Top AI Use Cases for Banks and Credit Unions to Show Immediate ROI
Ready to get started on your AI/ML journey?
A use case that aligns with your business goals is more likely to show ROI and encourage adoption across your organization. AI and ML use cases can be applied across your financial services institution, from creating operational efficiency to supporting a better customer experience. Below, you’ll find use cases that could bring immediate value to your bank or credit union, along with tools to support the initiative. Would you like help getting started? Passerelle provides consulting and implementation support for all use cases and technologies listed below, along with a complimentary AI-readiness workshop to provide a roadmap for adoption.
AI Use Cases at Banks and Credit Unions Include:
Customer Service – Prevent Customer Attrition/Churn
Predictive analytics and anomaly detection algorithms can use historical data to predict future events, which could include customer churn prediction. For a churn prevention use case, organizations use ML models to assess data based on features that could suggest a customer is at risk of leaving, including declining balances, account transaction patterns, and product usage. The model will assign a score to the customer, that can feed front-end digital and customer facing systems for retention efforts and give relationship managers a chance to retain the account holder with personalized offers or proactive engagement in 1-1 settings.
Tools: Snowpark ML, Qlik AutoML, Amazon SageMaker
Operational Efficiency – Automate Loan Underwriting
Banks and credit unions can use AI to automate repetitive tasks or optimize workflows. This can have significant ROI – not just in productivity gains, but with gains made as team members focus on work that adds value to the bottom line of your organization. A common automation task is seen in chatbot-supported customer service. However, financial services organizations can also use automation to speed up the loan approval process by quickly assessing applicant data from various sources, reducing the need for manual checks.
Tools: Amazon Bedrock, Snowflake Cortex, Snowpark ML, Amazon SageMaker, Qlik AutoML
Sales & Marketing – Identify Next Best Product and Drive Customer Loyalty
Most people are familiar with the next best product recommendations on e-commerce platforms, but banks and credit unions can also find immense value in using AI to help inform personalization and Customer Experience initiatives with personalized marketing campaigns or product recommendations. Using the rich data you have about account holder interactions, behaviors and assets, next best product use cases can be used to upsell account holders into new product offerings, such as car loans, business loans, insurance or investment products. Additionally, this same data can be used to drive additional service usage and customer loyalty with suggestions to improve the customer’s financial well-being.
Tools: Snowpark ML, Amazon SageMaker, Qlik AutoML
Risk & Compliance – Fraud Prevention
Banks and credit unions can use AI to identify patterns or outliers in data – especially useful for fraud detection and quality control. Increased cybersecurity risks are the other side of the coin in the rush to AI, as hackers have increased access to tools that improve their productivity. AI fraud prevention could become mandatory for organizations where cybersecurity is paramount.
Tools: Amazon SageMaker, Qlik AutoML, Snowpark ML
Sales & Marketing – Mine Unstructured Data
Natural language processing (NLP) analyzes human language to inform use cases including sentiment analysis, automated document processing and voice recognition. With NLP, organizations can unlock insights that might be hidden in data sources that have previously been too burdensome to mine, including call center transcripts and PDFs. A high-value use case for financial services institutions could look at PDFs of old loan applications, kept in cold storage for regulatory purposes, that might contain previously uncaptured information about the assets of any individual who previously applied for a loan. In the competitive deposit environment institutions continue to face today, these insights could prove an invaluable tool for deposit acquisition within your existing customer and applicant base.
Tools: Snowflake Cortex, Amazon Bedrock, Qlik Answers
Compliance/Regulatory Efficiency
Use NLP and Large Language Model (LLM) tools to mine unstructured data for enhancing vendor risk management, compliance, and regulatory validations within your organization’s internal policies and document repositories. These tools can cross-check specific regulatory wording or phrasing, identify missing elements, and validate document metadata, such as update dates and responsible individuals, ensuring robust and proactive institutional regulatory governance.
Tools: Snowflake Cortex, Amazon Bedrock, Qlik Answers
Don’t be intimidated by the rapid growth of AI and ML technology – start planning your AI strategy today. We would be happy to help.
