The buzz around AI is deafening these days, no matter the industry. Experts of all sizes and specialties are raving about its ability to deliver speed, slash costs, crank up quality, and make things just plain easier. We, at Sphere, are right there with them.
We’ve been all over the map, exploring the real-world possibilities of AI in various sectors. From healthcare to portfolio management, wealth management to code-slinging with AI assistants, we’ve been uncovering its potential. Now, let’s shift gears and dive into the fascinating realm of AI in finance.
These insights come straight from the trenches – our client work and industry events like the recent AI in Finance Summit, held in New York City. The big takeaway there? Governance is key, and a holistic, cross-functional approach is the way to go when it comes to implementing AI.
The Evolution of Data Governance
One of the central themes at the AI in Finance Conference was Natural Language Processing (NLP) and its ability to ingest, summarize, and extract meaning from large volumes of unstructured data. This has led to a growing emphasis on AI governance, with many companies adopting a “whole-company” approach to AI governance, aiming to reduce or eliminate biases. This approach involves a cross-functional team representing all areas within the company, ensuring proper oversight and accountability.
“Most recent GenAI use cases involve generating content and formal documents for clients. The best practice, however, is to always have a “human in the loop,” ensuring AI-generated content never goes directly to clients without human review”.
Joe Nestory, Business Development Director at Sphere
The Rise of Retrieval-Augmented Generation
Another key focus of the conference was Retrieval-Augmented Generation (RAG), a technique that allows large language models to leverage external data sources beyond their training data. RAG can significantly enhance the relevance and accuracy of AI-generated outputs. According to one of the speakers, Brennan Lodge, Head of Advanced Analytics Engines, Cybersecurity at HSBC, this advanced tool embodies the fusion of AI’s potential with the intricacies of GRC, offering a revolutionary way to manage compliance, understand data privacy laws across jurisdictions, and bridge the gap between current practices and regulatory expectations. By leveraging RAG’s capabilities, institutions can navigate complex regulations such as GDPR and CCPA, as well as emerging data protection standards globally.
RAG serves as a critical solution for bridging the compliance gap. It allows financial institutions to conduct efficient gap analyses by comparing existing policies and practices against a wide range of global regulations and standards, including GDPR, CCPA, and emerging frameworks. This technology holds the potential to significantly improve governance and compliance within the financial services sector, leading to more streamlined and effective processes.
We’ve already explained how RAG works with an example from our own development – the unified Corporate Knowledge Agent for customer support. This agent uses RAG to quickly access and deliver accurate information to customers, streamlining support interactions and improving overall efficiency.
No AI Without Data
Vendors stressed the critical need for organizations to have their data “ready” for AI applications, echoing the sentiment that “there is no AI without data.” Academics advised companies to start AI proof-of-concepts (POCs) with narrow use cases and expand functionality once the initial goals are achieved. It’s estimated that 80% of organizational data is unstructured, highlighting the need for converting it into usable formats to maximize the potential of AI applications.
Legal and Regulatory Considerations
While many presenters advocated for a focused approach to AI use cases, the overarching consensus was that the legal and regulatory landscape surrounding AI remains unsettled. Attorneys highlighted the need for organizational agility and adaptability, as new AI regulations are expected to emerge frequently in the coming months and years. This adds a layer of complexity to AI implementation strategies, requiring companies to remain flexible and proactive.
Navigating the AI Fog
Perhaps the most significant takeaway from the conference was the recognition of the unprecedented pace of AI advancements. Speakers avoided making long-term predictions, acknowledging that the rapidly evolving AI landscape could quickly render forecasts obsolete. This uncertainty demands a high level of adaptability from companies in the finance sector.
AI Finance Use Cases
As the finance industry continues to embrace AI technologies, several key trends emerged from the AI in Finance Summit and other industry reports. Jonathan Regenstein, Industry Principal Sales Engineer at Snowflake, stated that while generative AI was the technology story of 2023, we expect 2024 will be the year when organizations shift from the art of the possible to real, tangible AI implementation. So, what is AI doing for the industry right now?
- AI Fights Fraud – AI excels at identifying suspicious activity. Machine learning can flag unusual transactions, helping you stay ahead of fraudsters and safeguard your clients.
- Chatbots: Your 24/7 Sidekick – Provide exceptional customer support with AI-powered chatbots that answer questions and streamline transactions, freeing up your team for more strategic work.
- Invest with the Power of AI – Gain a competitive edge with AI algorithms that analyze vast amounts of data to generate investment insights and portfolio recommendations.
- Smarter Loan Decisions – AI can assess creditworthiness more accurately, enabling faster loan approvals and tailored products for your clients.
- Effortless Compliance – Automate risk assessments and regulatory compliance tasks with AI, freeing up your team’s time and minimizing errors.
- Work Smarter, Not Harder – Extract data from financial documents, automate data entry, and streamline back-office processes with AI-powered NLP, saving you time and resources.
- Predict the Future – Leverage AI to analyze historical data and forecast future trends, empowering you to make data-driven decisions and stay ahead of the curve.
Embrace AI and Lead the Way
By focusing on these high-impact use cases, you can harness the power of AI to transform your financial institution. At Sphere, we’re here to guide you through the ever-evolving AI landscape and implement solutions that deliver a clear ROI. Contact us today and discover how AI can propel your business to new heights.
