Agentic AI in Financial Services – Applications, Benefits, and Implementation Insights

Agentic AI – the software systems that pursue objectives independently through reasoned decision-making, judgment, and the use of tools and resources – is at the forefront of artificial intelligence’s (AI) evolution. In financial services, it offers the ideal way to handle complex and variable data.

Which is why, at Saratoga, we are happy to share how we have successfully implemented agentic AI for financial services client. Not only to address these operational demands, but also to unlock all the associated benefits of AI agents. Let’s examine agentic AI’s place in the financial services industry, based on our project experience and industry experts, and offer guidance on adoption going forward.

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Developments in Agentic AI Adoption

Interest in agentic AI continues to grow among enterprises. Gartner forecasts that by 2028, 33% of enterprise software applications will incorporate agentic AI, rising from less than 1% in 2024.

Additionally, 15% of routine work decisions may occur autonomously via agentic AI by 2028. Gartner anticipates an even higher percentage – by 2026, up from under 5% in 2025. These trends point to increased autonomy in operations.

In financial services, agentic AI handles tasks that require flexibility, such as analysing diverse data. It stands apart from conventional AI workflows by emphasising context for independent actions.

Want a deeper look into AI Agents? Read A Guide to AI Agents in 2025

Real-World, Measurable Benefits of Agentic AI in Financial Services

Time savings

Agentic AI yields tangible operational gains. According to a recent report by McKinsey, it can achieve 20% to 40% time savings and reduce backlogs by 30% to 50%. In compliance areas, AI agents lower expenses by minimising manual involvement and accelerating reviews.

A PwC study indicates up to 90% time reductions in core processes, allowing reallocation of resources. In broader contexts, agentic AI cuts human review loads by up to 60% and shortens integration times from days to hours.

Cost savings

Cost efficiencies are evident across applications. Deployments can decrease support costs and overall operational expenses in customer service and fraud management.

Compliance benefits

Risk management and compliance see enhancements too. Agentic AI supports continuous transaction oversight for fraud detection and regulatory adherence, and cuts associated risks and costs. In IT functions backing financial operations, it minimises downtime and automates issue handling, which contributes to additional economies.

These results are consistent with PwC findings on time and cost savings alongside productivity boosts. McKinsey notes that agentic AI extends cost optimisation beyond standard approaches.

Agentic AI Case Study in Financial Services: Automated Document Processing

Agentic AI applies effectively to targeted financial processes. In a project with a fintech client, Saratoga addressed document processing issues. The client managed large volumes of claim documents in multiple formats, which involved lengthy manual efforts that raised costs.

Our agentic AI system solves for that, and now automates extraction, manages inconsistencies and prepares data for verification. This shortens processing durations and grants access to in-depth data at reduced costs relative to legacy tools.

The client enabled trend evaluations and fraud identification, with seamless system integration and human oversight for precision.

Additional applications of AI agents include

• Onboarding and KYC: Accelerates verifications to reduce timelines while upholding standards, potentially from days to minutes.
• Credit Evaluations: Conducts real-time data reviews for reliable lending choices.
• Fraud and AML Detection: Spots irregularities quickly to curb losses.
• Client Support: Provides customised assistance to elevate service quality.

View AI Agent case study here.

Adoption Considerations

Agentic AI is set to expand in financial services to facilitate more autonomous functions. Gartner indicates it will fill gaps in current AI and yield adaptable systems over time. For businesses, this supports improved decisions, productivity, cost savings, and risk controls.

So, where does that leave you and your business? If you have not yet done so, consider an initial AI implementation before progressing to agents. Then employ microservices for modular, scalable setups. This approach suits areas like productivity in the workplace, process automation, and research initiatives.

Ask the Experts at Saratoga for Your Agentic AI Solutions

With locations in Cape Town and the UK, we are dedicated to giving you the full benefits of our decades of experience and expertise, married to the latest tools, trends, and technology. We have built and validated agentic AI to yield cost reductions and enhanced data utilisation.

Speak to us today to explore tailored options for your operations.

 

FAQs

What are AI agents in financial services?

At Saratoga, we define AI agents in finance as autonomous systems designed to handle complex tasks like document processing, fraud detection, and compliance checks by leveraging reasoning and adaptability. These agents integrate with existing financial workflows, such as those we implemented for a leading fintech client, to improve efficiency and accuracy. They represent a shift toward intelligent automation tailored to the unique demands of the financial sector. Speak to the experts at Saratoga for your AI Agents.

How is AI being used in the financial services industry?

AI is being utilised in financial services to streamline operations. Saratoga’s Agentic AI framework automates document processing and data analysis for our clients, significantly reducing manual effort. It also supports fraud detection, customer onboarding, and risk assessment by analysing vast datasets in real time, as demonstrated in our successful fintech case study. This technology enhances decision-making and operational scalability across banking, insurance, and investment firms. Speak to us today about your AI needs.

What are the 5 types of agent in AI?

While AI agent classifications can vary, common types include reactive agents that respond to specific inputs, model-based agents that use internal knowledge, goal-based agents that work toward objectives, utility-based agents that optimise outcomes, and learning agents that improve over time. At Saratoga, we focus on developing goal-based and learning agents, as seen in our fintech solutions, to address dynamic financial challenges effectively. Speak to the experts at Saratoga today about your AI agents.

What is the best AI for financial services?

The “best” AI depends on specific needs, but Saratoga’s Agentic AI stands out due to its proven track record, as evidenced by our successful implementation for a fintech client handling complex claim documents. Our solution offers tailored automation, seamless integration, and adaptability, making it a strong fit for financial services challenges like compliance and fraud detection. Contact us to explore how our tested expertise can meet your requirements.