Artificial intelligence is reshaping how banks and financial institutions manage risk, serve customers, and run operations. The entire industry is in a race to see how quickly and profoundly this cluster of innovation can transform businesses and accelerate growth. However, firms are discovering that it is their data rather than the sophistication of the AI models that limits successful adoption.
Unlocking AI at scale requires firms to move from being ‘data rich’ to genuinely ‘AI ready’. That implies unifying meaning across the organisation through shared semantics and a common business language, making data accessible through controlled services rather than ad hoc extracts, and ensuring it is trusted and traceable through demonstrable quality, lineage, fairness and compliance.
Deployment across highly regulated, legacy-heavy environments is possible – but only with the proper strategic framework.
Fragmented systems result in a fractured data landscape
Financial institutions carry their history inside themselves. Decades of infrastructure sit embedded in their technology stacks. Core banking and payments systems are the obvious examples, but the same sediment runs through trade finance, treasury, CRM, risk engines and regulatory reporting. Some systems even pre-date the institutions that run them today, inherited through mergers like geological layers built up over time.
Each layer comes with its own data model, identifiers and business definitions. A “customer,” an “exposure” or a “default” can mean one thing in retail and something else entirely in corporate or risk. A credit model might be trained on one definition and monitored on another. Clients can appear as different people in deposit, lending and wealth platforms because no single master record exists. This leads to higher false positives, weaker early-warning signals and a warped view of the business.
Access is often just as fragmented. Teams rely on one-off extracts, point-to-point feeds and shadow databases stitched together under time pressure. One upstream schema change can quietly break a model and undermine trust in the whole programme. Without visibility into data quality or drift, models absorb stale or biased inputs. In regulated areas such as underwriting or collections this creates direct model-risk and regulatory exposure (which means AI can’t be deployed until these threats are neutralised).
Even the most sophisticated algorithm is learning from an uneven picture of reality. The institution’s own history becomes a source of friction, and every decision built on that foundation inherits the same cracks.
Identifying and Addressing Data Gaps
Most institutions treat data trust as a vague aspiration rather than something measurable. A resilient data foundation reverses that. It assigns trust scores to critical datasets and defines clear expectations for completeness, accuracy, timeliness and consistency. Automated checks then enforce those standards across KYC, collateral, exposures, limits, complaints and other high-risk domains.
Tracing data through the organisation is just as important. End-to-end lineage – from source systems through transformations, feature stores, models and ultimately decisions – lets teams pinpoint where a model went wrong and why. It is also the backbone of explainability and auditability under regimes such as BCBS 239, the EU AI Act and supervisory expectations from the OCC and CFPB.
Institutions also need context, not just controls. Catalogues, glossaries and semantic layers give models and developers a shared language: what a field means, the range of acceptable values, its sensitivity and the circumstances in which it can be used. This is critical for AI use-cases, where the models bring no reserve of heuristic knowledge about how data is to be used and interpreted. AI solutions need metadata to guide them.
Once the gaps are made visible, the next task is to enrich the data landscape so that models see a fuller and more consistent picture. The first step is internal. Deposits, lending, cards, trade systems and digital channels all hold fragments of the same customer. When those fragments are linked into coherent customer, counterparty or household entities, institutions get stronger risk aggregation, sharper fraud detection and more reliable customer analytics.
External sources play a similar role. Bureau data, KYC and KYB feeds, ESG scores, macro indicators and firmographic datasets help fill in the blind spots that appear with thin-file customers, complex counterparties or emerging markets. Combined, these signals form a more complete foundation for decision-making.
The final layer is reuse. Curated feature stores anchored in a semantic layer allow teams to work from the same set of trusted variables – utilisation ratios, behavioural scores, payment patterns and the like – rather than reinventing logic for every new model. It reduces inconsistency, speeds development and ensures that models across the organisation are trained on the same underlying definitions.
The role of synthetic data
Synthetic data is artificially generated information that reproduces the statistical patterns of real datasets without exposing any actual customer records. It becomes most valuable when real data is sparse, highly sensitive or heavily regulated, because it gives teams a way to build and test models without depending on limited or restricted history.
Its first use is filling the gaps that real-world experience can’t supply. Stress-testing, fraud detection and early-warning systems rarely have enough examples of extreme events to train on. Synthetic datasets let institutions create those missing scenarios and see how their models behave under pressure, as long as the generation process includes controls to prevent the amplification of existing bias.
Its second use is safe experimentation. Retail banking, wealth and insurance models are built on datasets with personal identifying information that can’t be shared freely across teams. Synthetic versions provide a sandbox that behaves like the real environment without revealing identities or triggering additional compliance hurdles. It allows engineers to prototype quickly while staying inside privacy boundaries.
Conclusion
Closing data gaps gives institutions a single, dependable view based on their own operations. Shared meaning, governed access and routine enrichment turn scattered inputs into a coherent asset that doesn’t collapse under pressure. The benefits accumulate – stable pipelines, fewer surprises in production, and analysis that matches what actually happens in the business. Models built on this behave predictably because the ground underneath them is solid. This is the critical work that decides whether AI becomes a constant maintenance burden or a practical extension of existing analytics. It moves the organisation into a position where new tools can be used without fighting the past.
