When it comes to tech innovations, the banking sector is usually ahead of the curve. This is especially true for Generative Artificial Intelligence (Gen AI), the deep-learning tech that can generate human-like text, images, videos, and audio. It can even synthesize data for training other AI models.
Banks are buzzing with excitement over Gen AI, and its future in the sector looks even brighter. Major banks worldwide are making it a top priority to develop and integrate GenAI solutions.
According to McKinsey, banking (along with high tech and life sciences) could see the biggest revenue impact from Gen AI, with potential added value of up to US$340B annually if all of the anticipated use cases are fully implemented.
Integrating Gen AI into banking is expected to bring about a huge shift in the banking industry. The question for banks isn’t if GenAI will have a big impact, but how. How will Gen AI change the traditional banking model?
In this post, we’ll explore how Gen AI is set to transform banking. We’ll dive into its practical applications, highlighting potential barriers, and offer some key fundamentals to focus on for its successful implementation.
Before we dive into the application of Gen AI in banking, let’s take a look at how the sector has been gradually adopting AI over the years.
The Evolution of AI in Banking
Banks have always been early adopter of AI, given their data-intensive business models.
AI in banking has evolved dramatically since the introduction of machine learning (ML) and deep learning technologies. With the advent of Python for Data Analysis and libraries like pandas in the late 2000s, ML gained traction in the banking sector. Banks and financial institutions became some of the most active users of these earlier AI technologies, paving the way for new developments in ML and related fields.
Traditional AI systems in banking primarily rely on ML to recognize patterns in historical data, identify root causes of past events, and forecast future trends. These systems use predefined rules and are trained on structured data typically stored in databases and spreadsheets.
Common use cases of traditional AI systems in banking include:
- Fraud detection
- Customer service automation
- Credit score calculations and risk assessment
- Algorithmic trading
- Market trend and customer behavior prediction
Each successive FinTech innovation has incrementally transformed key banking functions, paving the way for Gen AI to revolutionize the entire industry.
Gen AI to Reshape Banking Business Models
The advent of Gen AI in the banking industry is not just about technological evolution; it is poised to redefine the essence of banking by shaping entirely new business models. Gen AI’s impact spans all banking functions, especially in operations and decision-making.
Traditional AI has significantly improved efficiency and decision-making in banking, but it has limitations with unstructured data, natural language understanding, and complex contextual analysis. Gen AI provides state-of-the-art capabilities that can address these limitations. Compared to conventional AI, Gen AI’s large language models (LLMs) can learn patterns and structure from vast amounts of data, including unstructured inputs.
In a data-rich environment like banking, where customer interactions and routine tasks are abundant, Gen AI is set to enhance operational efficiency, customer experience, and decision-making processes.
Gen AI models like Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs) can create synthetic data to replicate the statistical properties of real-world datasets. This is crucial in contexts where real data is scarce, expensive, or sensitive. For instance, transactional data required for training anti-fraud models.
The shift towards sophisticated and creative AI models such as GPTs (Generative Pre-trained Transformers) also enables highly customized content generation. This enables banks to generate highly responsive, accurate, and personalized customer communications. At the same time, banks will be able to reduce the headcount in their customer service departments. This means that banks will be able to provide higher quality customer service at lower incremental cost, potentially providing incumbent banks with a new source of competitive advantage.
Gen AI can also significantly improve decision-making processes. By leveraging advanced predictive analytics and machine learning algorithms, banks will be able to assess credit risk, fraudulent activities, and market trends more quickly and accurately. These AI models can analyse vast amounts of data in real-time, providing actionable insights and enabling more informed, data-driven decisions. For example, AI-driven credit scoring systems can evaluate a broader range of customer data points than was traditionally possible, resulting in more accurate credit assessments. Similarly, AI can enhance the quality of investment advice offered by identifying patterns in real-time and predicting market movements.
The bottom line
The transition from traditional AI to Gen AI in banking signifies a move toward more flexible and human-like AI systems capable of understanding and generating natural language text while considering context.
This transformation will have valuable use cases in both customer service and back-office roles. Gen AI can streamline customer support by performing sentiment analysis of unstructured text data, provide personalized financial advice based on customer interactions and preferences, and automate tedious processes like report generation and compliance.
Jason Oh is a Senior Manager at TD Bank’s Enterprise Strategy team. Previously, he was at Strategy&, EY, and Novantas as a strategy consultant, where he led / contributed to a range of high-impact projects to improve both top- and bottom-line across the financial services sector.
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