Financial Services
In financial services, every data decision eventually has to be explained: to a regulator, an auditor, a board or a customer. Legacy platforms and manual reporting make that explanation expensive.
This work is for Chief Data Officers, CIOs, risk and compliance leaders, finance directors and transformation leads in banks, insurers, lenders, wealth managers and fintech firms.
You gain governed data foundations, reporting with documented lineage, a practical position on AI governance, and a cloud platform sequence that reduces cost and risk rather than adding to it.
Work we have delivered
Our work covers the disciplines a regulated firm cares about most: governed reporting and agreed definitions at DC Thomson, and structured AI readiness with South of Scotland Enterprise.
Data Understood push to learn the nuances of the company, its overall strategy, its people and thus deliver solutions that benefit the company to its core and lay the foundation for a strong future in data.
They provided costed structure and solutions to create an ongoing credible data management capability.
They delivered exactly as per scope and were flexible in the face of environmental challenges. What stood out was the sense of partnership.
Definitions and ownership come before controls
Reporting only becomes defensible once each figure has a named owner and a documented source. That was the first step at DC Thomson and it is the first step in any regulated reporting estate.
AI readiness is an assessment, not an ambition
The South of Scotland Enterprise programme was built around helping organisations judge honestly what they were ready for. The same discipline keeps AI use cases inside a firm’s risk appetite.
Governance survives when people understand it
Both engagements included workshops and upskilling. Controls that are only understood by a central team tend not to hold under audit pressure.
Common challenges
These are the situations we are most often asked about. They are rarely solved by a single tool, because the underlying issues are ownership, lineage and operating model.
Ledger, policy, lending and CRM platforms each hold part of the customer and risk picture. Reconciliation happens in spreadsheets, and the effort to answer a simple question is out of proportion to its value.
Returns and internal reporting often rely on manual steps and rules that live with individuals. When a regulator or auditor asks how a figure was produced, the answer takes weeks to assemble.
Business lines are already testing generative AI in service, underwriting support and operations. Risk, compliance and technology functions need a clear position on acceptable use, oversight and model accountability.
Models and alerts exist, but the operational response, tuning and feedback loop are informal. Detection improves slowly because nobody owns the whole cycle.
A single customer appears differently in each product line. Retention, pricing and next-best-action decisions are made on partial views, which limits both commercial performance and fairness assessment.
Some workloads have moved, some have not, and the interim state adds cost and complexity. Leadership needs a sequence that reduces risk rather than a rebuild that pauses the business.
Data & AI opportunities
We describe opportunities in the terms a finance or risk committee would use.
Regulatory, board and management reporting produced from governed data with documented lineage, so the provenance of a number is available on request rather than reconstructed.
Better feature availability, monitoring and feedback loops around existing models, with a clear operational owner for tuning and false positive management.
Product, service and channel data brought together to support retention, pricing and service decisions, with the controls that customer data in a regulated firm requires.
An agreed view of where AI is appropriate, what human oversight looks like, and how model decisions are recorded, so adoption can proceed without waiting for perfect certainty.
Manual reconciliation, rekeying and report assembly reduced through engineered pipelines, freeing skilled people for analysis and control work.
A sequenced platform plan on Microsoft Fabric or Azure that consolidates duplicated effort and makes the running cost of data visible to the finance function.
Our approach
We use our Discover, Design, Develop, Deploy engagement model, with our DIAlog framework to surface the leadership, governance and cultural factors that determine whether data work lands in a regulated firm.
Trusted reporting means every published figure has a known owner, a documented source, a tested transformation and a repeatable process behind it. It is the precondition for both regulatory confidence and useful AI, because a model inherits the quality of the data it is trained and run on.
Stage 01
We work with the executive team, the data function and second line risk and compliance to understand the commercial objectives, the reporting and regulatory obligations, and where trust in data currently breaks down.
Stage 02
We agree where data and AI should create value first, what governance and lineage evidence is required, and how the target platform and operating model fit inside existing control frameworks.
Stage 03
We build in sequence: governed foundations, then the reporting, risk or customer insight the business is waiting for. Controls, documentation and testing are part of delivery rather than a later exercise.
Stage 04
We embed ownership, monitoring and review rhythms, train your teams on the platform they have inherited, and leave the documentation your auditors and regulators will ask for.
Where to go next
Financial services engagements usually begin with governance or platform work and continue into analytics once the foundations are trusted.
Buying questions
Written for the people who have to make the internal case, covering how this work is run inside a regulated firm.
Whether you're developing a data strategy, modernising your data platform, preparing for AI or tackling a specific business challenge, start with a 30-minute conversation with Inez.
