Industry
Banking
Banking Services Transformation: Unlocking Exponential Value
Highlights
- BFSI firms struggle to scale AI due to 'semantic debt' from inconsistent data definitions across siloed systems. This slows delivery, increases compliance risks, and prevents AI models from becoming production-ready and defensible.
- Semantic data models (SDMs) resolve this by creating a governed semantic layer that separates business meaning from source data, ensuring consistent, trusted context for enterprise-wide AI applications and overcoming data fragmentation.
- Adopting SDMs enables AI-ready data, delivering tangible RoI through faster insights, stronger governance, and audit-ready evidence. This transforms AI from fragile pilots to a scalable, strategic capability that provides a competitive advantage.
Data model change: The key to scaling AI in BFSI
With the advent of AI, the underlying data models of banking, financial services, and insurance (BFSI) firms are undergoing significant change. Data models can no longer simply store transactions and support reporting, they must help systems understand context and reliably explain decisions.
In BFSI, effective AI adoption needs more than facts. It needs business context: why an event matters, how it relates to entities, obligations, and constraints, and what it means for operational execution.
Yet many data estates remain optimised for transactional integrity and historical analytics. They may support proofs of concept (PoCs), but struggle to scale due to gaps in governance, lineage, and interoperability. Lack of uniformity in definitions across AI systems results in semantic debt, which slows delivery, introduces inconsistencies, increases the compliance burden, and becomes a drag on AI adoption. Recreating logic across use cases often indicates that meaning is not yet fully managed as a shared enterprise asset.
In BFSI, effective AI adoption needs more than facts. It needs business context: why an event matters, how it relates to entities, obligations, and constraints, and what it means for operational execution. A disconnect in these aspects impacts day-to-day operations. For instance, the term ’customer’ may mean an account-holder in retail banking, a legal entity in corporate banking, or a counterparty in capital markets.
Carrying the same gaps into AI systems makes defensible AI harder to achieve, effectively limiting AI RoI because models that cannot be explained, traced, or audited seldom move beyond pilots.
Similarly, the meaning of ’transaction’ and ‘exposure’ may also vary across different sub-functions such as payments, cards, trading, anti-money laundering (AML), and risk. A misalignment in meaning can result in AML systems missing linked accounts, delayed know your customer (KYC) remediation, challenges in defending credit decisions, and increased time spends by regulatory teams to reconstruct lineage for challenged models or reports. Carrying the same gaps into AI systems makes defensible AI harder to achieve, effectively limiting AI RoI because models that cannot be explained, traced, or audited seldom move beyond pilots.
Figure 1: From fragmented data to context-based AI
In our view, semantic data models (SDMs) are the foundation of scalable and governed AI (see Figure 1). SDM creates a governed, machine-readable semantic layer that separates business meaning from source system implementation. By serving as the semantic backbone, it helps move AI from pilots to production-ready AI projects. SDM eliminates silos and supports virtual, materialised, and hybrid deployment models. It embeds governance through validation, lineage, and lifecycle controls. With modern graph platforms and high-performance computing (HPC) systems, SDM can scale to enterprise requirements.
How to build a context layer: The four-phase SDM lifecycle
Effective AI adoption in BFSI demands consistency in the meaning of terms that are reused across risk, compliance, operational, and AI systems, which necessitates a governed, semantic context layer. Such a layer comprises four phases (see Figure 2).
Figure 2: A four-phase SDM lifecycle for BFSI
Phase 1: Semantic contract definition establishes consistent meaning for terms used across high-value domains such as risk, AML, lending, claims, and market surveillance. This ensures business, compliance, reporting, and AI teams work with the same definitions.
Phase 2: Source mapping and transformation links core banking, trading, payments, customer relationship management (CRM), claims, and reporting systems to the shared meaning, creating an auditable trail between source systems and business context.
For banks, insurers, and capital markets firms, SDM adoption delivers the most value when three priorities are addressed from the outset: consistent meaning of terms across the enterprise, governance embedded in the data layer, and audit-ready evidence built into decisions.
Phase 3: Graph construction and persistence turn the semantic contract and mappings into an executable enterprise layer. It makes relationships explicit, helping teams answer questions such as linked-account fraud, beneficial ownership, exposure concentration, customer 360, and market-abuse patterns.
Phase 4: Validation, governance and delivery apply controls before data is consumed. AI decisions thus carry evidence, eliminating the need for manual reconstruction later.
While the business case for SDM is clear, BFSI firms should consider a few adoption priorities upfront. For banks, insurers, and capital markets firms, SDM adoption delivers the most value when three priorities are addressed from the outset: consistent meaning of terms across the enterprise, governance embedded in the data layer, and audit-ready evidence built into decisions.
Consistent meaning: Given BFSI products, entities, and obligations are reused across risk, finance, compliance, operations, and customer channels, creating definitions separately for each programme results in heavy reconciliation effort, duplicate controls, and inconsistent outputs. SDM reduces this friction by defining key business terms as governed financial vocabularies and internal policies, ensuring that the same meaning is reused across products, reports, models, and geographies.
Built-in governance: AI accountability depends on knowing what controls were applied before data was consumed. SDM makes these controls part of the data operating model rather than a separate compliance exercise. Definitions, mappings, ownership, access rules, and change approvals are controlled throughout the process, from the source data to its consumption by AI.
Auditability: When a model risk team, auditor, or regulator challenges an AI-enabled decision, a robust defence must not require a separate evidence-gathering exercise. SDM ensures that the meaning, source, lineage, validation checks, and control history are connected to the decision path. This shortens audit response or defence cycles and improves confidence in AI-enabled decisions.
Real-world RoI: How BFSI firms succeed with contextualised SDMs
SDMs improve AI RoI by reducing repetitive effort across data interpretation, reconciliation, and governance. For BFSI firms, the impact is real: faster insights, broader surveillance coverage, less manual investigation, shorter reconciliation cycles, stronger audit readiness, and faster model adoption. Several BFSI firms are adopting SDM to resolve their data challenges and enable AI-ready data, reaping significant benefits.
Several BFSI firms are adopting SDM to resolve their data challenges and enable AI-ready data, reaping significant benefits.
A large APAC exchange wanted to enhance the pace of detection of market-abuse patterns across high-volume securities trading data. The surveillance team had to identify market manipulations such as wash trading, compensation trades, parking, among other suspicious behaviours in near real-time. The exchange used a knowledge graph underpinned by an SDM to connect trades, orders, instruments, participants, and relationships to strengthen surveillance, create a unified view, and improve response when suspicious activity occurred. With this solution the exchange realised some key benefits:
- More comprehensive abuse coverage by expanding surveillance, beyond trade-only patterns, to layering, spoofing, circular trading, and trade-to-communication linkages, helping investigators connect suspicious orders, executions, entities, and related emails or messages in one review path.
- Around 20–30% reduction in manual review effort by linking trades, entities, instruments, and relationships into a unified view, facilitating faster investigation cycles.
- Reduction in escalation and reporting turnaround by a potential 15-25% driven by better evidence readiness and greater transparency of trading reviews, in turn improving oversight of regulatory compliance processes.
A large US-based financial institution was operating with a fragmented data view of applications, infrastructure, services, and users. Without a unified view of dependencies, root-cause analysis was slow and manual, and maintenance was largely reactive. This increased operational costs, service risk, and exposure during incidents. The institution deployed an SDM with a graph-based architecture, creating a unified, time-aware view of devices, services, users, and dependencies. With this implementation, the financial institution realised some quick wins:
- 30% reduction in the time taken for root-cause analysis.
- Around 15% improvement in incident resolution.
- Improved operational resilience through better change visibility and understanding of impact on service, predictive maintenance and context-led resolution, as well as reduction in downtime.
A global bank was spending significant effort to reconcile business terms across risk, finance, compliance, and analytics platforms. The same instrument, exposure, account, or legal-entity relationship could be interpreted differently across reports and model inputs, necessitating manual checks leading to delayed reporting cycles and repeated rework during model onboarding. The bank implemented a governed SDM to standardise core definitions, connect them to source data, and reuse approved entities and relationships across reporting, analytics, and model workflows. The bank realised other benefits as well:
- Around 20% reduction in manual reconciliation and interpretation effort.
- 15-25% potential increase in model adoption rates.
- Improved traceability of business terms to source data, controls, and approved definitions.
- Better governance and reuse—the shared model scaled to 197 contributors, around 6,400 commits, and 2,400 pull requests.
Becoming future-ready with contextualised architecture
The next frontier of AI in BFSI is not better models, but better business context. As AI moves from advising humans to executing decisions autonomously, BFSI firms that will lead are those whose data infrastructure can carry that trust: consistent definitions, governed lineage, and evidence that survives audit. Firms must build this foundation before semantic debt becomes a structural liability.
Targeting one high-value domain and demonstrating proof of value for a single use case will pave the way for reuse of the semantic contract and priority sources in other domains and AI initiatives, laying the foundation for scaling AI.
BFSI leaders should treat SDM not as a data architecture project but as strategic infrastructure in the AI era, deriving intelligence from the context-aware SDM to underpin precise, defensible, real-time business decisions that generate value. In our view, BFSI firms must adopt a well-defined roadmap to implement an SDM for a high-value domain such as customer and counterparty for AML or KYC, risk exposure for instruments, or policy and claims processing. Targeting one high-value domain and demonstrating proof of value for a single use case will pave the way for reuse of the semantic contract and priority sources in other domains and AI initiatives, laying the foundation for scaling AI. Reusing governed meaning instead of rebuilding it for each new AI initiative significantly helps in scaling AI. However, BFSI firms must act quickly to move from isolated AI pilots to defensible, context-aware intelligence at enterprise scale to gain a competitive edge.