About Us
Blog
Careers
Become A Partner
Contact
Connecting People With Technology
Solvios is the leading IT company in the business to give you growth, revenue, and a global platform.
Key Takeaways
In banking, Agentic AI is no longer considered a futuristic technology. Hundreds of financial institutions and banks have already deployed AI agents and achieved measurable results. Over 200 financial institutions have already embraced agentic AI in their workflows. The tangible outcomes that these institutions have already achieved include 30% to 50% manual workload reduction, 60% fewer false alerts for financial fraud, 75% quicker compliance audits and reporting, and a whopping 90% speedier KYC process. So, for the fastest-growing banks, it is already a question of keeping competitive momentum in agentic AI adoption.
But while adopting agentic AI, CIOs need to be more alert about trading off faster adoption with gaps in regulatory frameworks, starting with wrong use cases, not alleviating operational readiness, and missing data governance measures. Let’s explore how CIOs can navigate this fast-evolving space and put a balancing act.
As a marketing label, what agentic AI is projected to the people differs to a great extent from how agentic AI works in the production environment with real tasks, tools, and workflows.
Generative AI that answers queries and generates documents and content based on the user's prompts uses limited reasoning capability. It understands a prompt, reasons once, and returns an output to the user. In contrast, agentic AI works towards an objective, breaks the objective into multiple subtasks, uses specialized agents to execute each task, evaluates the final outcome against the success parameters for the preset objective, and iterates the same process to address the shortcomings until the final evaluation approves it.
For example, for onboarding a new customer, the AI agents can handle the entire workflow, right from the document validation, data extraction, sanction checking, risk quotient evaluation, to escalating edge cases to human experts. All is done without active human intervention.
As we have already mentioned, starting with a wrong use case is a pitfall that any CIO should avoid while adopting agentic AI. Some use cases are pretty obvious, and they have already proven to be the most effective for agentic AI automation. Here we explain three of them.
90% Faster Onboarding, 30% Staff Workload Reduction
The most obvious banking process that can be automated using agentic AI and deliver the fastest result is the KYC process. There are financial institutions that have already implemented AI KYC automation for their customer onboarding and reduced onboarding time by 90% and lowered staff workload by 30%.
The agentic AI architecture for automating the KYC process involves several agents: a dedicated document ingestion agent to parse client information such as identity, address, ownership status, etc., another validation agent for evaluating the data accuracy, a third agent for sanctions checks, a fourth agent for risk assessment based on the extracted customer information, and a fifth agent to create a profile compiling all the information and validation results for the human experts to review.
Agentic AI doesn’t just speed up customer onboarding but also ensures consistency throughout the documentation and validation process. Unlike rule-based systems that flag multiple addresses or recent name changes as potential risk factors, agentic systems reason against context to eliminate false risk flags. Agentic AI for the KYC process ensures optimum accuracy in anomaly detection and streamlined audit cycles.
Another major use case where agentic AI has already proved to be highly effective is financial fraud detection. Almost every bank has some kind of transaction monitoring system in place, and most of these systems rely on rule-based algorithms, resulting in tens of thousands of false positive cases leading to unnecessary fraud investigations.
False positive cases alone consume 42% of fraud investigation budgets. Agentic AI systems capable of contextual reasoning, covering customer history, peer group behavior, interaction patterns, user session signals, and device fingerprints, can easily differentiate real fraudulent activities from instances that trigger false positives,
Typical Agentic AI architecture used for fraud detection involves a dedicated data ingestion agent to collect customer transaction and historical data, another concurrently running risk assessment agent to compare transactions against different risk models, a third agent for contextual analysis of fraudulent account connections, a fourth agent to escalate suspicious cases along with contexts for human review, and a fifth feedback loop agent to return the investigation feedback back into the model for ongoing refinement.
Since regulatory requirements and restrictions are stringent in the banking sector, many banks spend heavily on compliance monitoring. This is one major area where agentic AI can streamline compliance reporting with optimum accuracy. Through agentic AI, banks can reduce the manual workload spent on data extraction, reconciliation of inconsistencies, validation against regulatory frameworks, and report generation.
An agentic AI loop for compliance reporting involves separate agents for data extraction from source systems, data quality validation, reconciliation management, data cleaning based on regulatory frameworks, escalation for human review, report generation, and compiling submissions.
Agentic AI ensures 75% faster compliance reporting, shorter audit cycles, and fully-observable audit trails showing all data sources with respective time-stamps.
Many Agentic AI projects in the banking sector failed because the authorities ventured to build a super-agent with multifaceted capabilities, rather than a squad of well-orchestrated agents. Most successful Agentic AI deployments in the banks fall in the latter category. A squad of specialized agents, each built for reliable execution of a single task, coordinates among themselves like relay-race participants and completes a set of tasks faster with higher precision.
Building this squad-based architecture becomes far easier with a Fintech Software Development Solution that offers pre-built orchestration, seamless integration with core banking systems, and compliance checkpoints from day one.
For example, here’s a case study from McKinsey.
A multinational bank, when deploying Agentic AI for KYC, built a squad of agents instead of building a super-agent handling the entire KYC workflow. Here’s how it built different agent squads for multiple task sets.
Present-day CIOs deploying agentic AI banking solutions with adequate governance measures can aim for a faster timeline. Thanks to the support of cloud-enabled AI platforms like AWS Bedrock, Azure AI, and Google Vertex AI, they can deploy Agentic AI within 4 to 8 months. They don’t need to build the orchestration layer from scratch. Cloud-enabled AI platforms with pre-built agentic frameworks reduce the deployment curve.
A typical agentic AI deployment timeline goes like this:
In the above-mentioned timeline, delays mostly happen in the 3rd and 4th weeks, when data sensitivity is audited and labeled accordingly, compliance requirements and gaps are assessed, and required access controls are decided. This is one major area where AI integration services can help.
An AI integration service can help with:
In banking, regulatory and compliance requirements are becoming stricter. Let’s explore actual regulatory deadlines that every banking CIO must know.
DORA requires explicit audit trails, risk assessment documentation, incident flagging and response protocols, and vendor risk management procedures. The regulation, effective from January 17, 2025, is applicable to all ICT-based banking workflows, including agentic AI systems.
Any bank having operations within the EU must abide by this regulation, which requires transparent documentation, human-in-the-loop oversight, model training and validation documents, and a pre-deployment conformity assessment check. August 26 is the compliance deadline for the EU AI Act.
This amendment from global banking sector regulators focuses on integrating AI risk within the capital requirements. As per the amendment, risk assessment models, credit decisions, and compliance protocols should be explainable and resilient.
As a bank CIO, you must ensure that these regulatory requirements are integrated within the agentic AI solution you are deploying. Retrofitting these requirements later will only delay deployment and increase cost.
Agentic AI is no longer seen as a task automation tool. AI agents are increasingly relied on for complex and high-risk workflows. No wonder, a whopping 57% of executives in the banking sector expect AI agents to play a bigger role in fraud detection, risk assessment, and compliance management within three years.
The toolkit for embedding AI agents into complex workflows is improving at a breakneck speed. Already, leading cloud providers have embedded agentic AI frameworks right into their cloud platforms. The managed Agentic frameworks from cloud providers help organizations develop agentic capabilities faster. Without getting mired in orchestration layer development from scratch, developers can start designing agentic workflows.
To make the compliance adherence curve shorter and easier, we also have compliance tools that help organizations with built-in regulations and compliance trails. Compliance platforms like Sardine, Backbase, and Rezo.AI help us with an out-of-the-box compliance toolkit and architecture.
Instead of reinventing the wheel of agentic AI adoption with a less common use case, understand why most banks start implementing Agentic AI for KYC or transaction monitoring. These banking workflows offer the highest data quality, straightforward compliance requirements, a lower deployment curve, and the quickest ROI impact. Start with an obvious use case where agentic AI can deliver tangible value in quick time.
Agentic AI has already produced eye-popping results for banks and financial institutions, especially for obvious use cases like KYC management, fraud control, and transaction monitoring. In the years to come, we can expect agentic AI to be a staple for more complex banking processes. At this stage, whoever successfully adopts agentic AI first has a clear competitive advantage.
Agentic AI refers to AI systems that pursue a goal on their own. They break it into subtasks, use specialized agents and tools to complete them, check the results, and iterate until the goal is met. Unlike generative AI, which responds to a single prompt, agentic AI can run multi-step workflows such as customer onboarding or fraud investigation, escalating to humans when needed.
Banks mainly use AI agents for KYC and customer onboarding, fraud detection and transaction monitoring, and compliance reporting. These workflows have high data quality, clear rules, and measurable ROI, which makes them good starting points. Banks typically deploy a squad of specialized agents rather than one super-agent.
It can be, if governance is built in from the start. That means human-in-the-loop oversight, audit trails, access controls for agents, data governance, and continuous monitoring for model drift. Retrofitting these controls after deployment is costlier and slower.
Agents handle document ingestion, data validation, sanctions and adverse media checks, risk assessment, and compiling a consolidated profile for human review. Because they reason in context, they can reduce false risk flags (such as a recent name change) and keep documentation consistent. Institutions report significantly faster onboarding and lower staff workload.
Regulators expect transparency and explainability, documented risk assessments, human oversight, traceable audit trails, vendor risk management, and incident response protocols. Frameworks such as DORA and the EU AI Act set these expectations, so banks should design for compliance before deployment rather than after.
We'd love to understand what you want to build. The more context you share, the faster we can give you a useful response not a sales pitch, but a genuine assessment of how we can help and what working together would look like.
Solvios is a leading software development company crafting innovative solutions for businesses. We Specialize in custom software, web & mobile app development, and digital marketing. Partner with us to transform your vision into reality and achieve digital success.
Startups
Fintech
Healthcare
Manufacturing
Marketplaces
Mortgage
Real Estate
Retail
Sports and Fitness
Telecom and IT
Logistic
SaaS Product
About Us
Blog
Careers
Become A Partner
Contact
