The AI Journal | How smart banks leverage AI benchmarking for growth and risk management
AI is revolutionizing banking, changing how financial institutions measure efficiency, assess risk, and enhance customer experience. Instead of relying on one-size-fits-all benchmarks, banks can now use AI-driven insights to create real-time, dynamic performance metrics—helping them make smarter, faster decisions.
Historically, banks relied on backward-looking financial models and industry reports to set performance benchmarks. These traditional methods often fail to capture real-time market changes, evolving risk factors, and shifting customer behaviors. AI flips the script by enabling:
- Real-time performance monitoring – AI can continuously ingest and analyze transaction data, allowing banks to adjust benchmarks dynamically.
- Adaptive risk scoring – AI-powered risk models evolve based on new fraud trends, regulatory updates, and customer behavior.
- Personalized customer insights – AI-driven customer segmentation helps banks set benchmarks tailored to specific segments and product offerings.
- AI-powered customer support – From reducing wait times to providing 24/7 support, AI can help enhance the customer experience while improving operational efficiency
AI benchmarking: Key use cases
1. Risk management & fraud detection
Banks are leveraging AI to redefine fraud detection benchmarks. Instead of static thresholds, AI models analyze billions of transactions as they are made, establishing a more risk-based benchmarking system to detect suspicious activity. AI enables banks and credit unions to set alert thresholds based on deviations in transaction behavior rather than preset dollar limits, reducing false positives and saving anti-money laundering (AML) professionals time.
AI-powered fraud scoring models can set different benchmarks for fraud by sector, transaction type, and customer segment. They even allow for comparison against peer banks dealing with the same types and amounts of fraud. Shifting to AI-assisted risk management is a step towards proactive, rather than reactive, fraud detection, reducing financial institutions’ losses and enhancing customer security.
2. Credit risk & lending benchmarks
AI has transformed credit risk assessment, moving beyond traditional FICO-based lending benchmarks. One of the biggest challenges in credit risk management is ensuring consistency across loan reviews. Traditional methods rely on individual experience and manual checks, which can introduce variability and human error. A recent Abrigo survey found that individual loan reviewers’ years of experience are trending downward at financial institutions nationwide, potentially adding to this dilemma. Generative AI can help loan review teams by standardizing reviews, applying consistent risk parameters, and identifying patterns that even the most experienced analysts might miss.
AI-driven credit and lending models improve processes by incorporating the following:
- Alternative data – AI gives lending staff a greater capacity to evaluate more data (such as transaction history, social behavior, and cash flow patterns) to improve creditworthiness assessments.
- Dynamic risk tiers – Instead of fixed risk benchmarks, AI creates micro-segmented benchmarks based on real-time borrower data.
- Machine learning models that assess credit risk trends at an institution and can compare a bank’s lending performance to peer data and industry averages.
These credit and lending innovations ultimately free up bank staff for more face-to-face relationship banking and complex cases. They also help communities by giving banks the tools to offer credit to thin-file customers whose lack of banking experience may have caused them to be overlooked by legacy lending and credit models.
3. AI benchmarking for efficient customer service
AI-driven automation allows banks to benchmark operational efficiency across branches, including customer service departments and digital banking channels. AI chatbots establish real-time benchmarks for service speed, adjusting expectations dynamically based on customer demand.
For example, Pentagon Federal Credit Union (PenFed) is leveraging AI to enhance member support and operational efficiency. By integrating a Salesforce Einstein-powered chatbot, PenFed now provides real-time loan application updates, product details, and technical assistance nearly 40,000 times per month. This AI-driven solution resolves 20% of member inquiries on first contact, significantly reducing call center demand. As a result, despite a growing membership base, PenFed has improved response times—cutting the average call wait time by nearly a minute to under 60 seconds.
For many community banks, fast customer service through a real person is a differentiator from the competition. But AI can still help reduce costs while maintaining a high-touch, quality customer service environment. Abrigo client BAC Community Bank uses an app that answers customer questions and matches them with a BAC banker as their assigned contact, fostering personalized relationships.
4. AI-driven product personalization
Behavioral data provides critical insights into how customers engage with their financial institution—whether through mobile apps, branch visits, or digital transactions like transfers and bill payments. By analyzing these interaction patterns, banks can move beyond traditional segmentation and better understand which products and services drive the most value.
Taking a data-driven approach enables financial institutions to personalize offerings, enhance customer engagement, and optimize resource allocation for maximum impact. AI-powered recommendation engines have redefined how banks measure and optimize customer engagement. Instead of broad customer satisfaction scores, AI can create personalized benchmarks, such as:
- Next best action predictions – AI benchmarks customer interactions, recommending tailored products and services.
- Churn risk scoring – AI predicts customer attrition based on behavioral signals, setting early intervention benchmarks for retention.
These AI-driven benchmarks ensure banks attract and retain high-value customers through proactive engagement.
Keeping AI-powered processes ethical and compliant
While AI-driven benchmarking presents immense opportunities, banks must ensure their machine learning tools are vetted as thoroughly as their other platforms. AI models must be continuously audited to prevent biased benchmarking that could lead to unfair lending practices. For regulatory compliance, banks must choose AI tools that will align with financial regulations and update alongside them as regulations evolve. AI-powered processes must also be transparent enough that staff can confidently explain them to regulators and examiners. Lastly, AI benchmarking relies on vast amounts of data, making cybersecurity a critical priority.
By addressing these concerns with vendors and staff, banks and credit unions can use AI benchmarking to drive innovation while maintaining trust. And the tools available to them today are just the beginning. Future advancements might include AI-powered market simulations that predict future financial scenarios and adjust benchmarks proactively or secure data sharing to establish industry-wide AI benchmarks without compromising privacy. Banks that embrace AI-driven, dynamic benchmarking models will enhance profitability and risk management and gain a significant edge in customer satisfaction and operational efficiency.
