Explore AI and AI-Related Use Cases for Credit Unions and Small Banks
Unlock value-driven AI use cases to transform your organization.
The number of potential use cases for AI in both the credit union and small bank markets is growing daily, and AI enablement is rapidly becoming democratized. Initially, only the largest and most sophisticated banks had the resources required to implement and support AI. However, AI has rapidly been scaled and is now within reach for many smaller financial organizations.
AI has quickly made an extensive impact on financial services. Small banks and credit unions must keep pace with the broader adoption of AI throughout the financial services industry or jeopardize their long-term viability.
The time for AI adoption is now. Your organization should consider the many use cases where AI can transform internal operations and positively impact your customers.
Our Advice
Critical Insight
Your bank or credit union’s data isn’t ready to support AI. As you work to understand AI, you need to start maturing your data practices. AI doesn’t work well without good data.
You will likely need to augment your IT talent. AI requires new types of skills, such as those of data scientists, that you will likely need to hire or retrain for.
Your existing processes may need to be modified.The nature of AI will likely require updates/modifications to your processes and good governance and project management.
You are unsure of the regulatory implications of AI.Understanding the regulatory side of AI is essential.
Impact and Result
Info-Tech can support you in AI roadmapping, data maturation, and ongoing bias mitigation efforts.
This AI use case library will help you:
- Identify potential sources of value to strategically operationalize use case capabilities.
- Jumpstart the idea generation process during the capability development phase.
- Implement AI-driven use cases.
- Integrate AI opportunities using the reference architecture.
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Explore AI and AI-Related Use Cases for Credit Unions and Small Banks
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nlock value-driven AI use cases to transform your organization.
Analyst perspective
AI offers a unique opportunity for credit unions and small banks to drive growth and reduce risk and costs.
The number of potential use cases for AI in both the credit union and small bank markets is growing daily, and AI enablement is rapidly becoming democratized. Initially, only the largest and most sophisticated banks had the resources required to implement and support AI. However, AI has rapidly been scaled and is now within reach for many smaller financial organizations.
Smaller financial organizations tend to rely more on vendor-based solutions. Many of your existing vendors have already introduced AI elements into your current offering. At the same time, other vendors have been quick to capitalize on the opportunity to introduce AI-based capabilities that have rapidly moved from conceptual ideas to must have capabilities.
AI has quickly made an extensive impact on financial services. Small banks and credit unions must keep pace with the broader adoption of AI throughout the financial services industry or jeopardize their long-term viability.
AI is reshaping almost all customer-facing activities and causing structural changes to the way that banks operate. The ultimate impact of these changes could be a rapid loss of customers or the inability to economically delivery the products and services that your customer demand.
The time for AI adoption is now. Your organization should consider the many use cases where AI can transform internal operations and positively impact your customers.
David Tomljenovic, MBA, LLM, CIM
Head of Financial Services Industry Research
Info-Tech Research Group
Executive summary
Your Challenge
AI adoption is happening quickly, and the pace is accelerating. Your organization is still trying to understand how AI impacts them and where they might implement it.
You are unsure of what will happen if you don't adopt AI. Some groups within your organization are reluctant and fearful of AI while others want broad-based adoption.
As you evaluate AI, your organization is struggling with how to implement it. Should you use external AI tools, or should you use new AI tools provided by your existing, or even new, vendors?
You are unsure how AI can be used.
Common Obstacles
Your bank or credit union's data isn't ready to support AI. As you work to understand AI, you need to start maturing your data practices. AI doesn't work well without good data.
You will likely need to augment your IT talent. AI requires new types of skills, such as those of data scientists, that you will likely need to hire or retrain for.
Your existing processes may need to be modified. The nature of AI will likely require updates/modifications to your processes and good governance and project management.
You are unsure of the regulatory implications of AI. Understanding the regulatory side of AI is essential.
Info-Tech's Approach
Info-Tech can support you in AI roadmapping, data maturation, and ongoing bias mitigation efforts.
This AI use case library will help you:
- Identify potential sources of value to strategically operationalize use case capabilities.
- Jumpstart the idea generation process during the capability development phase.
- Implement AI-driven use cases.
- Integrate AI opportunities using the reference architecture.
Info-Tech Insight
The use of AI within credit unions and banks seemed unattainable only a short time ago, but the rapid democratization of AI has changed that. Now your organization is scrambling to understand AI, its use cases, how others are using it, and how it should be implemented. Accelerate your AI implementation by reviewing how organizations like yours are using AI.
AI is a strong focus among bankers
1 – American Banker, 2024
2 – MatrixFlows, n.d.
3 – "How AI and ML Are Being Used," CUSO Magazine, 2023
4 – TechMagic, 2024
5 – Cprime, 2021
6 – "Digital is draining banks," The Financial Brand, 2024
*Credit union
AI offers four critical elements to your organization
These AI-driven benefits can be applied individually or collectively.
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INSIGHT |
SPEED |
COST |
GROWTH |
|---|---|---|---|
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AI's essential capability is its ability to ingest large amounts of data and detect patterns that can become the basis of new insights. |
Many of AI's benefits result from the massive effort put into building/training models. Up-front effort drives speed when the AI is applied throughout your organization. |
The application of AI is designed to incorporate data and business rules. Many AI costs are up-front and one-time in nature and replace recurring costs, resulting in long-term cost savings. |
AI brings together insight, speed, and cost reductions, driving revenue and profitability growth as well as customer satisfaction. |
AI in banking focuses on internal (operations) and external (customer-facing) applications
The considerations are quite different, so evaluate them separately.
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Internally-focused AI capabilities |
Externally-focused AI capabilities |
|---|---|
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Internal applications for AI are intended to impact noncustomer-facing elements, such as operations. Their primary goal is to drive intelligent automation, accelerate processes, and enable scaling without increasing costs. |
External AI is primarily focused on the customer and their interactions with your bank. Its primary goal is to elevate customer experience by making processes more relevant, easy to use, and always available. |
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Internal AI focus areas:
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External AI focus areas:
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Info-Tech Insight
Internal applications can be a good place to start your organization's AI efforts. Noncustomer-facing use cases are lower risk and can be an ideal place to build your AI skills and capabilities.
AI has internal and external roles in credit unions and banks
They share common goals but drive different outcomes.
Internal AI | External AI | |
|---|---|---|
INSIGHT |
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SPEED |
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COST |
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GROWTH |
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When internal and external AI connect, there is a flywheel effect
The impact of your AI implementations can be greater than their individual pieces. Each AI capability adds to your bank or CU's momentum.
- The flywheel effect is a concept about organizational transformations developed by Jim Collins.
- The flywheel effect does not occur at a specific moment in time. It is built from a process of continuous improvement.
- Each step achieves incremental progress, but also builds momentum that results in the cumulative impact being greater than the sum of all the incremental improvements.
- AI implementation within your credit union or bank will experience the same kind of cumulative improvement that will be referred to as the "AI flywheel effect."
Info-Tech Insight
It may take some time for your organization to reach the point where internal and external AI implementations work with one another. Pursue your AI initiatives independently, and the flywheel effect will occur once you reach a critical mass.
Source: Jim Collins, The Flywheel Effect
Implement AI to drive exponential internal and external outcomes
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Responsible approaches to AI are essential in banking |
Responsible approaches to AI are crucial, requiring the adoption of specific strategies, policies, training, and accountability, as well as regulatory and accounting compliance. |
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AI is revolutionizing how credit unions and small banks operate |
AI can revolutionize operations by intelligently automating processes, increasing customer options, and making your bank more efficient. |
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Implementing internal and external AI applications creates a flywheel effect |
As AI spreads, there is an exponential impact as internal and external AI capabilities begin to interact with one another. The cumulative impact is greater than the sum of the parts. |
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External AI will transform products, services, and customer experience |
Your customers' experience will be transformed by AI, changing how and when they interact with you, enabling new ways to experience your products and services. |
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Internal AI will drive speed, efficiency and growth while reducing costs |
Your operations will become more efficient as AI reshapes your process operations, employee capabilities, and workflow. |
AI in banking presents challenges and risks that need to be carefully addressed
Ethical and legal considerations are at the top of the list.
Protect sensitive and personal data.
AI in banking is powered by sensitive data about individuals' financial lives. Increasingly, businesses are acquiring third-party data to supplement their understanding of their customers. As a result, issues of privacy, consent, fairness, accountability, and oversight must all be addressed.
Eliminate biases and errors.
AI in banking introduces or amplifies the potential for biases. The processes that generate data may have built-in biases which then produce biased data sets that can be used to train AI models. Remove existing inequalities, prejudices, and stereotypes from the data, algorithms, or systems before introducing AI.
Build trust to maintain the fiduciary relationship.
As you design and deploy AI in your credit union or bank, you must always remember the trust that your customers have placed in your organization. Your AI capabilities must ensure that your customers feel your use of AI is in their benefit. Inappropriate AI use could permanently damage your customers' trust in you.
Implementing AI in your credit union or bank comes with obligations and responsibilities
You must establish and enforce appropriate standards and regulations to ensure compliance.
Responsible, ethical, and compliant use of AI must include:
Ethical principles and guidelines
Develop and apply ethical AI principles and guidelines that ensure all customers are treated the same regardless of geographic, economic, social, or racial attributes.
Compliance and accountability
AI Implementation must include compliance and accountability mechanisms to ensure your data, algorithms, and systems meet ethical and legal standards for oversight and auditing.
Education and awareness
Promote AI education and awareness among your employees, leadership, and board to ensure they have the competencies to maintain ethical behavior and legal compliance as they use and evaluate AI.
Info-Tech Insight
AI adoption brings new rules and scrutiny to the legal and regulatory side of banks and credit unions. You must ensure your ongoing compliance.
Info-Tech can help, regardless of where you are in your AI and digital journey
Start your AI journey by evaluating use cases
Use cases illustrate how AI can apply to capabilities in your credit union or bank.
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Use Case |
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|---|---|---|
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Industry specific |
Capabilities |
Technology |
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The use case's relevance to your bank or credit union |
The activities, or jobs to be done, that your organization performs to ultimately deliver a product or service |
The base technology that enables value-creating performance gains |
AI uses cases accelerate brainstorming
What is an AI use case library?
An AI use case library is a nonexhaustive list of AI/ML and AI-related use cases that can be organized by industry, function, capability, or technology.
Why are use case libraries important?
In the context of a business reference architecture, the AI/ML and AI-related use case library:
- Identifies potential sources of value to strategically operationalize value streams and capabilities.
- Jumpstarts idea generation during the capability development phase.
- Can help you map and prioritize where you will use AI/ML in your bank or credit union.
The reference architecture can be a strong alignment tool to ensure that you and your business partners share a common AI vision and understanding.
Download the Retail Banking Industry Business Reference Architecture Template
Use cases are the strategic building blocks of business reference architecture capabilities that ultimately deliver value to the organization.
Use Case | ||
|---|---|---|
Industry specific | Capabilities | Technology |
Leverage the retail banking capability map to identify AI use case opportunities
Business capability map defined…
In business architecture, the primary view of an organization is known as a business capability map.
A business capability defines what a business does to enable value creation, rather than how it does it. Business capabilities:
- Represent stable business functions.
- Are unique and independent of each other.
- Typically have a defined business outcome.
A business capability map provides details that help the business architecture practitioner direct attention to a specific area of the business for further assessment.
Retail Banking Reference Architecture
Value streams drive AI use cases in banking
All elements of the value stream and business capabilities have already been impacted by AI.
Evaluate AI use cases in the context of specific business capabilities
Banking industry capability tree
Value streams
Core components of an organization's value chain or support structure
Level 1: capabilities
Top-level activities that your organization performs to ultimately deliver a product, program, or service
Level 2: subcapabilities
Subactivities, or jobs to be done, performed within an overarching capability
Use cases apply to specific level 1 or level 2 capabilities within the industry value stream.
Align use cases to value streams and capabilities
VALUE CHAIN DEVELOPMENT
AI banking use cases consider four sources of value
Use cases may take advantage of one or more sources of value.
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Sources of value |
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|---|---|---|---|
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Insights |
Speed |
Cost |
Growth |
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Improve the community's experience with a product, program, or service that enhances reliability, engagement, transparency, and trust. |
Expand the organization's products, programs, and service capabilities to ultimately drive optimization and improve community impact. |
Optimize the employee experience through changes that make work easier, more efficient, and more enjoyable, thus increasing job satisfaction. |
Mitigate diverse risk, health, safety, and continuity of operations concerns to preserve stable and sustainable performance. |
Understanding the AI use case library
The AI use case library includes ten internal and ten external AI opportunities.
Available Soon
Explore AI and AI-Related Use Cases for Credit Unions and Small Banks
Check back soon to watch this webinar on demand.
David
Tomljenovic
Research Director
Christine
West
Managing Partner
About Info-Tech
Info-Tech Research Group is the world’s fastest-growing information technology research and advisory company, proudly serving over 30,000 IT professionals.
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