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Applied AI in Banking: How 4 powerful use cases are driving growth

Applied AI is the future of banking

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published September 2025

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“Taking a strategic approach to AI ensures that technology investments align with long-term business goals and integrates AI into decision making. It also creates a culture where both leaders and team members embrace AI as a fundamental capability with the power to enhance their skills.”—BRAD BOYD vp data and AI solutions

1. Intelligent automation for operational efficiency

Banking associates often juggle high-stakes responsibilities with routine administrative tasks, leading to inefficiencies and burnout. Applied AI can streamline burdensome workflows. Automated document processingUse natural language processing (NLP) and optical character recognition (OCR) to process KYC documents, loan forms and compliance reports. Smart email routing and response generationAI models like GPT can draft personalized customer replies, categorize queries and route them intelligently. Transaction and case processingAI bots can validate, verify and process high-volume transactions with fewer errors and faster turnaround times. These actions lead to a measurable reduction in operational costs, decreased error rates and faster service delivery. Delegating these routine tasks to AI enables banking professionals to focus on strategic and high-touch customer interactions.

2. Hyper-personalized customer experience

In a digital economy, customer loyalty hinges on personalization and instant service. Applied AI enables digital twin customer intelligence, providing real-time, contextual support at every touchpoint. Conversational AIAI-driven chatbots and voice assistants are soon expected to resolve up to 80% of customer inquiries autonomously, according to recent Gartner research. This shift will reduce call center volume and improving response times. Augmented human agentsAI copilots boost first-contact resolution by empowering customer service agents and branch representatives with instant access to product manuals, customer history and cross-sell opportunities. Behavioral analyticsAI analyzes spending patterns, product usage and engagement to deliver customized financial advice or product recommendations. This AI-first approach deepens customer engagement and opens up new revenue streams through better product targeting and lifecycle management.

3. Proactive regulatory compliance and audit readiness

Regulatory compliance is a major cost and operational burden for financial institutions. Applied AI enables proactive, scalable compliance solutions that reduce risk exposure. Third-party risk management and internal audits are key areas that can benefit directly as banks must take an enterprise-wide view of risk. AI for anti-money laundering and KYCMachine learning algorithms improve suspicious activity detection and reduce manual false-positive reviews. Audit trail generationAI systems log and tag all actions, ensuring data traceability and transparency for regulators. Real-time regulatory updatesNLP systems ingest regulatory texts and map new rules to existing processes and data flows. By using AI to build adaptive compliance infrastructures, banks can move from reactive compliance to continuous monitoring. This change reduces penalties, improves governance and saves millions in remediation costs.

4. Smarter data insights across credit and origination

Credit decisioning faces key integration challenges and is ripe for disruption. Applied AI models analyze a broader spectrum of customer data to evaluate credit worthiness more accurately and responsibly, without human bias. Alternate data modelingEvaluate non-traditional data such as utility payments, digital wallet activity or e-commerce behavior. Dynamic risk scoringReal-time adjustments to credit models based on behavioral or macroeconomic shifts. Responsible AIAI tracks borrower behavior post-approval, flags early warning signs of default and recommends interventions or product adjustments. These AI tasks lead to better lending decisions, reduced default rates and an opportunity to serve customers and gain additional market share.

Ensuring AI fluency and talent strategy in execution

Each of the above use cases can transform a financial institution’s efficiency and bring an immediate impact. But successfully implementing widescale AI-driven solutions is highly dependent on a bank’s ability to build the proper talent and experience ecosystem. This is increasingly difficult in today’s environment, as emerging technology and a tightening talent pool converge to make the talent landscape ever more competitive. “The challenge isn’t as simple as finding people who are skilled with AI,” said Brad Boyd, vice president of data and AI for Kforce Consulting Solutions. “Leadership faces a complex and multithreaded challenge because not all AI roles are created equal. A perfect storm has arrived where unclear skill pathways are met with increased innovation demand.” An adequate response balances three solutions, Boyd said: accelerating best practice delivery with AI technology, aligning internal talent across projects and augmenting expertise for critical gaps with proven consultants. To solve this, some banks are leaning forward and working with partners to create new roles and modernize their ecosystems to capitalize on the power of applied AI. Examples of emerging roles AI/Data Engineers and ML Ops specialists to build, deploy and monitor models in production AI Analysts to ensure agentic explainability to establish transparency and regulatory compliance AI Governance and Ethics Officers to oversee responsible AI frameworks Additionally, banks are upskilling traditional functions and roles to ensure they are futureproof and serve the bank in the long term. This includes rethinking traditional segmentation of roles and creating hybrid roles for polyskilled individuals, such as finance and data. Examples of upskilling and reskilling As demand for hybrid “finance + data science” profiles grows, banks are investing in internal academies and partnering with universities. Short-form certifications in cloud-based AI platforms (AWS SageMaker, Azure ML) are becoming table stakes.

The banking industry is undergoing a technological transformation—and applied artificial intelligence sits at the heart of it. Financial institutions are embedding AI deep into their digital cores to improve operations, reduce risk, enhance customer experience and stay competitive in an increasingly digital-first world. Applied AI is redefining how banks operate, from intelligent automation to real-time fraud detection and hyper-personalized services. Applied AI refers to the strategic use of artificial intelligence to solve practical, domain-specific problems. Leaders can implement AI systems to handle decision making, automation and predictive modeling. Because of this potential to transform workflows, Gartner predicts GenAI spending to increase 76% in 2025. In banking, AI can be applied to challenges such as monitoring account and card activity, expediting loan origination and credit approvals, and navigating regulatory demands. Thoughtfully applying this technology can enhance customer service, risk management, compliance, operations and investment management. Banks that integrate generative and applied AI into their core platforms will gain significant advantages: Faster product innovation Higher operational agility Lower risk exposure Stronger customer loyalty The financial services industry is highly regulated, extremely innovative and under constant pressure to upscale services and experience. As such, solutions are highly outcome driven. Keeping that in mind, Kforce has identified four powerful key use cases for applied AI in banking. This article provides examples of how banks can immediately apply AI within each of these areas to improve customer satisfaction and increase revenue.

“Leadership faces a complex and multithreaded challenge, because not all AI roles are created equal. A perfect storm has arrived where unclear skill pathways are met with increased innovation demand.” —BRAD BOYD vp data and AI solutions

Gartner

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