Financial services firms have plenty of data and very little slack. Regulators want reports on time and to the letter. The board wants a clean picture of risk, capital and client profitability. Trading and portfolio teams want cuts of the same book faster than the risk system can produce them. Our financial services analytics work is built for firms that need all three, and cannot afford the delivery to be slow or approximate.

Where the analytics tends to add most value

The highest value work in most UK financial services firms sits in three areas. First, management information — the monthly board pack, executive dashboards, and the numbers that the CFO, CRO and COO need in front of them without waiting for finance to close the ledger. Second, client and portfolio analytics — assets under management, flows, fee economics, client segmentation, portfolio performance and attribution. Third, regulatory and risk reporting — the numbers that have to reconcile to the ledger, to trading systems and to what has already been submitted to the FCA or PRA.

Power BI is a good tool for the first two and a good reporting layer for the third, provided the underlying data model is designed by someone who understands the reconciliation problem. Analytics that does not reconcile is worse than no analytics at all.

Data platforms that hold up under audit

The pattern we normally recommend for financial services is a Microsoft Fabric or Azure-based lakehouse, with clean bronze, silver and gold layers, lineage documented, and semantic models that carry the definitions of KPIs. Everything is versioned in source control. Numbers on a dashboard can be traced back to a specific source system extract and a specific transformation. Auditors like this. Boards like it more, because they stop being told "the number is different because two systems disagree" halfway through a meeting.

Regulatory reporting and MI, joined up

The most common weakness we see is a firm that has invested heavily in a regulatory reporting platform, and separately invested in a management information stack, with no shared data model between them. This produces reconciliation pain every month end. The fix is not another tool. The fix is a single conformed data layer that both reporting stacks draw from, with the regulatory rules applied as clearly documented transformations rather than as opaque logic buried in a legacy tool.

Client and portfolio analytics

Wealth managers and asset managers ask us the same questions: which clients are most profitable, which relationship managers are producing the growth, which funds are winning and losing money, and how those pictures change under different fee and cost allocation assumptions. A clean client and portfolio data mart, refreshed daily, lets front office and finance work off the same numbers and drops most of the political heat out of quarterly reviews.

Insurance analytics

For UK insurers, the same platform pattern supports loss ratio and combined ratio reporting, claims triangulation, reserving inputs, distribution channel profitability, and renewals analysis. Actuarial teams keep their tooling. The business teams get a version of the numbers they can actually use in operational meetings, refreshed daily rather than at quarter end.

How we work with financial services firms

Discretely. Almost every engagement starts with a defined, scoped piece of MI — a board pack, a client profitability dashboard, a regulatory reconciliation view — with a fixed delivery date. Once that piece is trusted, we extend the underlying model to cover the next reporting need. This is a deliberate strategy: financial services teams have been burned by large data programmes that promised everything and arrived late, and the fastest way to build confidence is by landing something valuable inside six to ten weeks.

Financial services analytics without the noise.