In 2024 , AI adoption in the financial sector surged, with 85% of finance chiefs identifying productivity gains as the main driver for AI investment, according to Deloitte. However, only 30% have implemented Agentic AI in finance, revealing a wide gap between interest and execution. This shows both the massive potential and real-world challenges of applying autonomous AI systems to core financial functions.
Some hedge funds, like Bridgewater Associates, are already using Agentic AI systems that autonomously monitor markets, rebalance portfolios, and execute trades—cutting human involvement and boosting speed and accuracy. That’s not just automation—it’s action. The integration of Agentic AI in finance is no longer optional for firms that want to stay sharp and ahead.
In this blog, we explore how Agentic AI is reshaping the financial sector. From automating complex tasks like risk management and fraud detection to enhancing customer service, we’ll look into the key applications of these AI systems.
What is Agentic AI? Agentic AI refers to AI systems that can make decisions and take actions autonomously to achieve a specific goal. Unlike traditional AI, which relies on human instructions, Agentic AI can plan, execute, and learn from experience without constant supervision. These systems are self-driven and capable of refining their strategies over time.
In sectors like finance and customer service, Agentic AI is revolutionizing workflows by automating tasks such as fraud detection , portfolio management, and client support. Its ability to act independently makes it a valuable tool for improving efficiency, reducing human error, and enhancing decision-making across various industries.
Traditional AI vs. Agentic AI Feature Traditional AI Agentic AI Control Human-driven; follows specific instructions set by users Self-directed; can set and pursue its own goals autonomously Task Execution Executes tasks based on pre-defined rules and programming Defines, executes, and adjusts tasks on its own based on real-time input Learning Limited learning, often requires retraining by humans Continuously learns and adapts based on experience or feedback Goal setting Goals are set by humans or pre-programmed Capable of setting its own goals and deciding the best path to achieve them Adaptability Static; requires manual adjustments for changes in conditions Highly adaptable; modifies its behavior based on new data or environmental changes Decision Making Decisions based on fixed algorithms and historical data Makes decisions autonomously, refining strategies over time based on feedback Automation Level Mostly task automation under strict supervision Full task automation with autonomy, requiring little to no human intervention Real-time Action Operates in a controlled or reactive environment Takes proactive actions, even in unpredictable or dynamic situations
How Can Agentic AI Enhance Financial Workflows 1. Improved Operational Efficiency Automation of Repetitive Tasks : Agentic AI automates labor-intensive tasks like transaction monitoring, fraud detection, and compliance reporting to free up human resources for strategic tasks.
For example, for example, predictive maintenance systems powered by Agentic AI reduced equipment downtime by 30%, showcasing its ability to optimize operations autonomously
2. Improved Risk Management Real-time risk assessment: Agentic AI understands borrower solvency and market conditions, allowing institutions to predict both liquidity risks and geopolitical disruptions.
For instance, Investment places leverage AI agents to watch markets and adjust portfolio allocations.
3. Enhanced Fraud Detection Adaptive Fraud Prevention : agentic AI continuously learns from novel techniques deployed by fraudsters and adapts the detection strategy to ensure financial institutions remain ahead of evolving threats.
For instance, banks leverage AI to analyze real-time transactions, detect anomalies that suggest fraudulent behavior, and take swift measures to mitigate financial damages.
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4. Personalized Customer Service Tailored Financial Guidance: Agentic AI can assess customer data and provide personalized financial advice and investment strategies to improve customer satisfaction and loyalty
For instance, robo-advisors utilize AI to provide personalized PPAs according to unique risk profiles for effective financial planning.
5. Regulatory Compliance Automated Compliance Monitoring: By automating compliance processes, Agentic AI helps ensure financial activities align with regulatory requirements, minimizing non-compliance risks.
For example, financial institutions use AI to monitor transactions for money laundering and ensure adherence to KYC standards, automating reporting to regulatory bodies.
6. Innovation and Inclusion Cost-Effective Expansion: Agentic AI allows financial institutions to serve previously unbanked communities at a fraction of the costs through process automation and lowering operational costs.
For instance, Fintechs can utilize AI to provide tailored financial services for underserved populations and improve financial inclusion.
How Are Financial Institutions Leveraging Agentic AI Today? 1. Autonomous Trading Agents These AI-driven systems analyze real-time market data, detect patterns, adjust trading strategies, and execute trades autonomously, reducing latency and often outperforming human traders by learning from market feedback.
Use Cases:
JPMorgan Chase – LOXM : An AI-powered trading algorithm designed to optimize trade execution in global equity markets by leveraging predictive analytics to minimize market impact and improve price efficiency. Goldman Sachs : Utilizes machine learning-driven systems for high-frequency and programmatic trading, employing AI agents to analyze volumes, sentiment, and liquidity to guide trading decisions. 2. Fraud Detection Agents These agents scan millions of transactions in real-time to spot anomalies, adapting quickly to new fraud patterns by updating their models on the fly, thereby reducing false positives and catching threats early.
Use Cases:
PayPal : Deploys AI-powered filters that harness machine learning to assign risk scores to every transaction, contextualizing each transaction and improving fraud detection accuracy]. Wells Fargo : Enhances fraud protection by using machine learning and analytics to identify and prevent fraudulent transactions in real-time, thereby safeguarding customer assets more effectively. 3. Risk Management Agents AI agents assess financial risk across portfolios, markets, and operations, factoring in economic shifts, regulatory changes, and performance metrics to manage exposure and suggest strategy adjustments.
Use Cases:
Morgan Stanley – Debrief: Citigroup : Developed advanced risk analytics scoring engine using AI to streamline the review of high volumes of global trade transactions while ensuring regulatory compliance. Agentic Automation: The Future Of AI-Driven Business Efficiency Explore how agentic automation is driving next-level business efficiency by enabling AI systems to act, decide, and execute with minimal human effort.
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4. Compliance Monitoring Agents These agents ensure that all transactions, processes, and financial activities adhere to local and international laws, flagging irregularities and automatically adjusting frameworks when new regulations are introduced.
Use Cases:
Deutsche Bank – Black Forest : An AI system built to uncover money laundering and tax evasion, operational since 2019, which flags potential criminal patterns using transaction history, client profiles, and external data.
Citibank: Utilizes AI-driven compliance scoring engines to review trade data and match it with regulatory rules, automating compliance reporting, reducing manual errors, and ensuring traceability in audits.
5. Customer Service Agents Agentic AI supports 24/7 client interaction with tailored recommendations, handling transactions, analyzing financial behavior, and assisting with proactive financial planning.
Use Cases:
Bank of America – Erica:
We have developed a suite of AI agents designed to streamline operations and enhance decision-making in the financial sector. Our agents, including Alan, Susan, Mike, and Sally, are tailored to address specific challenges within financial services.
Alan – Legal Document Summarizer Function : Alan efficiently condenses lengthy legal documents into concise summaries, enabling quick understanding and decision-making. Financial Application : In finance, Alan assists in rapidly reviewing contracts, regulatory documents, and compliance reports, ensuring timely and informed decisions. Susan – PII Redactor Function : Susan specializes in identifying and redacting Personally Identifiable Information (PII) from documents, safeguarding sensitive data . Financial Application : Financial institutions handle vast amounts of sensitive customer data . Moreover, Susan ensures that documents such as loan applications and account forms comply with data protection regulations by securely redacting PII. Mike – Quantitative Proofreader Function : Mike meticulously reviews quantitative data, identifying inconsistencies and ensuring accuracy in numerical information. Financial Application : In finance, Mike is invaluable for auditing financial statements, investment analyses, and market reports, ensuring that all numerical data is precise and reliable. 1. Integration Through Data-Driven Decision-Making Agentic AI systems utilize vast amounts of structured and unstructured data from existing financial databases , APIs, and platforms to make informed decisions.
Example:
Portfolio Management: AI agents integrate with investment platforms to analyze market data in real-time, adjust asset allocations dynamically, and optimize returns based on predictive analytics . Fraud Detection: AI agents connect with transaction monitoring systems to autonomously detect anomalies and prevent fraudulent activities by analyzing patterns across large datasets. 2. Leveraging Existing Infrastructure for Automation Agentic AI integrates with traditional financial tools like enterprise resource planning (ERP) systems, customer relationship management (CRM) platforms, and compliance software to automate workflows.
Applications:
Regulatory Compliance: AI agents integrate compliance monitoring tools to automatically track regulatory changes, flag issues, and generate reports. Expense Management: Agents automate expense reporting by connecting with ERP systems to process invoices and ensure compliance. 3. Enhancing Customer Interaction Agentic AI integrates with customer-facing tools such as chatbots , mobile banking apps, and robo-advisors to deliver personalized financial services.
Examples:
Bank of America’s Erica: Erica integrates with banking systems to provide proactive financial advice and execute transactions autonomously. Robo-Advisors: Agentic AI-powered advisors interact with investment platforms to tailor strategies based on individual risk profiles and market conditions. Agentic AI: How Autonomous AI Systems Are Reshaping Technology Explore how autonomous AI systems are changing the way technology works, making decisions and taking actions without human input.
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4. Advanced Risk Assessment Agentic AI connects with risk management systems to autonomously assess creditworthiness, monitor borrower behavior, and predict future risks.
Use Cases:
Morgan Stanley: AI agents integrate with credit risk models to continuously evaluate borrower solvency and adjust lending terms accordingly. Sophisticated Stress Testing: Agents perform scenario-based analyses by integrating with financial modeling tools to predict risks under various economic conditions. 5. Blockchain for Secure Transactions Agentic AI leverages blockchain technology to enhance security and transparency in financial transactions.
Integration Features:
Decentralized ledgers ensure secure data exchange between agentic AI systems and financial platforms. Fraud prevention is strengthened by integrating blockchain-based transaction monitoring systems . 6. Intelligent Workflow Management Agentic AI integrates into workflow management tools to streamline operations across departments such as finance, HR, and customer service.
Applications:
Automating invoice processing by connecting with shared services platforms. Managing due diligence workflows in mergers or acquisitions through integration with legal and compliance tools.
What are the Challenges in Adopting Agentic AI for Finance? 1. Systemic Risks Market Volatility : Agentic AI can exacerbate systemic risks due to synchronized decision-making by multiple AI agents, potentially leading to herding behavior and sudden market swings. AI-Driven Automation Risks: Increased automation in market transactions may lead to unintended consequences, such as cascading failures during high-frequency trading events. 2. Governance and Regulation Lack of Regulatory Frameworks: Current regulations are not fully equipped to address the complexities of autonomous AI systems. Transparency Issues : The opaque nature of agentic AI decision-making makes it difficult for stakeholders to understand how decisions are made, raising concerns about fairness and trust. 3. Privacy and Cybersecurity Concerns Data Privacy Risks:Cybersecurity Vulnerabilities: Autonomous systems introduce new attack vectors, such as adversarial inputs designed to manipulate AI decision-making. 4. Labour Market Disruption Job Reductions: Agentic AI automates tasks such as compliance checks, investment management, and auditing, potentially displacing human workers. Workforce Adaptation: Employees must be retrained to collaborate effectively with agentic systems and transition into higher-value roles requiring strategic thinking 5. Infrastructure Readiness Technological Upgrades: Existing legacy systems may lack the capability to support agentic AI’s advanced requirements for real-time data processing and decision-making. Cost of Implementation: The upfront investment in hardware, software, and training can be prohibitive for smaller institutions or emerging markets.Compare AI Agents and AI Assistants to determine which technology best suits your business needs and drives optimal results.
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Key Considerations for Adopting Agentic AI 1. Ethical Frameworks Develop robust governance structures that ensure ethical use of agentic AI: Address biases in decision-making processes (e.g., credit underwriting ). Ensure fairness in customer interactions by maintaining transparency in how decisions are made 2. Human Oversight Implement a “human above the loop” approach where humans retain ultimate accountability for high-stakes decisions made by agentic AI systems: Example: Financial institutions should ensure human intervention in cases involving regulatory compliance or large-scale investments 3. Explainability Prioritize explainability by designing agentic AI systems that provide clear insights into their decision-making processes: Example: Use visual dashboards or interpretable models to help stakeholders understand why certain decisions were made 4. Collaboration with Regulators Work closely with regulatory bodies to develop frameworks that address the unique challenges posed by autonomous systems: Example: Collaborate on standards for transparency, accountability, and cybersecurity measures specific to agentic AI 5. Financial Inclusion Leverage agentic AI to advance financial inclusion by reaching underserved communities Example: FinTechs can use agentic AI for micro-loan assessments based on local data or create personalized mobile banking solutions tailored to individual needs without requiring traditional credit histories.
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By developing industry-specific models, we help organizations automate complex processes, make data-driven decisions, and secure a competitive edge. Whether it’s improving financial forecasting, enhancing customer experiences in retail, optimizing manufacturing workflows, or advancing healthcare, Kanerika’s AI solutions are flexible and designed to tackle unique challenges. Our dedication to client success has earned us a reputation as a leader in AI, trusted by companies to transform their operations and achieve measurable improvements through intelligent automation and analytics.
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Frequently Asked Questions (FAQs) What is the use of Agentic AI in banking? What is an example of an Agentic AI? A prominent example of Agentic AI is JPMorgan Chase’s LOXM , a trading algorithm that autonomously executes trades based on real-time market analysis, optimizing portfolio performance and reducing human intervention
What type of AI is used in finance? In finance, machine learning (ML) , predictive analytics , and natural language processing (NLP) are commonly used. These AI technologies help in tasks such as fraud detection, financial forecasting, customer interaction, and data analysis, providing actionable insights.
What are Agentic AI systems? Agentic AI systems are autonomous AI agents designed to perform tasks and make decisions on their own, without continuous human input. These systems can analyze data, learn from past actions, and adapt to new information in real time to achieve specific goals.
What is the difference between generative AI and Agentic AI? Generative AI focuses on creating new content , such as text, images, or audio, based on learned patterns. Agentic AI , on the other hand, is focused on automating decision-making processes and carrying out tasks autonomously in response to specific objectives , such as fraud detection or risk management.
What is the use of AI in financial services? AI in financial services helps with automating routine tasks, enhancing fraud detection, improving risk management, personalizing customer services, and streamlining compliance processes. AI systems help financial institutions improve operational efficiency, reduce costs, and offer better services to clients.
What is the difference between ChatGPT and Agentic AI? ChatGPT is a conversational AI based on natural language processing , designed to generate human-like responses in a chat. Agentic AI , however, is more goal-oriented, capable of autonomously making decisions and performing tasks based on data analysis, like fraud detection, financial predictions, or automated trading
How can financial institutions benefit from Agentic AI? Financial institutions benefit from Agentic AI by automating complex decision-making, improving accuracy in risk assessments, streamlining customer service, and reducing operational costs. This allows firms to operate more efficiently, make data-driven decisions, and stay competitive in a rapidly changing financial landscape.
