AI powers the new era of financial services in 2024

The financial services sector is undergoing a transformative shift, largely driven by the integration of artificial intelligence (AI).

The NVIDIA "State of AI in Financial Services: 2024 Trends" report based on the response from 400 global financial services professionals, provides an in-depth look into how AI is reshaping the financial industry, backed by substantial data and real-world applications.

In 2024, AI adoption in financial services has reached new heights, with banks, asset managers, and fintech companies deploying AI to enhance customer experiences, streamline operations, and improve decision-making processes.

According to the survey, 43% are using generative AI, while 46% are using large language models (LLMs) in their organisations. This shows that AI technologies are becoming integral across various facets of financial services, including customer service, risk management, investment strategies, and regulatory compliance.

AI's impact on customer experience and personalisation is particularly notable. AI powered chatbots and virtual assistants are now commonplace, providing personalized and immediate responses to customer inquiries.

These tools use natural language processing (NLP) to understand and address customer needs effectively, with 47% of firms currently utilising NLP technology. Statistics from the report reveals that generative AI now handles 48% of operations in financial service firms, resulting in a 43% improvement in operational efficiency.

This has significantly reduced response times and enhanced customer experience by 27%. Additionally, 34% of financial firms are exploring the use of generative AI and large language models (LLMs) for improving customer experience and engagement.

AI data processing, used by 57% of financial organisations, employs retrieval-augmented generation (RAG) and customised large language models (LLMs) to gather information from internal and external knowledge bases.

This approach enables the creation of assistants that have access to the latest industry and company-specific information, allowing them to produce more accurate results. Chat GPT is a perfect example of a customised LLM model, demonstrating that generative AI is rapidly becoming mainstream.

Furthermore, 55% of respondents in the survey are exploring the use of generative AI in addition to those who already employ the technology.

Risk management and fraud detection have also benefited immensely from AI, as 45% of responders use AI solutions in risk and compliance. Machine learning algorithms analyse vast amounts of transaction data to identify and mitigate fraudulent and anti-money laundering (AML) activities in real-time.

The report stated that 69% of financial institutions are utilising AI powered data analytics to detect anomalies in investments, loans, and other financial instruments.

This accelerated data analysis and AI models enables the efficient identification of subtle and sophisticated fraud patterns by flagging unusual transaction amounts or patterns that need to be investigated by staffs.

In the realm of investment strategies and portfolio management, AI is revolutionising traditional approaches. 29% of firms use AI for portfolio optimisation, while 27% apply it to algorithmic trading.

Additionally, asset managers optimise portfolios by analysing market conditions, tracking portfolio performance, and identifying risks and opportunities.

They also use it to personalise investment and financial plan recommendations. AI powered robo-advisors offer data driven investment strategies and automated portfolio rebalancing, making sophisticated investment management accessible to a broader audience. For insights into AI robo-advisors or personalise investments, click here.

The synthetic data created with generative AI is use predictive analytics to forecast market trends and optimise investment portfolios.

Financial institutions use machine learning to enhance algorithmic trading by analysing market data and recognising patterns in real-time. Generative AI extracts insights from financial filings, earnings call transcripts, and sentiment analysis to inform investment decisions.

34%of financial firms use AI for marketing, while 27% utilise it for sales. Generative AI analyses customer demographics, transaction history, and behaviour patterns to create personalised marketing campaigns, new strategies, and sales initiatives.

Marketing departments use AI to craft personalised recommendations, targeted ads, and tailored campaigns, while sales teams benefit from AI enhanced lead generation, customer relationship management, and sales forecasting.

Report generation is another significant area, with 37% of firms exploring AI for this purpose. Generative AI swiftly creates documents like risk assessments, fraud detection reports, and investment performance reports.

Having real-time reports readily available allows finance professionals to make quick, informed decisions, streamlining operations.

Enhanced data security is also critical, with financial institutions investing heavily in AI driven cybersecurity measures to protect sensitive information from breaches and cyberattacks.

The report discloses that 51% of fraud detection and 32% of credential/identity attacks on financial firms are addressed by AI-based cybersecurity solutions, significantly enhancing their security posture​​.

Looking forward, the outlook for AI in financial services is bright, with 51% of respondents "strongly agreeing" that AI will play a crucial role in their company's future success.

More sophisticated AI models are expected to emerge as the support for continued investment in AI projects grows. However, there are some challenges in achieving AI goals, and data issues and recruitment are the biggest concerns.

38% of financial organisations encounter data-related challenges, including privacy concerns, sovereignty issues, and managing data dispersed globally under various regulatory frameworks.

Regarding recruitment, 32% find it difficult to recruit and retain AI experts and data scientists. Insufficient budgets and inadequate data volumes for model training and accuracy are some of the other challenges.

In conclusion, the report highlights the transformative impact of AI on the financial industry. By embracing AI technologies, financial institutions can enhance efficiency, improve customer experiences, and drive innovation.

However, addressing challenges such as data privacy, skills gaps, and technology integration is crucial for maximising the benefits of AI. The future of AI in financial services is bright, with endless possibilities for growth and advancement.