Artificial Intelligence (AI) is transforming markets and creating new opportunities for data-rich financial institutions. While the field of AI is developing rapidly, it can be difficult for organizations to identify the best strategies for harnessing the unique opportunities presented by this emerging technology.
At a recent webinar, Dr. Alex LaPlante, Senior Director at Borealis AI, discussed some of the innovative ways that financial institutions are beginning to use AI to better serve their customers.
1. AI is already here
Discussions about AI often focus on how it will one day change the world. But in reality, it has already begun to change many aspects of our daily lives, from retail to transportation to entertainment. And while the advent of AI has created significant investment opportunities, implementing AI successfully within a large enterprise is no small feat. Fortunately, recent developments are making it easier than ever.
2. Generative AI is a game-changer
GPT3 enables AI to write essays, summarize text, and answer nuanced natural-language questions with equally nuanced answers.
Generative AI has received a great deal of attention since last year’s release of the Generative Pre-trained Transformer 3 (GPT3), the world's largest and most complex language prediction model. GPT3 enables AI to write essays, summarize text, and answer nuanced natural-language questions with equally nuanced answers. It can also do complex forms of translation like parsing a legal document into layman’s terms.
Another more commonly discussed application of generative models is the creation of so-called deep fakes, images or video that feature artificially generated—and incredibly realistic—depictions of real people. Not only can pictures and videos be faked, but people's voices can be cloned using the same technology.
3. Computer vision technology is reaching commercial viability
Computer vision, the field of AI that analyzes image data, has also begun to disrupt industries in numerous ways of ways.
Facial authentication is one application of computer vision that many businesses, including financial institutions, have begun to leverage for a variety of purposes. Another game-changing example is in retail, where Amazon Go stores use image-tracking technology to track customers, detect when they pick up items, and automatically bill them when they leave. Other revolutionary applications include self-driving cars and smart factory sensor systems.
4. Tiny Machine Learning provides new opportunities to leverage data
Traditionally, AI has been used to crunch enormous volumes of data on massive supercomputers. Tiny Machine Learning is a branch of AI that is capable of performing data analysis on low-cost, low-power microprocessors. This technology allows companies to capture and analyze vast quantities of real-time data that would traditionally been discarded due to storage costs, like sensor readings in appliances and vehicles. While still a nascent technology, there is already significant investment in this space.
5. Finance is a data-rich industry
There are myriad opportunities to apply AI to financial data. For example, front offices are becoming increasingly reliant on chatbots, voice assistants, and biometric authentication, while capital markets firms are using AI to support trading strategies, index creation, and trade execution.
Risk management and portfolio optimization require vast amounts of data to solve complex problems.
The next big opportunities for AI are in the middle office. Risk management and portfolio optimization require vast amounts of data to solve complex problems, making them great use cases for this technology. AI is also transforming the back office, where human resources teams use it to conduct sentiment analysis and improve staff retention.
6. Reinforcement learning has enormous potential in financial services
Reinforcement learning is another branch of AI that continually refines its behaviour to improve performance as it observes the impact of each of its decisions. Reinforcement learning originated in the gaming industry, where it was used to develop algorithms that could defeat chess grandmasters and world champion Go players. Now, it is being applied in financial services.
In partnership with RBC Capital Markets, Borealis AI developed Aiden, a block trade execution platform powered by reinforcement learning. Aiden dynamical learns and adapts as it is exposed to new market data, allowing the algorithm to maintain performance regardless of market conditions.
7. Hyper-personalization will soon reshape how financial services are provided
Clients in the financial services industry are increasingly demanding a hyper-personalized experience. They want products and services tailored to their needs while still receiving a consistent experience across channels and engagement platforms. Hyper-personalization is the use of AI to meet these demands.
Currently, social media and entertainment companies are leading the field of hyper-personalization. Netflix, for example, leverages user data to customize most aspects of the user's interface, from recommendations to images to text content. Financial institutions are currently experimenting with this technology, but must deal with the intricacies of multi-channel engagement and the sensitivities around managing personal finances.
8. It takes careful consideration to scale AI innovations
Having the right business culture is crucial to implementing AI at scale. Often, businesses focus on raw technical expertise rather than on building multidisciplinary teams. Data scientists, researchers, machine learning engineers and technical product managers are all critical, but so is cognitive diversity. Most businesses invest in training their existing staff on how to work with AI, but it is also vital to educate AI development teams on business operations. Implementing AI at scale is only possible with a one-team mindset.
Having the right business culture is crucial to implementing AI at scale.
Successful AI development requires a human-centric culture. It is people who build AI systems, and it is people who will ultimately use them. Building trust and respect among the teams working on these projects is crucial. Technical teams and domain experts should always be mindful of the end user.
9. Machine Learning Ops is evolving how enterprises implement and manage AI
AI systems are not static solutions—they require continuous monitoring and adjustment. The process of deploying, maintaining, iterating and monitoring these systems over time is called Machine Learning Ops, or AI Ops. AIOps helps to manage the production lifecycle if AI systems and allows for improved production quality and stability. It is a crucial component to achieving reliable and scalable AI capabilities within the enterprise.
10. Governance and risk management remain persistent challenges for AI innovation
AI brings its own set of challenges concerning governance and risk management. Fairness, bias, robustness, and explainability are essential considerations when designing AI systems, especially in the financial services industry. These ethical principles must be embedded into the design and development process from the get-go in order to ensure that AI is applied safely.
Final word: AI is no longer a distant prospect but is already a game-changer that is transforming industries today. Data-rich sectors, including financial services, are just at the beginning of their journey as they race to keep up with the latest machine learning innovations. While many aspects of these new technologies require a novel and forward-thinking approach, firms should not forget to apply sound governance and risk management procedures when implementing and utilizing them.