Producing novel content represents a definitive shift in the capabilities of AI, moving it from an enabler of our work to a potential co-pilot. It can be difficult to implement uses of gen AI across various business units, and different units can have varying levels of functional development on gen AI. With this archetype, it is easy to get buy-in from the business units and functions, as gen AI strategies bubble from the bottom up. Here are a few examples of companies using AI and blockchain to raise capital, manage crypto and more. Gynger uses AI to power its platform for financing tech purchases, offering solutions for both buyers and vendors.

Banking

Banks and other financial institutions can take different approaches to how they set up their gen AI operating models, ranging from the highly centralized to the highly decentralized. We have observed that the majority of financial institutions making the most of gen AI are using a more centrally led operating model for the technology, even if other parts of the enterprise are more decentralized. Ascent provides the financial sector with AI-powered solutions that automate the compliance processes for regulations their clients need. It analyzes regulatory data, customizes compliance workflows, constantly monitors for rules changes and sends quick alerts through the proper channels. The company aims for financial firms to have increased accuracy and efficiency. AI assistants, such as chatbots, use AI to generate personalized financial advice and natural language processing to provide instant, self-help customer service.

Investments

By breaking down these silos, applying an AI layer, and leveraging human engagement in a seamless way, financial institutions can create experiences that address the unique needs of their customers while scaling efficiently. Many organizations have gone digital and learned new ways to sell, add efficiencies, and focus on their data. Going forward, they will need to personalize relationship-based customer engagement at scale. Delight your customers with human-like AI-powered contact center experiences, such as banking concierge or customer center, to lower costs, and free up your human agents’ time. Transform personal finance and give customers more ways to manage their money by bringing smart, intuitive experiences to your apps, websites, general journal digital platforms, and virtual tools. The widespread use of AI could introduce new sources and channels of systemic risk transmission (e.g. interconnectedness, herding behaviour, procyclicality, third party dependency).

Benefits of AI in Finance

Companies are continually looking for an edge and AI is proving an important tool. By leveraging AI capabilities, companies are seeing improvements streamlining operations by automating routine tasks, reducing human error, and optimizing processes. It’s the schools, the churches, the sports teams, and definitely the businesses. I’ve got a total soft spot for small businesses, particularly those started and owned by women and nonbinary people, where the founder is everything to the business—CEO, general counsel, CMO, CFO.

1. And since Finance draws upon enormous amounts of data, it’s a natural fit to take advantage of generative AI.
2. Financial institutions using more dispersed approaches, on the other hand, struggle to move use cases past the pilot stage.
3. But what I realized that evening was that, while Jack was awesome, what the women and nonbinary individuals who were there really benefited from was, number one, just finding each other.
4. One report found that 27 percent of all payments made in 2020 were done with credit cards.
5. We fed it the knowledge of all the diligence questions we had answered up to that point, and we fed it our management presentation.

A May 2023 survey of around 75 CFOs at large organizations found that almost a quarter (22 percent) were actively investigating uses for gen AI within finance, while another 4 percent were pursuing pilots of the technology. In the short term, generative AI will allow for further automation of financial analysis and reporting, enhancement of risk mitigation efforts, and optimization of financial operations. With its ability to process vast amounts of data and quickly produce novel content, generative AI holds a promise for progressive disruptions we cannot yet anticipate.

With software automation systems, customers can securely upload identity documents to a web-based location. This simplifies the customer interaction with banks, reduces overall processing time, and reduces human errors in the process. Our surveys also show that about 20 percent of the financial institutions studied use the highly centralized operating-model archetype, centralizing gen AI strategic steering, standard setting, and execution. About 30 percent use the centrally led, business unit–executed approach, centralizing decision making but delegating execution. Roughly 30 percent use the business unit–led, centrally supported approach, centralizing only standard setting and allowing each unit to set and execute its strategic priorities. The remaining institutions, approximately 20 percent, fall under the highly decentralized archetype.

Attitude and challenges

Morgan Chase found that 89 percent of respondents use mobile apps for banking. Additionally, 41 percent said they wanted more personalized banking experiences and information. Gradient AI specializes in AI-powered underwriting and claims management solutions for the insurance industry. For example, the company’s products for commercial auto claims are able to predict how likely a bodily injury claim is to cross a certain cost threshold and how likely it is to lead to costly litigation.