What are the major threats and opportunities of generative AI in payments?

Employing advanced Machine Learning (ML) techniques, Generative AI can generate content that copies human creativity, learns from examples, and produces novel outputs based on these learnings. The roots of Generative AI can be traced back to the 1960s, but is only in the past decade that its significance has grown rapidly. 

A pivotal moment came in 2014 with the introduction of the Generative Adversarial Network (GAN), capable of creating highly realistic images, videos, and human voices. This innovation quickly led to the recognition of Generative AI as a game-changing tool across various industries, including the payments industry. Financial institutions within the payments sector can leverage Generative AI for a range of purposes, such as customer acquisition, engagement, risk profiling, and overall operational enhancement. However, the use of generative AI in payments doesn’t come without a few challenges.

Probable threats from generative AI to payments

Here are the major threats that generative AI poses to payments:

• Cyber threats – New digital technology also means new cyber threats. The potential vulnerabilities of a new technology cannot be all known on the day one. This would be true in the case of generative AI algorithms and systems as well. Cybercriminals may exploit such potential vulnerabilities before they are addressed by the users. In payments, such attempts can result in lead to endangerment of payment data integrity and privacy. To mitigate cyber-attacks, payment companies must maintain a strong authentication mechanism, and follow rigorous testing with regular updates.
• Privacy concerns – Implementing certain generative AI tools may require personal data, while others can be trained using synthetic data or anonymised datasets. Some of this data may include sensitive information, making storage and access a consistent privacy concern for all tech-driven systems, not just generative AI. To safeguard against data breaches and unauthorised access to sensitive information, strong encryption and access controls are essential.
• Reliability of output – The output produced by generative AI is frequently synthetic, and its accuracy compared to real data may not be fully known, especially during the early stages of its development. Payment companies must verify that the generative AI model is delivering reliable performance. To address concerns about reliability, they should regularly test the accuracy of the synthetic data.
• Model integrity – Payment companies will also need to monitor and audit the outcomes of their generative AI models. These models rely on learning implementation, and any inherent bias in models can lead to inaccurate outcomes. This, in turn, would affect the decision-making process and customer experience.

How can the payments industry benefit from generative AI? 

While payment companies explore the probable threats of generative AI and address them, they are also aware that it can immensely benefit their business and service quality. Here are the opportunities that generative AI presents:

• Process improvements – The use of generative AI algorithms can streamline processes within a typical payment company, whether it's managing invoices or handling reconciliation. Improved automation reduces human intervention points, leading to more accurate results. By optimising these processes, generative AI can also cut down processing time and costs.
• Superior chatbots – While AI chatbots rely on built-in logic and databases to assist users, generative AI leverages machine learning and natural language processing (NLP) to offer personalised support. This personalised assistance results in better query resolution than format-driven AI chatbots. The resolution might involve a more customised product recommendation, a service aligned with customer preferences, or a comprehensive solution to a customer's issue. 
• Structured data handling – Generative AI can help payment companies get their data organised and sense-checked in large volumes, at a fast speed and with accuracy. Improvement in data quality would contribute to better insights and accurate information, leading to better forecasting, planning, and decisions.
• Fraud prevention and detection – Generative AI carries out big data analysis in real-time. It uses ML capabilities to sift through massive data and identify potential threats and frauds. It is expected that generative AI models are more efficient in identifying the deviations from the norm, and red flagging them for prompt action. 
• Data protection – Generative AI is privy to sensitive data in the payments industry. To insulate privacy around such data, generative AI should be used as a tool to add efficiency to backend ops, while ensuring a larger framework of oversight and management control around it. The presence of internal data protection and information security policies regulatory compliance frameworks, and a robust enterprise risk management architecture will be helpful in this regard.

Join the next big leap

Generative AI can offer purchase recommendations to customers, and proceed with risk-appropriated autonomous payments, which ends up saving customers’ time and effort. Adopting these types of generative AI services is the next clarion call for businesses of all sizes.

Worldline is at the forefront of innovations in the payment industry. Find out how Worldline India can be the ideal payment solution provider for your business, explore the versatile Worldline service suite today.