BEYOND KNOWLEDGE MANAGEMENT: UNLOCKING GEN AI’S FULL POTENTIAL IN FINANCIAL SERVICES

In recent years, Generative AI (Gen AI) in financial services has extended far beyond some primary use cases such as search and knowledge management. What was once considered an advanced application has quickly become a standard capability across many organizations. Financial institutions are now looking to push the boundaries of Gen AI, exploring broader business applications that go beyond simple knowledge management enhancements.

With the significant infrastructure investments in Gen AI, companies are pressured to achieve tangible returns. The key to unlocking greater value lies in integrating Gen AI across end-to-end business processes, driving operational efficiencies, and enabling more intelligent decision-making. This article explores some of the latest trends in Gen AI applications across financial services, with insights into how the technology is utilized to transform various facets of business operations.

Justifying Gen AI investments through process transformation

Gen AI’s ability to improve business operations is rooted in its capacity to impact processes holistically. Implementing AI for singular functions, such as knowledge management, delivers limited value. To fully capitalize on Gen AI’s potential, organizations need to embed it across a series of interconnected tasks within a process, enabling automation and orchestration of complex workflows.

Financial operations often involve various interwoven tasks, making them challenging to automate. However, Gen AI shows promise in addressing these challenges by managing multiple categories of functions, such as:

1. Interacting with unstructured data: This involves interpreting documents like procedures, guidelines, service tickets, or contracts.
2. Querying databases: Using large language models (LLMs) to generate database queries, perform data transformations, and create visualizations.
3. Leveraging APIs: To retrieve, modify, and track data, ensuring seamless integration with existing systems.
4. Combining structured, unstructured, and API data: Bridging diverse data sources to gain comprehensive insights.
5. Triangulating information: Analyzing multiple data sources to identify inconsistencies and areas requiring attention.

Successful Gen AI applications often combine multiple tasks to improve efficiency. They can decompose large analysis tasks into smaller subtasks, interact with endpoints, and perform actions like creating new service desk tickets.

Emerging use cases of Gen AI in financial services

While the technology is still evolving, several high-potential use cases of Gen AI are emerging across various roles within financial institutions. These include applications for customer service, internal operations, and strategic decision-making.

• Customer support and contact centers: Gen AI plays a transformative role in modernizing contact centers by augmenting customer support agents with advanced capabilities. By integrating AI to analyze unstructured data (such as operating procedures and business rules) and retrieve customer data via APIs, AI-powered systems can provide accurate, timely information to address customer inquiries. This significantly reduces response times and enhances overall customer satisfaction.
• Strategic users and contract analysis: Contract analysis and similar use cases involve navigating complex hierarchies of unstructured documents, such as contracts with diverse terms and conditions. Leveraging AI, legal teams can efficiently compile comprehensive legal packs by extracting critical information from contracts, regulations, and external news sources, significantly reducing research time and enhancing accuracy. Additionally, strategic users benefit from AI-powered systems that integrate structured database queries with advanced data manipulation, empowering data-driven decision-making with real-time insights.
• Internal systems for general employees: Employee experience is another critical focus area for Gen AI applications. Financial services firms are exploring simplifying access to internal HR, payroll, and operations by deploying AI-driven systems that communicate with existing APIs. The result is more efficient workflows and reduced daily task friction, improving employee productivity.
• Operational use cases: Loan underwriting and fraud investigation: Gen AI is making inroads in operational tasks that require human judgment, such as loan underwriting and fraud investigation. These processes typically involve reviewing data from multiple sources (APIs, structured databases, and unstructured data). AI systems can analyze this data in real time, flagging potential issues or highlighting anomalies that require further investigation, enabling faster and more accurate decisions.
• End-customer self-service applications: While still in the early stages, financial institutions are exploring ways to use Gen AI for end-customer self-service applications. These solutions combine the full spectrum of AI capabilities—unstructured data analysis, database queries, and API integration—offering customers personalized insights and services in real time. However, due to regulatory and reputational risks, these applications are being adopted cautiously, with an emphasis on ensuring data security and compliance.
• Reimagining user experience: Many business processes and workflows can be reimagined with Gen AI applications blended into the workflow systems. For example, managing a large report estate with hundreds of report templates can be revitalized by reimagining the user experience using Gen AI. By embedding AI into the process, users can efficiently search for existing templates, raise tickets for new reports, and receive tailored suggestions from administrators. In addition, developers can quickly find the best method to build report templates. The application can combine all personas and make the entire process efficient with significant levels of collaboration.

The road ahead: Maximizing ROI from Gen AI

The promise of Gen AI lies not only in automating individual tasks but also in orchestrating entire processes including subtasks dynamically. With advances in LLMs that possess reasoning capabilities, combined with agentic frameworks, AI can now augment human experts by dynamically breaking down complex workflows into smaller tasks and orchestrating these tasks in real time.

In financial services, this results in an enhanced user experience through reduced context switching–fewer switches between systems and windows. For instance, embedding AI capabilities directly into enterprise communication tools like instant messaging systems in the form of avatars allows employees to access relevant information and perform tasks more efficiently through personalized digital assistants.

Overall, banks and insurers are just beginning to tap into Gen AI’s transformative potential. As these organizations continue to experiment with different applications and fine-tune AI capabilities, they will realize higher returns on their AI investments. The key to success lies in applying AI across the full spectrum of business processes, driving both operational efficiencies and superior user experiences.

Want to learn more?

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