The age of testing is over: AI has moved from pilot projects to powering the core of financial services. With the introduction of Agentic AI technology (or simply AI agents), 2025 is now officially the year when AI has transitioned to delivering “transformation” across the business domain. The impact of AI agents is probably the highest in the sensitive BFSI domain. This year, AI agents will handle an estimated 65% of customer interactions in the BFSI domain.

According to CredAble, Agentic AI can deliver an annual value of $200 to $340 billion in the banking sector. The Agentic AI infrastructure is projected to drive a growth opportunity of $80 billion, at an annual rate of 43%.  These figures highlight not just potential but momentum, the impact of AI in financial services is already real.

Today, AI agents are actively being deployed across the BFSI industry to address critical business needs. Key use cases include:

• Fraud detection
• Customer onboarding and support
• Risk assessment
• Investment advisory

That said, AI can’t work without the right data; without data, AI projects are like a car without an engine. In the highly regulated BFSI industry, accessing real data is often difficult. Synthetic data offers a solution, enabling banks to train and test AI models while overcoming challenges such as data privacy and scarcity. By using synthetic datasets, AI agents in finance can be trained to “simulate” real-life scenarios like fraud detection.

Here’s our insight into how BFSI companies can operationalize AI agents using synthetic data.

Agentic AI in banking – a new paradigm

Agentic AI in banking marks a paradigm shift from “traditional” AI systems to intelligent, autonomous agents capable of contextual self-learning and independent decision-making. According to Gartner, Agentic AI will autonomously address 80% of customer service problems by 2029.

Here are some benefits of AI agents when used in the banking and finance sector:

• Next-generation customer service
  In the modern banking domain, timely responsiveness is a crucial element in customer service. AI-powered agents can transform customer service by understanding the context of the customer query, retrieving the contextual data, and providing a relevant response
• Proactive fraud detection
  When it comes to fraud detection in the BFSI domain, AI agents are powering the mindset change from “passive detection” to “autonomous action.” With the right agent, banks can now identify and mitigate the threat before any damage is done.
• Regulatory compliance management
  With Agentic AI technology, banks can ensure compliance in the ever-changing regulatory landscape. In place of a static rule-based method, AI agents can help banks adapt dynamically to changing regulations in real-time.
• Back-office operations
  Besides addressing customer-facing issues, AI agents can automate back-office operations like trade reconciliations and risk management. This helps in freeing up back-office employees to focus on non-repetitive tasks such as improving customer experience.

Besides these use cases (and more), BFSI companies need Agentic AI models to accurately predict rare events like a major financial fraud or an extreme event like a stock market crash. However, in reality, AI agents are restricted by their smaller data size or imbalanced datasets. Synthetic data generation tools can overcome these limitations by generating “artificial” data modelled on real-world financial data.  This allows banks to safely train AI models, run simulations, and test new systems without exposing sensitive customer information or being limited by scarce datasets.

Operationalizing AI agents using synthetic data

Here’s a strategic framework to operationalize AI agents using synthetic data solutions:

1. Develop a robust data foundation.
   For accurate synthetic data generation, companies need to develop a robust foundation for their real-world data. This foundation enables synthetic data generation tools to accurately capture the statistical properties of the real-world data.
2. Create high-quality synthetic datasets.
   The next step is to create high-quality synthetic datasets using advanced techniques like Generative Adversarial Networks (GANs). This process involves augmenting real data with synthetic data for a larger and unbiased training of AI agents.
3. Identify the high-impact use cases.
   By focusing on low-risk use cases, BFSI companies can safely implement and test Agentic AI models before shifting to more complex applications. To encourage stakeholders, they can align every use case to a business goal, such as financial forecasting or credit approvals. 
4. Monitor the performance of AI agents continuously.
   Through regular pilot trials, BFSI companies can continuously monitor AI agents for any potential issues or performance bottlenecks. Further, they can consider integrating AI agents with existing systems to access data more seamlessly.

Leveraging Onix’s Kingfisher for AI agents

As Onix’s proprietary tool for synthetic data generation, Kingfisher delivers high-quality data to AI agent modelling in the BFSI sector. Right from model testing to AI training, this synthetic data generator can be easily deployed and directly integrated into any business environment.

In our latest eBook, “Synthetic data – the prerequisite for AI agents in BFSI,” we explore why synthetic data matters in the BFSI industry – and how Kingfisher is delivering enterprise-grade synthetic data for Agentic AI models.

If you wish to know about how to utilize our innovative Kingfisher tool, access this eBook today.

Reference links: 

Why Banks Are Banking Big on Agentic AI: The New Brain Behind Lending

Why AI agents for BFSI work while you sip coffee

Agentic AI in Banking: The Next Frontier for Smart Banking