How to unlock the real value of AI

In the dynamic world of financial services, the integration of artificial intelligence (AI) has ushered in an era of transformative innovation, catapulting the industry into the realms of unprecedented efficiency, accuracy and foresight. As we reflect on our journey, it’s crucial to delve into tangible examples that showcase the remarkable strides we’ve made in tackling AI challenges within the financial sector as so few people out there have actually completed production implementations. Many of these examples use technology structures that will be re-usable to solve similar problems in the future.  

Pioneering predictive analytics using generative AI with the Thought Machine core banking platform  

The financial industry’s perennial challenge of predicting market trends and making informed decisions has found a solution in the integration of AI, exemplified by our work with the Thought Machine core banking platform. By leveraging large language models (LLMs) in a secure way, we have enabled banks to offer unique services to clients to better summarise spend, see financial trends, and spot future financial challenges or savings goals. This integration has empowered institutions to stay ahead in the ever-changing landscape, turning challenges into strategic opportunities.  

Ensuring compliance through language models in banking agent chats  

Many organisations have looked at a simple customer chat agent using generative AI and applied it to an online banking situation. Most of these solutions that we have seen still fall foul of some of the key weaknesses in the technology; prompt injection and hallucinations.  

We have used these failings of early adopters to ensure what we build in production is robust and can stand up to the scrutiny of both regulators and customers alike. We too have built banking agents, but we have been able to re-use some of the patterns to be more focussed on solving specific business problems, not just aiming them as a generic ‘shop window’ for Q&A.  

Navigating the intricate terrain of regulatory compliance has become more streamlined through the implementation of language models (LLMs) in our banking agent chats. By using an LLM to highlight possible gaps in a dialogue, we’ve provided a robust basis for checking whether agent interactions comply with regulations. These models analyse natural language patterns in the conversations that a bank is having with its customers, ensuring that customer interactions adhere to the evolving regulatory standards and the right product details are supplied. As well as providing a better experience for customers, this innovation also reduces the potential fines from breaching regulations that banks fall foul of every year.   

Innovating security measures with Feedzai at a tier 1 global bank  

Security considerations have long been a key concern in the financial sector and the amount of money banks lose through fraud means any small incremental upside leads to millions in saved revenue. Through the integration of the third-party software Feedzai, we’ve built a formidable fraud detection platform, plumbed into existing core banking systems. This software employs advanced AI algorithms to analyse patterns, identify anomalies, and enhance real-time threat detection. We strongly believe that AI is a perfect solution to help banks in fortifying cybersecurity measures, safeguarding financial assets, and maintaining the trust of both institutions and their clients.  

Implementing transaction monitoring with machine learning at a global bank 

Regulators such as the FSA and the the Fed require that clients and bank employees are checked for compliance with sanctions, anti-money laundering, fraud and insider trading down to the resolution of individual transactions. Without significant automation of these processes, banks face the need to review and report on tens of thousands of clients and hundreds of thousands of employees every six months. GFT has has implemented automation into the review process at a global bank, using machine learning, reducing the review and evaluation burden by orders of magnitude. This has allowed for a significant reduction in people intensity and ensured the bank is able to meet both regulatory deadlines and headcount targets. 

This is just a small proportion of what GFT is capable of, our real-world examples showcasing the tangible impact of AI in overcoming longstanding challenges in the financial services sector. From predictive analytics and compliance-focused large language models to robust fraud detection platforms, our collaborations exemplify the transformative power of AI. As we celebrate these triumphs, we remain committed to fostering a culture of continuous innovation and collaboration, re-use of technology for efficiency, ensuring that AI not only addresses present challenges but anticipates and navigates the complexities of the future financial landscape. 

Register now for our upcoming event with Google Cloud on ‘Creating real business value from AI’ here 

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