Large Language Models such as GPTs (Generative Pre-trained Transformers) exhibit remarkable capabilities across a broad spectrum of applications. Nevertheless, due to their intrinsic complexity, these models present substantial challenges in interpreting their internal decision-making processes. This lack of transparency poses critical challenges when it comes to their adaptation by financial institutions, where concerns and accountability regarding bias, fairness, and reliability are of paramount importance. Mechanistic interpretability aims at reverse engineering complex AI models such as transformers. In this paper, we are pioneering the use of mechanistic interpretability to shed some light on the inner workings of large language models for use in financial services applications. We offer several examples of how algorithmic tasks can be designed for compliance monitoring purposes. In particular, we investigate GPT-2 Small's attention pattern when prompted to identify potential violation of Fair Lending laws. Using direct logit attribution, we study the contributions of each layer and its corresponding attention heads to the logit difference in the residual stream. Finally, we design clean and corrupted prompts and use activation patching as a causal intervention method to localize our task completion components further. We observe that the (positive) heads $10.2$ (head $2$, layer $10$), $10.7$, and $11.3$, as well as the (negative) heads $9.6$ and $10.6$ play a significant role in the task completion.

As the CTO of a major financial institution, it is crucial to stay informed about the latest trends in data and AI in the financial services industry in order to prepare for the future and remain competitive. While there are many vendor platforms and systems available on the market to help decision-makers solve their challenges initially, the true value varies based on your organization's readiness to implement. In the next five to 10 years, there are several key trends expected to shape the financial services industry. Banks are increasingly leveraging cloud-based solutions to store, process and analyze large amounts of data, as well as to improve scalability and reduce costs. This can help them gain insights into customer behavior and market trends.

Artificial intelligence (AI) is not a new phenomenon. It is human-made technology, that simulates, replicates and can potentially replace or add to human intelligence. It's been under development for over 60 years. You and I have been using it for quite some time now -- be it the maps we use for navigation, chatbots we use, digital assistants, video gaming, and much more. AI is similarly used a lot in the financial services sector.

Using a GPU (Graphics Processing Unit) can accelerate trading by allowing for faster processing of large amounts of data. This can be particularly useful for traditional banks, capital market firms and fintech companies that rely on data-intensive trading algorithms and need to process large amounts of data in real-time. Running machine learning algorithms: Machine learning algorithms can be computationally intensive, and a GPU can speed up the training process. This can be especially useful for developing and testing trading strategies that rely on machine learning. Data processing: A GPU can process large amounts of data quickly, which can be useful for tasks such as real-time data analysis and market monitoring.

It's been decades since algorithmic trading transformed Wall Street with its high-frequency trading, and years since the financial services industry began to integrate artificial intelligence in areas such as fraud detection, lending decisions and robo-advisory services. Yet the recent explosion of generative AI tools like ChatGPT – providing human-like text on seemingly any subject and any style so successfully it easily conquers the vaunted Turing Test – has opened the floodgates of possibilities. The advent of such a power language processor like ChatGPT – open source and available for public use – threatens to upend various parts of the financial services industry, spanning beyond areas such as chat bots and robo-advisors to even the workforce needed in something as skilled as coding. As artificial intelligence reaches a crucial tipping point – and AI bias lingers – whether the proper private and public controls are put in place ahead of the technology's dizzying progress becomes even more urgent yet challenging. In a recent Nvidia survey, 78 percent of financial services companies said they use at least one kind of artificial intelligence tool.

With voice-activated services, such as SIRI, Alexa and Google Assistant now a staple in our day-to-day lives, the introduction of this technology to help manage our finances is still subjected to scrutiny. While chatbots and virtual assistants are now well embedded in our everyday banking, do people really feel confident that their data and money are in safe hands? A recent US survey revealed that a huge 86% of consumers prefer humans to chatbots, demonstrating that there is a long way to go until people fully value and trust AI. Research by Maintel shows why companies hesitate before rolling out this technology nationwide. Data protection was cited as a key concern of consumers, with almost half (47%) of them saying that they are unwilling to use a virtual assistant to contact a company out of fear their device could be hacked, giving someone access to their sensitive personal data. This is unsurprising given the high-profile data breaches we've seen in the past by consumer brands using this kind of technology.

Suresh is a Data and AI Engineering lead for the financial services industry at Microsoft and a senior member of IEEE Computer Society. The financial services industry (FSI) is increasingly adopting artificial intelligence (AI) in recent years. The results of a recent survey by the Economist Intelligence Unit show that 85% of the respondents (banking IT leaders) have a "clear strategy" for using AI in product and service development. This is also evident in recent hiring trends in banks for AI-related jobs. It's great to see AI adoption at this scale, but this also makes it crucial for FSI leaders to watch for and avoid some of the following leading pitfalls in AI initiatives.

For best-in-class artificial intelligence solutions to actually earn that designation, Sindhu Joseph warns that the tools can't be used as "set it and forget it." Joseph, the co-founder and CEO of CogniCor, a California-based developer of an AI-powered business automation platform, reminded those attending her panel on day two of the inaugural Future Proof festival of the massive failure that was Microsoft's Tay. In spring 2016, the AI chatbot, named as an acronym for "thinking about you," was launched and pulled within a day of operation. Its machine-learning capabilities had caused it to spew racist, misogynistic and anti-semitic statements across Twitter, in a spectacular public display of garbage in, garbage out. Just "letting the machine run" without proper human guidance or care is a huge pitfall, said Joseph, who holds a PhD in artificial intelligence and is the inventor of six patents related to the technology. "There's a lot of applications where that works really well.

Crowe is looking for a Process automation senior developer with the drive to work in an entrepreneurial environment supporting our internal audit teams and large client base in the financial service industry. The RPA Engineer will work to understand workflow challenges and introduce solutions using robotic process automation tools such as Blue Prism, Microsoft s Power Automate and Power Automate Desktop. This individual will be contributing to our Financial Service Consulting practice, while working on various areas including RPA Development and Process Automation for Internal Audit processes. Ability to work both independently and in a team environment either onsite or at home. Passion, flexibility and patience to learn new things while improving processes.

Customer experience is one of the pillars upon which business success is founded. The quality of the journey a customer makes when engaging with a company determines their ongoing loyalty. For financial organisations, which handle people's income and monetary assets, the entire customer engagement process must be as stress-free as possible. The two main priorities for financial services contact centres is first-contact resolution and ensuring quick response times. Put it this way, if you call your bank and are passed off onto multiple agents – each one knowing as little as the last – before you get your response, how confident would you feel in the company's customer service?