The role of agentic AI assistants in corporate banking

The adoption of AI in banking has shifted from conceptual exploration to real-world implementation. A key driver of this transformation is the introduction of agentic AI assistants - artificial intelligence systems designed to autonomously complete tasks and work with banking systems to achieve specific objectives.

Unlike traditional AI systems, which are largely reactive, agentic AI in banking is proactive, capable of making decisions, executing complex workflows and continuously learning from data. AI is particularly impactful in financial services where accuracy, risk management and efficiency are paramount.

Keep reading to understand exactly how agentic AI plays a pivotal role in corporate banking and how Finastra’s solutions support it.

What is agentic AI in banking?

Agentic AI in banking is a specialized form of AI that reaches beyond basic automation. While traditional AI assistants respond to specific commands, agentic AI operates autonomously, setting and achieving objectives without continuous human intervention. This shift enables banks to implement more complex, agentic workflows in AI, streamlining processes across business channels.

As highlighted by The Banker, the implementation of agentic AI tools in banking systems is driving operational efficiency by adjusting workflows based on real-time data, resulting in quicker approvals, more accurate risk assessments and dynamic lending models. Similarly, Citigroup has explored how agentic AI can integrate proactive decision-making and data-driven insights via specific financial operations.

How agentic AI differs from traditional AI assistants

Generative AI is predominantly reactive, generating content based on existing data. Generative AI relies on machine learning models called deep learning models (algorithms that replicate human learning and decision-making processes) and other technologies like robotic processing automation (RPA).

In contrast, agentic AI is inherently proactive. Instead of waiting for specific commands, it autonomously executes tasks, adjusts its actions based on situational data and pursues predefined business objectives. As defined by IBM, agentic AI combines the characteristics of large language models (LLMs) with the accuracy of traditional programming. This ultimately enables such AI systems to achieve goals via technologies like natural language processing (NLP), machine learning, reinforcement learning and knowledge representation. This difference in primary function enables AI agents in banking to function as independent decision-makers, streamlining operations with limited supervision.

Key use cases of agentic AI in corporate banking

Automating complex banking processes

Agentic AI is reshaping operational workflows across banking. In lending, for example, digital platforms are embedding agentic AI to proactively support corporate users in managing complex loan decisions, such as drawdowns, rollovers and repayments, by analyzing real-time financial data, market trends and facility terms.

Through integration with ERP systems and ESG platforms, AI can deliver predictive, data-driven recommendations that optimize loan utilization, reduce interest costs and support sustainability-linked lending. This innovation empowers treasury teams with greater agility and cost efficiency, driving faster, smarter and more strategic decision-making.

Enhancing risk management and compliance

The role of agentic AI assistants in banking extends to risk management, where these systems autonomously monitor financial transactions, detect potential fraud and ensure regulatory adherence. By analyzing real-time data, agentic AI tools can identify anomalies and flag suspicious or fraudulent activities before they escalate, ultimately reinforcing compliance frameworks.

For example, in trade finance, agentic AI can automate end-to-end transaction processing, ensuring timely execution and accurate data management by proactively detecting duplicates and mismatches across systems and managing handoffs like SWIFT and APIs. Using pattern recognition, fuzzy matching and rule-based validation, it enhances operational accuracy, audit readiness and fraud resilience. This autonomous approach reduces costs and minimizes human error in complex financial workflows.

Improving client services with agentic AI assistants

Gartner predicts that, by 2029, agentic AI will autonomously resolve 80% of common customer service issues without human interference, resulting in a 30% reduction in operational costs. Personalized client engagement is a valuable application of agentic AI banker systems. By anticipating client needs, providing bespoke recommendations and proactively resolving issues, stronger client relationships are built.

The role of enterprise data in agentic AI performance

High-quality enterprise data is essential for informed decision-making, error reduction and process efficiency. It’s also pivotal for agentic AI performance.

Data-driven decision-making

The success of agentic AI depends heavily on data quality. By analyzing large datasets, AI systems identify patterns, predict outcomes and make proactive decisions to achieve certain strategic business objectives. Poor data quality can cause AI systems to make inaccurate predictions or risk assessments.

Customization and adaptability

To maximize the impact of agentic AI tools, banks must ensure these systems are flexible enough to adapt to specific operational requirements. Customized AI systems can autonomously tailor their actions to align with business priorities, enhancing overall effectiveness.

Compliance and risk mitigation

Regulatory compliance is an important consideration in the deployment of agentic AI. By leveraging enterprise data, agentic AI systems can monitor transactions, identify violations and mitigate risks associated with fraud or data breaches.

Challenges and considerations in implementing agentic AI

Implementing agentic AI in banking comes with its challenges, including the need for robust data security to minimize breaches, the risk of regulatory fines, and the need to meet governance requirements on data transparency and security. Although agentic AI’s autonomy holds various business cases, its application must adhere to stringent regulations like GDPR, CCPA, AML and Basel III.

System integration is another key consideration. Legacy systems can pose problems for real-time data accumulation and access to API gateways. Additionally, ethical AI practices must be in place to avoid unintended biases in decision-making processes, which could impact client experiences.

Market disruption is another key consideration as the application of agentic AI could reduce employment roles (data entry, compliance, investment, asset management and auditing, for example) and increase the need for industry-wide reskilling.

Future of agentic AI in corporate banking

Looking ahead, the role of agentic AI in corporate finance will grow as AI systems become more sophisticated and capable of autonomous decision-making. Banks that effectively integrate agentic AI tools can expect to gain a competitive advantage by optimizing workflows, reducing costs and enhancing client offerings and experiences.

For a comprehensive look at how AI is shaping the future of banking, refer to our insights on modernizing corporate banking.