Industry
Banking
Banking Services Transformation: Unlocking Exponential Value
Highlights
- Agentic AI has the potential to transform BFSI operations by enabling autonomous, context‑aware decision‑making across areas such as retail and commercial banking, as well as financial advisory.
- However, scaling agentic AI requires more than automation—it demands deep, dynamic business context. The solution lies in building an enterprise context fabric: a semantic, adaptive layer that unifies process, regulatory, domain, and data context across the enterprise.
- By embedding governance, workflows, and domain knowledge into AI architectures, context fabric enables explainable, compliant, and human‑aligned AI decisions. For BFSI firms, operationalising the context fabric is critical to improve the quality of business decisions.
On this page
Overview
In the banking, financial services, and insurance (BFSI) industry, the rapid rise of agentic AI, characterised by the orchestration of autonomous and adaptive workflows, is reshaping core operations across retail and commercial banking as well as wealth management, among others. The shift toward AI-driven reasoning and decision-making marks a critical transition beyond using AI for content creation or responding to customer queries through chatbots. This evolution toward human+AI collaboration with minimal human oversight expedites complex, multi-step tasks such as credit risk assessment, anti-money laundering (AML) monitoring and compliance, and financial advisory.
Indisputably, agentic AI offers unprecedented opportunities to unlock new business value. To drive adoption, BFSI firms are building the capabilities needed to integrate agentic AI into core business processes, while ensuring alignment with evolving AI architecture and data foundations. In BFSI firms, the adoption of agentic AI at scale will demand a rethink of AI strategies and the definition of actionable roadmaps. However, moving beyond traditional automation to create intelligent agents with deep domain knowledge is not easy. While AI excels in analysing massive amounts of structured and unstructured data, it struggles to deliver unified, actionable, real-time intelligence grounded in business context in complex environments.
Task and workflow contexts are rarely static in BFSI applications. To achieve human-like reasoning and decision-making, AI agents must understand dynamic, diverse operating parameters, distinct environmental factors, and interlinks across value chains. To successfully adopt agentic AI, BFSI firms must integrate domain context across AI and data architectures to ensure outcomes are rooted in business context.
Enter enterprise context fabric
For BFSI firms, adoption of agentic AI at scale will necessitate building a context fabric—a flexible, semantic layer that acts as an adaptive mesh to unify process and data across enterprise applications. This fabric helps connect data, systems, and users, building AI agents with the ability to deliver unified intelligence in response to dynamic business conditions.
By seamlessly blending business ontology with metadata and semantic models, the context fabric translates complex data into business-friendly insights. Acting as a responsive glue, it empowers AI agents to reason like expert human analysts, when confronted with evolving business needs. Through continuous learning and feedback, AI agents enhance their contextual understanding to support decisions around prioritisation and execution of tasks. BFSI firms can deploy context-sensitive AI agents in several areas, improving process efficiency and enhancing compliance with both regulatory and organisational policies. However, this will require building a flexible fabric that encompasses process context, embeds regulatory requirements, enterprise policies, domain-specific knowledge, workflow, and business logic, as well as exceptions and overrides. Given the multiple areas where AI agents can be deployed in BFSI, the context across the above five dimensions varies significantly, introducing complexity (see Table 1).
Table 1: Process context fabric sample
The data catalogue and sources underpin the data context layer, supervised by laid down governance policies. Governance frameworks enforce role and activity-based access, ensuring compliant data use. Additional dimensions such as tool specifications, semantic knowledge, as well as historical examples that can be leveraged to validate task completions must be part of the context fabric.
Table 2: Data context fabric sample
Putting it all together
For successful agentic AI adoption, BFSI firms must define a high-level architecture founded on a context fabric to connect the AI layer with enterprise data systems. As BFSI firms scale up investment in enterprise-level data and AI initiatives, the context fabric offers opportunities to credibly infuse domain-sensitivity in agentic AI operations. To align and amplify the business value of outcomes, BFSI firms must outline a strategic, interconnected approach to weave the agentic and data layers into their AI platform. This interconnected approach to operationalising the context fabric must include the following foundational pillars:
- Externalisation: Create an external context layer curating business-specific functional orientation along with underlying data and analytics insights—this layer must be accessible to both the data and agentic layers.
- Harness: Integrate dynamic context into AI agents through the runtime harness across architectural layers.
- Manage: Manage the currency of the context through human-controlled continuous learning from feedback and enterprise knowledge.
By enabling flexible adaptation of business dynamics and technology tools into the behaviour of AI agents, the context fabric enhances agents’ receptivity to domain aspects (see Figure 1).
Figure 1: Key elements of agentic AI architecture
Let us examine the key components of an agentic AI architecture.
- Data and governance platform: This includes data sources, target systems, mappings, transformation logic, and an inventory of data products. It also lays down entitlement rules spanning access and security controls, classification, privacy, and audit logging as well as compliance with regulations around data use.
- Context fabric layer:
- Process and task context: Includes regulatory obligations, enterprise policies, workflow rules, typologies, decision criteria, escalation paths, and human override criteria.
- Data context: Encompasses protocols needed by AI agents to access data sources and associated tools.
- Semantic layer deployment: Comprises a semantic mesh that maps business glossary to both data and process knowledge along with managed application programming interfaces (APIs) for definitions, permitted datasets, tools, precedents, and workflow constraints.
- Agentic automation layer: Specialised agents aligned with BFSI workflows and supported by critique or compliance agents that validate rationale, evidence, and policy conformance before actions are taken.
In the above construct, exceptions and low-confidence actions of agents are routed to human reviewers. Decisions, overrides, outcomes, and new skills enrich the context fabric to improve retrieval and continuous learning. In parallel, data and agent runtime governance enforce prompt and tool policies, control approved actions, and enable monitoring and incident handling to ensure explainable operations. This layered approach lets agents act on a trusted, consistent business context, ensuring decisions are auditable and controllable through policy-driven constraints, critique checks, and human interventions.
The way forward
Deploying AI agents aligned with business goals is key to successful agentic AI adoption in BFSI firms. For this, the behaviour of AI agents must be in sync with the ever-evolving business context and rapid technology advances. At the same time, AI agents must comply with regulations and internal policies while being secure, explainable, and transparent. Here’s where building a context fabric can help, significantly enhancing operational and cost efficiencies. Furthermore, the context fabric improves visibility into operations across the front-, middle-, and back-office, greatly enhancing time-to-decisions and equipping BFSI executives with the ability to quickly adapt to change, in turn unlocking better outcomes. In our view, BFSI firms must act quickly to operationalise the context fabric to benefit from the first-mover advantage.