The Evolution of Artificial Intelligence In FinTech: Generative AI vs Traditional AI

The financial services sector is undergoing a significant transformation, fuelled by the integration of AI technologies. NVIDIA’s fourth annual State of AI in Financial Services Report highlights the current landscape and emerging trends for 2024. According to the report, a remarkable 91% of financial services firms are either exploring AI or have already integrated it into their operations. These companies harness AI to drive innovation, streamline operations, and enhance customer experiences. Moreover, the financial sector’s investment in GenAI (Generative AI) is expected to grow exponentially. According to the KPMG generative AI survey report, a compound annual growth rate of 28.1% for Generative AI in finance is expected over the next decade.

Aleksei Glukhov and Evgeny Mishchenko, co-founders of the British FinTech company Payrow, shared with TechRound their insights on the role of AI in the FinTech industry, discussing the distinctions between Generative AI and Traditional AI.

 

How Does AI Influence FinTech?

 

The McKinsey Global Institute estimates that the banking sector could gain between $200 billion and $340 billion annually through GenAI adoption. This technology enhances core financial operations, such as improving risk assessment and rethinking customer engagement.

Generative AI is already impacting various areas within financial services. In operations, AI automates processes, enhances data analysis, and informs investment decisions. In risk management, it analyses vast data sets to detect suspicious activities and anomalies. In marketing, AI predicts customer preferences, enabling personalised campaigns.

Generative AI’s applications in FinTech are broad. It can personalise customer experiences by providing tailored insights and recommendations, helping users make informed decisions. In document processing, it rapidly extracts and analyses data, reducing processing times for tasks like loan applications and compliance.

AI also strengthens fraud detection with advanced pattern recognition, leading to better credit evaluations and underwriting. By automating routine tasks, AI boosts operational efficiency, cuts costs, and allows FinTech companies to focus on innovation.

Finally, GenAI drives product innovation by analysing market trends and consumer behaviour, identifying gaps, and enabling the creation of new financial products to meet evolving customer needs.

 

 

Key Differences Between Generative AI and Traditional AI Models in the FinTech Industry

 

As we explore the realm of AI further, it is crucial to recognise and differentiate its various forms. Generative AI is one of the rising trends, and it’s a branch that holds great promise for transforming industries.

 

While both Generative AI and Traditional AI play an important role in advancing the FinTech industry, they serve different purposes and offer distinct advantages. Understanding these differences is crucial for making informed decisions about which type of AI to implement in specific contexts.

 

Purpose and Functionality

 

Traditional AI models are designed primarily for pattern recognition and predictive analytics. These models are built to analyse existing data, identify trends, and make predictions or decisions based on that analysis. For example, in FinTech, Traditional AI is widely used for tasks like fraud detection, credit scoring, and customer segmentation. These models excel at interpreting historical data and making accurate predictions about future outcomes.

In contrast, Generative AI (GenAI) is designed to create new data instances that resemble the training data. For instance, GenAI can be used to create synthetic data for training models when real data is scarce or sensitive or to generate hypothetical financial scenarios for risk modelling.

 

Data Requirements

 

The data requirements for Generative AI and Traditional AI differ significantly. GenAI typically requires large volumes of data to learn the underlying distribution and generate new, plausible data points. This makes it particularly useful in scenarios where data augmentation is needed, such as creating synthetic data to train models.

On the other hand, Traditional AI can operate with smaller datasets, focusing on classification, regression, and clustering tasks to extract insights and make predictions. While Traditional AI models can still benefit from large sets of data, they are generally more flexible in terms of their requirements than GenAI.

 

Use Cases in FinTech

 

The use cases for Generative AI and Traditional AI in FinTech are also distinct. GenAI is often used in scenarios where new data needs to be created or simulated. For example, Generative AI can be used to generate synthetic data for training AI models, create personalised financial advice and products, or model hypothetical financial scenarios for risk assessment.

Traditional AI, on the other hand, is more commonly used for tasks that involve analysing existing data and making predictions based on that analysis. For instance, Traditional AI is widely used in fraud detection, where it can identify anomalous transactions and flag potential fraud. It is also used in algorithmic trading, where it can explore historical market data to make informed trading decisions, and in customer service, where it enhances chatbots and virtual assistants to provide better support.

 

Complexity and Interpretability

 

Generative AI models are often more complex and harder to interpret than those built for Traditional AI. This is because GenAI involves creating new data, which requires sophisticated models that may not be easily explainable. For example, Generative Adversarial Networks (GANs) involve a dynamic interplay between two neural networks, which can make it challenging to understand how the final output is generated.

Traditional AI models, on the other hand, are generally easier to interpret, especially those based on decision trees or linear regression. They provide clear rules and relationships that can be easily understood and explained, making them more transparent and interpretable than GenAI models.

 

Technological Advancements

 

Finally, Generative AI represents a newer wave of AI technology, leveraging advancements in deep learning and neural networks. These advancements have enabled the creation of more sophisticated AI algorithms that can generate highly realistic outputs, such as text, images, and financial models. As a result, GenAI is at the cutting edge of AI research and development.

In contrast, Traditional AI utilises well-established methods that have been refined over decades. These methods have a strong foundation in statistical learning and are widely used across various industries, including FinTech. While Traditional AI may not be as cutting-edge as GenAI, it is still a powerful tool for analysing data and making predictions, and it remains a key component of many AI-driven systems in FinTech.

 

Challenges and Risks of GenAI in Finance

 

Despite the many advantages of Generative AI (GenAI) in the finance sector, its implementation brings significant challenges and risks. Below are some of the main obstacles to adopting GenAI in finance.

 

Data Privacy and Security

 

The use of GenAI requires extensive data access, raising risks of breaches and misuse. As highlighted by the 2024 Cisco Data Privacy Benchmark Study, 27% of companies have prohibited the use of GenAI due to concerns over data privacy and security. Given the presence of sensitive financial and personal information, ensuring robust data protection is essential.

 

Data Cutoff Limitations

 

AI models may rely on outdated information, leading to inaccurate responses. Regular updates to the data sources used by RAG systems can help keep AI outputs remain current and reliable.

 

AI Hallucinations

 

GenAI can generate incorrect or nonsensical responses. To improve accuracy, companies can implement retrieval-augmented generation (RAG) systems and incorporate human review processes.

 

Regulatory Compliance

 

Noncompliance with evolving regulations in finance can lead to legal penalties. Integrating GenAI into workflows where outputs are reviewed by employees can help ensure adherence to regulatory standards.

 

Bias and Fairness

 

GenAI models may unintentionally perpetuate biases, leading to unfair outcomes. Implementing rigorous testing and refining models can reduce the risk of embedded prejudices in AI outputs.

 

Understanding the Potential of GenAI in Finance and How to Get Started

 

To successfully adopt Generative AI in financial services, it’s essential to approach the process with caution and strategic planning. Start by ensuring accurate data collection and governance to build a solid foundation for AI integration. Educate yourself and your team on GenAI’s capabilities, limitations, and risks, and align these initiatives with your business objectives. Begin with a low-risk pilot project to demonstrate the potential benefits, then scale up gradually, addressing additional strategic use cases as you refine and optimise AI applications across different areas of your operations.

While GenAI offers significant potential for innovation and efficiency, it’s crucial to carefully assess and mitigate any risks. If doubts remain, see them as an opportunity for deeper exploration rather than a reason to abandon the idea. With the right approach, GenAI can become a powerful tool in driving your financial services forward.