Summary: The financial services industry has seen the buzz over artificial intelligence (AI) before, but will the result be different this time? In its current form, AI is augmenting human intelligence and deriving insights at a transactional level. In the future, however, AI could help process data to build new algorithms around financial services.
Since its inception in the 1950s, AI has seen at least two major hype cycles and long winters of disillusionment. Although AI – in its purest form – just underwent its longest disillusionment cycle (1990s - present), AI’s supporting and ancillary tools have thrived. This has unleashed a new wave in applications of artificial technology, many of which have seeped into fintech.
Financial services have been revolutionized by the computational arms race of the last twenty-plus years, mainly as technologies such as big data analytics, expert systems, neural networks, evolutionary algorithms and machine learning have allowed computers to crunch deeper and more varied data sets than ever before. Financial services companies are entering a new wave of possibility on how to leverage artificial intelligence by enabling computers to watch and learn [^1].
While most “AI” businesses today are built around machines making decisions, they don't really deploy AI, in the true sense of the word. In actuality, these businesses are using data-intensive technologies that will help companies get closer to implementing “true” AI in commercial applications.
Replacing or Augmenting Humans?
Despite the hype surrounding intelligent machines, the current applications of AI are not replacing humans and human intelligence, but rather augmenting them. Text-based conversational applications have been embraced by many startups as a way to deliver a personal assistant-like experience. In fintech, for instance, we've seen companies such as Kasisto augment financial applications by enabling intelligent conversations using an optimal mix of speech, text and touch interfaces.
Consumer banking, advisory services, financial planning and wealth management can be delivered using a conversational UX powered by AI. The mix of technologies can enable companies to tap into customer segments that were previously unprofitable to service (i.e. lower net worth segments for personal financial, investment and retirement planning and advisory).
In addition to new services that use this kind of UX as a differentiating factor, larger financial services companies may integrate conversational UX powered by AI as a way to provide better, faster and cheaper customer support. Some interactions will still be handled by humans, but AI can enable companies to deliver better service and refocus consumer support costs on higher-value areas.
Delivering Personal Experiences
Is a robo-advisor that offers you rules-based advice based on a set of predefined parameters considered AI? Probably not. But future technologies that are based on watching and learning from your behaviors at the individual level - rather than the collective level - could deliver advice and outcomes that are far more personalized. Today, however, fintech companies can provide service and advice at the transactional level. For example, the smart wallet Wallet.AI incorporates contextual awareness into its software, analyzing users’ transaction histories to offer timely financial advice.
Enterprise Oversight and Decisioning
The idea of augmenting human interactions and intelligence with AI doesn't end with consumer-facing products. AI could power technologies that provide oversight and tracking mechanisms on employees, providing compliance, security and monitoring e.g. Palantir's Trader Oversight. By monitoring discrete, repetitive data entry tasks, computers could watch and learn to verify data entry as well as test for specific events, assess risk, and identify fraud. Any area of fintech that is regulated creates the opportunity to deploy AI-powered employee and systems oversight.
The opportunities in the enterprise, however, don't end with inward-facing applications. Lending and underwriting products could be augmented by AI technologies that allow computers to make better decisions than humans can. Indeed, data created by the Internet of Things can be used to make more accurate credit and insurance decisions. AI-based technologies make it more likely for firms to use these new datasets in highly personal ways at scale.
Algorithms and AI
Fintech firms have historically created evolutionary algorithms to help them learn, find, and act on existing and new heterogeneous data sets. New data sets created by companies such as Dataminr use powerful, proprietary algorithms to analyze publicly available data to deliver off the radar context and perspective. Going forward, AI could help oversee and augment trading decisions and rules, helping process the data and actually creating the algorithms.
Platforms and Applications
Fintech is in an interesting position in that the technologies around AI have been tested and deployed in specific applications for the last couple decades and have powered much of the innovation in financial services. While much of the investment in AI has been funneled into multi-purpose platforms that are still figuring out their specific, high-value use cases [^2], the opportunity in fintech is a bit different. Fintech has a base of technological prowess in the technologies supporting AI and a number of immediate high-value applications.
At first, AI may be deployed more intensely in back-end technology settings to power large-scale decisioning in lending, trading and financial analysis, but it could eventually be a technology that expands how everybody interacts with financial services firms. Many complaints with incumbent financial institutions center on the difficulties in getting high quality and personalized service. Perhaps it's an AI agent that that will help deliver much cheaper, faster and more personalized services in the future.