The potential applications of generative AI, particularly through tools like ChatGPT, are vast, promising an era of enhanced customer engagement, operational efficiency and innovation. While large banks have the muscle to invest and wait longer for returns, smaller banks need tangible results over a quicker timeframe. In this post, we'll explore 10 common use cases for generative AI that community banks and credit unions can get started with.
1 Personalized Advice: Tailoring conversations to customer needs
Many banks provide financial advice to their customers. Such financial advisors can combine human expertise with the power of AI to give consumers more comprehensive and customized financial plans. The Bank must train the Generative AI on their customers’ financial goals, risk profiles, income levels, and spending habits. Once trained, the AI tool can then make personalized budgeting and saving recommendations.
The same goes for investing. Generative AI can make suggestions based on customers’ financial goals, income, and time horizons. For financial planners, this can lead to smarter investment & wealth management and trading decisions.
By providing personalized advice through generative AI, mid sized banks can enhance customer engagement and cultivate enduring relationships in their digital interactions.
2 Content Creation: A Gateway to Consistent Engagement
Creating engaging and timely content can strain lean marketing teams and consume valuable resources. Generative AI can be utilized to automate content creation bank’s newsletters, marketing messages and more.
Blog and Social Media Content Writing
With the right prompts and inputs, large language models (LLMs) are capable of creating appropriate and creative content for blogs, social media posts, product pages, and business websites. Existing content can be modified, shortened, or expanded upon with generative tools, and many of these tools can also generate entirely new content with the right user prompts and contextual information.
Generative AI models that focus on this type of content generation enable users to give instructions on article tone and voice, input past written content from the brand, and add other specifications so new content is written in a way that sounds human and relevant to the brand’s audience. However, users need to remember that generated content could contain inaccuracies or unsubstantiated claims; particularly for content that is journalistic or fact-driven, writers, editors, and/or QA analysts should take the time to personally fact-check AI outputs before publishing them.
AI's ability to generate high-quality content helps to ensure consistent customer engagement while relieving marketing teams of content creation pressures.
3 Service Chatbots: Automating customer support
This is a very common use case for AI and deployed successfully across banks of all sizes. A report by McKinsey & Company notes that, in the most advanced levels of AI maturity, companies can handle more than 95% of their service interactions through AI and digital channels. Generative AI’s capability to understand context can be utilized to create service chatbots that effectively address routine customer inquiries. By carefully training customers to provide comprehensive prompts, banks can ensure accurate and context-aware responses, improving customer interactions and reducing reliance on human agents.
4 AI Assistants: Bridging The Time Gap
Providing customer care beyond business hours poses a challenge for banks with a limited workforce, but recent trends indicate that the physical location and hours of operation of a bank are not nearly as important as they used to be because digital tools are helping them enhance customer engagement during non-traditional hours, catering to their changing preferences.
Generative AI solutions like ChatGPT, in particular, can serve as a personal assistant that enables banks to engage customers, schedule appointments and set reminders. By integrating AI-driven assistance with appointment management systems, banks can address customers needs beyond traditional working hours.
The true value proposition lies in time—the most precious asset for financial advisors. If an AI can conserve just an hour of an advisor’s time, it's considered a monumental success. New capabilities like automated note-taking feature that can draft documentation for editing or augmentation by financial advisors, seamlessly integrating with client relationship management systems and streamlining the follow-up process.
5 AI Interpreters: Breaking Language Barriers
Language diversity can pose communication barriers with non-English speaking customers, but generative AI’s language generation and interpretation capabilities can be harnessed to offer automated support to non-English speaking customers.
By automating language translation and interpretation, banks can provide more inclusive and accessible customer services.
6 Optimized Enterprise Search and Knowledge Base
Both internal and external search are benefitting from generative AI technology. For employees and other internal users of business tools, generative AI models can be used to scour, identify, and/or summarize bank’s resources when users are searching for certain information about their jobs or projects. These tools are designed for not only searching typical sources, like company files, but also company applications, messaging tools, and web properties.
Example solutions
7 Project Management and Operations
Project management platforms are in the early stages of incorporating generative AI into their toolkits, but many have already released public betas or full versions of AI suites to their users. These tools can support users with everything from task and subtask generation and recommendations to note-taking to project risk prediction, and use cases continue to expand, particularly for automation workflows. Project management AI tools also help users manage and summarize documents, datasets, and other assets so both internal resources and client-submitted information can be processed and applied to projects more efficiently.
Several generative AI tools have also emerged for assistive and secretarial tasks, both within project management platforms and as standalone solutions. With these tools, users can use a voice assistant to take notes and jot down ideas on their mobile devices, create smart and quick email replies, complete smart searches and summaries of important business documents, and automate certain communication workflows. The goal of this type of technology is to save time, giving users the ability to focus their efforts on higher-level strategy rather than day-to-day business and data management.
8 Business Performance Reporting and Data Analytics
Because generative AI can work through massive amounts of text and data to quickly summarize the main points, it is becoming an important tool for business intelligence and performance reporting. It’s especially useful for unstructured and qualitative data analytics, as these types of data usually require more processing before insights can be drawn.
Generative AI data analytics tools may be standalone products or embedded features within well-established data analytics platforms, such as Power BI. Generative AI enables traditional data analytics platforms to go beyond manual workflows and visualizations by supplementing the data scientist’s ideas with suggestions for improved visuals, easier-to-read reports, and cleaner data.
One of the most interesting areas being explored with this technology is data narratives, which are highly contextualized AI explanations of datasets. This goes beyond typical visualizations and dashboards into explainable data, which is particularly helpful for less technical business stakeholders and other key players who need straightforward information about business performance.
9 Intelligent Loan Intake & processing.
A generative-AI-based virtual assistant or copilot can help shepherd small-business owners through the time-consuming and sometimes perplexing loan application process and take care of much of the work loan officers typically do, such as data validation, qualification and loan approval.
- Loan Application: Moreover, generative AI-based chatbots can assist clients in completing forms and responding to queries as they move through the loan application process. Also, by having a discussion with customers in natural language, generative AI can be used in banking to confirm consumer data.
- Credit Analysis: Credit analysts can utilize generative AI to analyze consumer credit ratings and financial histories to determine a customer’s creditworthiness. Additionally, it may assess data from a variety of sources to gauge the riskiness of a loan application.
- Loan Underwriting: A decision-support tool for loan underwriters is generative AI. Customer data can be analyzed by the technology, which can then use its risk analysis to offer tailored suggestions. Automation of specific credit note parts is possible with generative AI. The executive summary, business description, and industry analysis are a few of them.
- Loan Servicing: The use of generative AI in banking can also assist borrowers with problems related to loan servicing. In particular, account management, billing questions, and payment reminders can be helped by generative AI. Based on their financial history, it can also tailor recommendations for debt payback
10 Customer Feedback Analysis
Generative AI automates customer surveys to enhance the data collection and analysis capabilities of traditional surveys. It analyses patterns in customer interactions to generate insights into what customers feel about the products/services offered. It also offers an in-depth understanding of customers’ needs by generating new questions based on customer behavior and response.
Conclusion
As is quickly becoming apparent, every company is becoming a generative AI company, putting the onus on banks to identify their unique value propositions and carve their distinct niche when getting started with AI. By focusing on distinctive customer experiences and leveraging AI as a tool for augmentation, banks can remain true to their core values while embracing technological progress.
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