Exploring 4 Impactful Examples of Using AI in Finance

For the banking and financial services industry, artificial intelligence (AI) isn't just a new tech trend. It's a powerful tool that will have a wide range of impacts, from risk management to operational efficiency and customer experience.

According to Deloitte, the world’s top 14 investment banks could potentially boost their front-office productivity by 27% to 35% by leveraging generative AI. McKinsey & Company predicts that AI could boost the value of the banking industry by an additional $200 billion to $340 billion annually. That might explain why IDC projects banking will be one of the two industries spending the most on AI solutions in 2024.

AWS Cloud Technologist Piyush Bothra noted in a recent interview that while algorithm-driven trading has been used for many years, there’s still great potential for financial organizations to use AI in other areas, like fraud detection.

“Think about the billions of requests coming through financial systems from various sources and places across the globe. It’s just not possible to analyze all the requests and data flows and still get insights using traditional analytics. That's where AI can help,” said Bothra. 

Read on to learn about more AI use cases for the financial services and banking industry. 

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AI will continue to have a transformative impact on the financial industry, especially in areas like risk and compliance. But its successful implementation requires competence in two core components—data and processes. It also requires that companies address technical debt so they can make the best use of AI-powered systems. The following use cases offer insight into how to successfully implement AI, including the role of data and process in AI integration.

“Changing regulations, such as Basel III, will continue to impact banks’ appetite for risk and drive changes in the way they manage risk processes across their organization,” says Guy Mettrick, Industry Vice President, Financial Services, Appian. “Banks will experience heightened scrutiny of the processes behind their risk management and credit scoring decisions. There’s also going to be a strong focus on auditability across the enterprise, and that's precisely where data fabric and AI automation platforms can make a difference.”

Bank executives and leaders at financial services firms understand that innovation is the key to enhancing resilience and addressing new challenges in risk management. Advanced technologies, from cutting-edge analytics to AI-based systems, allow institutions to leapfrog traditional risk assessment methods. By harnessing automation and machine learning for example, banks can gain real-time visibility into emerging risks, make informed decisions driven by data, and optimize risk management processes with precision.

For decades, financial services companies have relied on traditional, rule-based transaction monitoring and name screening systems, which are often prone to errors and false positives. Financial crimes have since become more prevalent and fraud patterns are continuously changing, making fraud prevention more complex than ever. Implementing a data-agnostic AI solution helps financial firms enhance existing systems to better identify previously missed transactional patterns, data anomalies, and suspicious relationships between individuals and entities that may indicate money laundering activity.

AI uses deep learning and natural language processing to look for these patterns of behavior at a large scale and learn to detect new patterns over time. As a result, the accuracy and efficiency of fraud detection processes continuously improve. AI can also help organizations investigate genuine fraud events more easily, since the information needed to investigate a screening hit can be accessed faster.

AI has a remarkable capacity to process and analyze vast amounts of data quickly, which can transform the dynamics of client relationships at financial companies. Communication has changed from mainly happening in-person and via phone calls to through online portals and chatbots. 

“It’s easy to envision a time when the traditional practice of picking up the phone to converse with a broker or retirement advisor about investment strategies will undergo a digital transformation,” says John Trapani, Industry Leader, Financial Services, Appian. “Clients will be able to engage in rich, informative dialogues online with chatbots powered by advanced large language models and bespoke content generation capabilities.”

It can be complex and time-consuming to deliver unique financial management insights to clients based on their investment trajectories and risk tolerance. Manually creating a report is labor intensive and prone to inconsistencies. AI-powered systems use tools like predictive models to address this challenge. They can analyze a client's credit history and financial statements against financial market trends to craft tailored commentaries, insights, or forecasts in real time. Once curated, this personalized content is automatically delivered to clients with unmatched precision and regularity.

Seamless AI integration like this, while invaluable, complements rather than replaces human workers. As AI takes on the tedious tasks of content aggregation, personalization, and dissemination, human intelligence is needed to bring a nuanced understanding of financial landscapes. The combination improves efficiency while still maintaining empathy, ensuring financial advice remains authentic and trustworthy. Clients get the best of both worlds: data-driven insights delivered with a human touch, resulting in a seamless customer experience.

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Like the efficiencies AI creates throughout the customer experience, it also has the ability to improve productivity for internal teams with document and query management. AI can be used to summarize documents, help craft legal agreements, extract information from research to assist research analysts, and gather details for RFPs, due diligence questionnaires, and more.

AI can extract the most relevant data from a document, categorize it, retrieve related information, and plug it all into another system for analysis, like in the process of making a credit decision. On a smaller scale, employees can use generative AI to query bank policies, draft emails, summarize meetings, and do other small, low-value tasks in less time.

Here’s a real-life example: An AI-powered automation solution can be used to process critical identity document applications for risk management purposes like anti-money laundering. For many organizations, it takes weeks to review these documents because of data that has to be matched manually. With a generative AI plug-in, a financial firm can create an app to automate the matching process. The app can take the text of the document and use AI to recommend the closest matches, reducing reviewer processing time.

For an organization in the finance industry to become a true AI enterprise, it needs to keep the elements of data and process in mind. The value AI brings to your organization is directly proportional to the quality of the data you feed it. The best way to do that is to use a data fabric, which is an architecture layer that connects data from systems across the organization to create a managed data pipeline that feeds your AI models. 

And you can try to implement AI without human intervention to assess nuances and make important decisions, but the results may be lackluster or even cause harm. The combination of AI and humans working together is what builds strong, accurate process orchestration that's crucial for AI to be at its most efficient and effective.