Smart finance companies will begin their AI transformation efforts by assessing their capabilities and then deciding which AI innovations they are capable of supporting.
When it comes to integrating AI into business, the finance industry is already far ahead of most of its peers. Banks have been using algorithms and classical machine learning for decades to help streamline processes like fraud detection and credit scoring.
But that doesn’t mean that the finance industry lacks room to grow in the realm of AI. On the contrary, by taking advantage of newer types of AI technology, such as generative AI, banks can double down on the value that AI already brings to this sector – which is on course to increase overall revenues by perhaps almost 5 percent through continued AI innovation, according to McKinsey.
Here’s a look at how the next generation of AI transformation in banking is likely to play out and what finance companies will need to do to ensure they’re prepared to take full advantage of modern AI.
How AI will reshape finance: Four examples
Modern AI technology is primed to impact finance companies across multiple areas of operation, although the innovation will probably happen in some contexts before others.
1) Adding efficiency to back-office operations
The back office – meaning the administrative part of a finance company rather than the client-facing part – will likely be among the first areas where next-generation AI technology disrupts banking operations. Here, generative AI can automate repetitive and time-consuming tasks like producing compliance reports and merging documentation systems following acquisitions.
We probably won’t see a ton of headlines about genAI-based innovations in areas like these because they are not ones that banks’ customers will notice. But from an operational perspective, integrating AI more deeply into back office processes can have a profound effect on overall productivity and ROI. It will allow banks to tackle increasingly challenging tasks, like staying ahead of regulatory laws that are constantly growing more complex, without growing their back-office headcount or personnel costs.
2) Optimizing classical finance AI systems
Another early opportunity for capitalizing on modern AI technology in finance is using it to optimize the AI systems that banks already have in place – such as, again, those that manage fraud detection and credit reporting.
I’m not talking here about rebuilding these systems from scratch. Instead, expect to see banks make changes like incorporating new types of data into conventional AI systems. For example, rather than trying to detect fraud by looking only for anomalies in payment transactions, a bank could also analyze real-time streaming video from the point of sale to assess whether the person trying to buy something is the legitimate account owner.
Upgrades like these could significantly increase the accuracy of traditional AI systems in the finance sector, which would, in turn, improve ROI and lower costs.
3) Delivering truly personalized experiences
The concept of customization or personalization has long been important in finance. Traditionally, however, most personalization efforts by banks have been limited. A bank might offer a credit card designed for people in their twenties or those who like rock music, for instance. But delivering truly personal banking services and products that are customized for each individual client has not been feasible.
Modern AI technology changes this, however, by making it possible both to perform hyper-individualized analysis of each customer’s preferences and to generate individualized content for products and services. Imagine, for instance, a banking website whose content is auto-generated on the fly by a large language model (LLM) to display opportunities of interest to each individual user.
This type of innovation will take some time to build. Currently, tools for use cases like on-the-fly generation of website content by AI models aren’t mature. But it’s feasible enough to do, and it’s likely only a matter of time before developers create the tools to do it.
4) Richer data sources and analytics for algorithmic trading
For years, sophisticated investors have used data sources like satellite images to gain valuable insights about where to place their money. But they have relied largely on manual processes to interpret and react to that data.
With modern AI, these processes could be fully automated, allowing hedge funds and investment banks to take algorithmic trading to a new level. For example, they could deploy AI systems that monitor the operations of manufacturing plants and then automatically make trades based on what they learn.
Here again, strategies like this require highly sophisticated systems that (as far as the public knows, at least) have yet to be built. But the AI technology necessary to build them is here.
See also: The Top Value Application for AI? Real-time Capabilities
Preparing the way for AI innovation in data
Although banks can theoretically begin building the types of AI solutions described above today, they’re not likely to get very far unless they address a few key AI challenges first.
One is the need for a healthy data foundation. Without high volumes of quality data, creating AI systems capable of handling complex finance use cases will prove impossible. This is especially true for the banking industry, where data has a tendency to be highly siloed between different types of systems – such as the decades-old mainframes that still power some banking services and more modern Customer Relationship Management (CRM) or sales platforms that banks have also adopted.
Banks will also need to assess and address the security challenges surrounding modern AI technology. While AI can help to automate security processes in finance, flaws in AI systems can create new risks. For instance, imagine that threat actors find a way to poison the LLM that a bank relies on to support customers whose accounts are locked due to suspected fraud. The attackers could potentially trick the LLM into causing the accounts to be reopened, effectively defeating the fraud protection controls.
Another unique challenge that arises when organizations adopt generative AI is a potential lack of transparency about how decisions are made. This could prove especially challenging for banks, which sometimes face regulatory requirements to provide an explanation for actions like closing accounts or denying loan applications. If these decisions are made by “black box” AI services, banks might not have the data they need to explain their decision-making.
Conclusion: The future of AI in banking
AI may not be new in finance, but new types of AI have opened a trove of novel opportunities for optimizing banking services and operations. However, enabling these innovations requires more than access to modern AI technology. Banks also need the data, security, and transparency solutions necessary to address the unique challenges posed by next-generation AI. Smart finance companies will begin their AI transformation efforts by assessing their capabilities in these areas and then deciding which AI innovations they are capable of supporting.
