10 Generative AI use cases to unlock new efficiencies & insights in Financial Services

10 Generative AI use cases to unlock new efficiencies & insights in Financial Services

<div class="insights_cta-component">Financial services firms are constantly seeking ways to streamline processes, reduce costs, and enhance customer experiences. Generative AI, with its ability to understand and create content, offers a powerful solution to many of these challenges. This article presents ten transformative use cases that can deliver short-term value across your financial services operations.</div>

Table of contents

1. Personalized contract and agreement generation

Financial service companies invest significant time in creating contracts for a wide range of clients. Generative AI enables automated contract generation, tailored to specific needs and situations, by integrating customer data, historical contracts, and Generative AI tooling. This use case streamlines the contract creation process, allowing legal teams to focus on value-adding tasks.

2.  Improved customer due diligence assessment

Financial service providers devote considerable effort to customer due diligence (CDD) processes, which are both time-consuming and demand a high level of attention to detail. In CDD it is necessary to analyze a variety of documents. For example, when identifying the ultimate beneficial owner (UBO) of businesses, CDD employees must evaluate text, images, and charts to assess board resolutions and shareholding patterns. Generative AI can process and analyze vast amounts of multi-modal content, providing summaries and insights, such as discrepancies between text and associated images. By leveraging Generative AI, CDD employees can be significantly unburdened, leading to substantial efficiency gains.

3. Automated call logging and summarization

Customer service & tele sales agents need to log and summarize many conversations. This can easily be automated -typically within just a few days of development time – by combining speech-to-text tooling like Deepgram, to generates the text transcript, and Gen AI tooling like OpenAI, to summarize the transcript into any desired format. This use case reduces time spent on summarizing calls by up to 100%; proven to result in 10-15% efficiency gain at many clients. Furthermore, Gen AI call summarization also improves quality and consistency of summaries across agents, generating valuable data for further analysis.

4. Improved transaction monitoring & investigation in KYC

Financial service providers face challenges in verifying and reconciling vast amounts of data during the KYC process. Generative AI can identify discrepancies between internal (e.g., previous transactions) and external (e.g., publicly available Politically Exposed Persons registers) data sources. It can also synthesize and share data between departments such as Compliance and Risk, ensuring consistency in information and streamlining workflows to avoid duplicative efforts. Additionally, by analyzing data patterns, Generative AI can suggest decisions, thereby improving the efficiency and accuracy of KYC staff and allowing them to focus on cases requiring nuanced human judgment.

5. Assisted data field entry

Customer service and back office agents are required to fill data fields with high accuracy, as errors like entering the wrong address can have potentially serious implications. Generative AI can enhance this process by verifying the accuracy of filled fields and offering suggestions during data entry. This can be based on live call transcriptions or existing customer information. Implementing this use case not only improves the quality of customer service and back office, but also ensures overall data quality, enabling many other (AI and non-AI) use cases.

6. Accelerated claim handling

Insurance companies spend considerable time handling claims. Generative AI, known for its capability to summarize large volumes of documentation, can significantly streamline this process. By integrating Generative AI tooling with customer data and historical claims, firms can develop tools that assist in managing claims more efficiently. Gen AI can provide suggestions with relevant references & reasoning, while keeping the human in the loop for the final verdict. This approach can greatly enhance the efficiency of back-office operations.

7. Automated customer FAQ updating

Customer service departments have difficulty keeping their FAQ sections continuously updated with relevant and accurate information. This process can be automated by using generative AI to analyze written and verbal customer service interactions, and identify frequently asked questions accordingly. Subsequently, you can automatically generate – and even automatically update – FAQs based on the responses provided by agents in past interactions. This approach ensures that the FAQs remain relevant and well-maintained, ultimately leading to fewer customer service calls.

8. Automated payment or churn risk alerting  

Retaining customers and ensuring timely payments are key worries for businesses. Generative AI can help assess and alert to these risks by analyzing conversations and identifying elements that may indicate potential churn or payment issues. This enables proactive alert triggering and advise on e.g., targeted discounts or budgeting for at-risk customers. This proactive approach not only enhances customer retention, but also improves payment rates, significantly impacting bottom lines.

9. Personal banking assistance

Households often struggle with complex financial decisions and understanding contracts. By leveraging Retrieval-Augmented Generation (RAG) with Generative AI tooling, as well as internal data sources (e.g., product information), and external data sources(e.g., news websites), a personal banking assistant can be developed to aid customers. This tool can assist customers in several ways: it can provide personalized advice for specific financial situations, but can also clarify complex documentation, such as mortgage contracts. Finally, it can serve as a personal financial expert, advising on decisions like whether it is wise to buy a house given rising interest rates. This use case reduces the workload for customer service and advisors, and improves customer satisfaction and retention.

10. Automated market, competitor, and pricing reports

Financial companies need to track many different industries and competitors, often resorting to purchasing expensive reports. Generative AI can assist by scraping and analyzing data from websites, automating the reporting process, and creating interactive reports. To illustrate this, you can distribute a weekly pricing update to your pricing team by automatically analyzing competitor prices. Additionally, you can ensure that your tech investment team receives timely alerts on critical developments in the tech industry. This approach not only cuts external costs but also keeps the company informed and up-to-date with market trends.

Intrigued by the potential of Generative AI but unsure where to start? Let's discuss your specific challenges. From workshops to identifying high-impact use cases and creating a personalized roadmap for your financial services organization, our experts work with you to develop custom solutions to accelerate your AI journey.
Matti van Engelen
Associate partner | Practice Lead Data & AI

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