crunching numbers; it's also about upholding ethical standards. As algorithms increasingly drive decision-making, there is a growing need to ensure that AI systems are developed and deployed ethically. 

Since these models are trained on a variety of input data, there’s potential for bias in output when the initial training data is biased. This raises concerns about the accuracy and reliability of AI-enabled recommendations. Transparent algorithms are crucial, allowing stakeholders to understand decisions and detect potential biases.

The ethical use of customer data also demands paramount attention. AI in finance processes vast amounts of sensitive information, resulting in increased unease over data privacy and security. Financial institutions must therefore invest in robust cybersecurity measures and comply with data protection regulations in 2024. 

New era of AI for finance in 2024

AI will be at the forefront of innovation in the financial industry in 2024. From reducing costs and reshaping customer interactions to creating new revenue opportunities, its potential is vast and exciting. Every use case of AI in finance brings something thrilling to the table, marking the beginning of a new era in financial services.

As the year unfolds, organizations must continue to stay informed and adapt to the evolving AI  landscape. This is the only way they can meet the changing demands of customers and position themselves for success in the dynamic world of finance.

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