Agentic AI has been a transformative catalyst and the financial industry is no exception. Banks use AI agents to execute tasks and decisions and engage with customers on the front line. They have the inherent capacity to learn from interactions, and form decisions through underlying technologies such as machine learning, deep learning, and neural networks. In this article, we will discover banks may use Agentic AI in finance for automated trading, and portfolio management, and offering personalized financial advice.
Laying the foundations of Agentic AI in finance
The technologies at the core of Agentic AI are Machine Learning and Natural Language Processing/ Deep Learning. AI-ML algorithms study data to learn and improve their performance and predictions through repetition. AI agents need this dynamic learning capacity to understand the data that is continuously generated in this domain.
Natural language processing algorithms may have an even bigger role to play here. AI agents are often deployed at customer desks or kiosks, to engage with people in real-time. They must understand and interact in natural, human language. NLP when combined with computational linguistics allows AI agents to work efficiently. They can form meaningful conversations, understand the context (query/grievance/ feedback), and provide appropriate responses.
In some cases, financial companies may use Agentic AI in tandem with Blockchain, to prevent alterations in the data/ nodes. This protects sensitive personal data from malicious intent and maintains its integrity and transparency.
Applications of Agentic AI in Finance
Automated Trading
Traditional trading methods often rely on guesswork and static algorithms, and both of them cannot adapt quickly to market trends. Although we heard of automated trading before, Agentic AI has made it better, by imparting remarkable speed and accuracy to those dynamic algorithms.
JPMorgan Chase and Goldman Sachs use AI tools to assist traders with their decision-making, thereby, reducing the associated risks. This has led to a 30-50% faster trade execution cycle and 20% better algorithmic performance.
Can an AI software development company assist in this case? Yes, by building systems to analyze market trends, macroeconomics, geopolitics, and social sentiment. Banks can work with their partners on algorithms that could minimize human errors by 20-40%.
Portfolio management
Portfolio management used to rely on periodic reviews, which did not leave space for real-time adjustments. With Agentic AI in finance, we now have dynamic and responsive portfolio management systems, that align with clients’ goals and objectives with market sentiments.
When geopolitical tensions rise or economic indicators shift, an AI-driven system can automatically rebalance a retiree’s portfolio to include safer assets, protecting their investments from potential losses. On the other hand, younger investors with a higher risk tolerance can see their portfolios shift toward growth-oriented stocks when their financial situation improves.
Robo-advisors are also evolving thanks to Agentic AI. They can now understand individual circumstances, such as planning for a wedding or saving for a child’s education, and provide personalized recommendations beyond generic financial advice. This tailored approach helps clients feel more valued and confident in the bank’s financial advice.
Personalized financial advice
Agentic AI enhances how banks offer suggestions by integrating advanced analytics into customer relationship management (CRM) systems. This integration allows banks to gather insights about clients’ behaviors and preferences over time.
When a client contacts their advisor at a bank using AI-enhanced CRM tools, the advisor is equipped with relevant information about the client’s recent activities and future goals. If the system detects that a client is planning a home renovation, it can automatically suggest suitable loan options or investment strategies tailored to that specific need.
Furthermore, predictive nudges powered by Agentic AI can help clients manage their finances more effectively. For instance, if the system identifies that a client has been overspending, it can send a friendly reminder or alert: “You’ve spent $200 on streaming services this month; would you like to review your budget?” This proactive approach encourages better financial habits without being intrusive.
Fraud detection
In 2025, we are experiencing advanced cyberattacks, and banks need a tool to prevent those attacks and protect customer assets. Agentic AI in banking is the perfect response, and here’s why. While traditional AI algorithms can identify patterns, Agentic AI can unmask even the slightest hint of malicious intent. An unexpected withdrawal from an unknown location- maybe this requires notification, it’s better to be safe than sorry.
As we know, Agentic AI learns from its experiences and improves its performance. Banks can rely on this feature to understand the subtle attacks and adapt quickly to the situation.
How banks and AI software development companies can work together to mitigate the situation?
- Banks can ask their partners to develop customized, omnichannel transaction monitoring systems
- Develop self-learning algorithms to identify advanced scams, like deep fake scams
- Work on creating advanced security protocols, like multi-factor authentication
Aside from these, banks can also use AI agents to comply with the regulations automatically. They can monitor the areas where the bank is lagging, and notify the authorities to work on compliance.
Augmenting humans with Agentic AI
The debate about AI replacing humans is a no-brainer, because, it has not replaced them in banks using these agents. Instead, the goal is to simply augment customer interactions, by employing these agents to deal with routine tasks. In some cases, AI agents are still relying on human insights for guidance and support. This will lead to smart resource allocation, allowing people to focus on more dynamic and complex tasks.
Upcoming trends in Agentic AI in banking
Currently, banks are using generative AI to drive customer engagement, and Agentic AI to drive hyper-personalization. Aside from these, technologies like blockchain, quantum computing, and completely autonomic systems will shape the future of AI in banking.
Blockchain might improve the current fraud detection systems, and smart contracts might speed up the loan processing time. Quantum computing will be used for risk assessment and management, and to work on financial modeling systems.
In some cases, banks might use blockchain and quantum computing together to work on fraud detection incidents. Lastly, the adoption of ethical AI will ensure that banks work with transparency and develop inclusive systems and algorithms.
Five years from now, Agentic AI and generative AI will be at the heart of banking and financial institutions. If you want to tap into it, you must partner with AI software development companies to raise the standard of operations and customer engagement. These technologies can be utilized for automated trading, portfolio management, fraud detection, and offering personalized services. Also, trends suggest the inclusion of newer technologies, to keep up with evolving market sentiment.
At Chirpn, we have worked with institutions and developed tools to improve their operations and customer satisfaction scores. We understand that no one-size-fits-all approach will work, that’s why align ourselves with our customers’ vision and work accordingly.
