FINANCIAL SERVICES · INSURANCE · BANKING AND LENDING · BUILDING SOCIETIES · WEALTH AND INVESTMENT
Your data already meets the regulator’s standard. Decision making still feels hard.
Financial services firms have built governed, auditable data infrastructure over the past decade. The problem was never having the data. It is turning it into evidence, insight and action fast enough to matter, whether you are a bank, insurer or lender running this for your own board and regulator, or a software vendor whose customers expect that same reporting built into the product they log into every day.
Book a free 14 day proof of concept on your own data
No cost. No demo environment. Your data, your first dashboard, before you sign anything.
What makes this different
- Self-service. Explore your own data and build your own view without raising a ticket with a BI team or waiting on an analyst.
- Zero data movement. Panintelligence queries your data where it already lives. Nothing is duplicated into a separate proprietary warehouse.
- Explainable, governed AI. Human in the loop by design, running on a bring your own LLM model, with every output interrogatable rather than black boxed.
- White label embedding. Ships under your brand, not ours, whether that is an internal dashboard or a product your own customers log into.
- Weeks, not months. A 14 day proof of concept on your own data, then a structured go live, not a quarter long project.
- Transparent pricing. Published pricing bands you can see now, not a number that only appears after a contract signature.
In brief
- The problem: management information is assembled by hand across systems that do not share a common definition, so evidencing a good customer outcome takes days rather than minutes.
- The fix: one governed semantic layer, embedded dashboards and explainable AI, deployed in weeks, with UK based support and pricing you can see before you sign anything.
- The number: Ebix Europe modelled 360 to 450 hours a year of reporting time released through automation.
This page speaks to two different problems
Both come from the same root cause, MI and reporting that cannot keep pace with what regulation and customers now expect. Which one is yours determines which parts of this page matter most.
| Running analytics inside your organisation | Embedding analytics inside your product |
|---|---|
| You are a bank, insurer, lender or building society and need one governed view of performance, risk and compliance for your own teams and your own board. | You build the lending, wealth, insurance or claims software other financial services firms run on, and your customers expect reporting inside your platform, not a separate tool. |
| Typically: CFOs, Chief Risk Officers, Heads of Compliance, COOs. | Typically: Heads of Product, CTOs, Product Directors at financial services SaaS vendors. |
Trusted across regulated financial services
- Running it internally: MotoNovo Finance and Aldermore Bank
- Embedding it in their product: Ebix Europe, Mulberry Risk, Davies Group and Eurobase
Built for the people actually making this decision
Four roles tend to be in the room for this kind of purchase, and each is solving a slightly different problem.
| Role | What is keeping them up at night | What changes |
|---|---|---|
| Chief Financial Officer (running it internally) | Board and investor packs still get built from spreadsheets the night before, and no two versions ever quite reconcile. | One governed source of financial MI, refreshed in real time, with a full audit trail behind every number. |
| Chief Risk Officer or Head of Compliance (running it internally) | Consumer Duty evidencing depends on manually assembled reports that cannot show how monitoring actually drove action. | Outcomes monitoring built into the platform, with permissioned, auditable evidence ready before the regulator asks for it. |
| Head of Product, lending or wealth platform (embedding it in a product) | Customers keep asking for better in-app reporting, and it keeps losing to roadmap priorities that ship revenue faster. | White label, embedded dashboards that ship as part of the product, not a separate engineering project. |
| Chief Operating Officer | Claims, underwriting and operations teams each run their own version of the truth, and reconciling them consumes real capacity. | A single semantic layer so every team, and every report, works from the same governed definition of the same metric. |
You built the infrastructure. The gap moved somewhere else.
Financial services firms have invested seriously in governed data infrastructure. Snowflake, Databricks, Postgres and SQL Server sit underneath most core banking, policy administration and lending platforms now, and the investment case for all of it was sound. What has not followed at the same pace is the ability to turn that infrastructure into evidence, insight and action at the speed regulation and customers now expect.
This is not a data problem any more. It is a translation problem, between what the systems hold and what a compliance officer, a customer, or a board member actually needs to see, in a form they can trust and act on immediately.
You are probably living with this if
Running it internally
- Your Consumer Duty board report still starts with someone manually pulling numbers from three or four different systems.
- Two teams can quote a different figure for the same metric, and nobody can quickly explain why they differ.
- Claims, underwriting or lending decisions rely on data that is accurate, but a day or more behind the decision being made.
- Getting a new cut of the data means raising a ticket with an analyst or a central BI team, then waiting your turn in their queue.
- An audit or regulatory request turns into a week of reconciliation rather than an afternoon of extraction.
Embedding it in your product
- Your customers or intermediaries are asking for better reporting inside your product, and it keeps losing to roadmap priorities that ship revenue faster.
- Your engineers are building one off exports or bespoke reports for individual customers instead of core product work.
- You are losing deals or renewals to competitors who can point to stronger in product analytics.
- You need multi tenant analytics where each customer only sees their own data, and building that in house looks like a multi month project.
Where this shows up, by part of the business
- Insurance. Claims oversight and risk signals tend to surface too late to influence the outcome, only in time to report on it. The FCA’s own multi-firm review of insurance outcomes monitoring under the Consumer Duty found continued gaps in how firms turn management information into evidence of good customer outcomes, and noted substandard service levels persisting across parts of the sector.
- Building societies. Member facing decisions and prudential reporting both depend on the same underlying management information, and inconsistency between the two is exactly the kind of gap that produced the £44 million enforcement action against a UK building society for weaknesses in its anti financial crime systems and controls, since that penalty was issued to a building society specifically, not a bank.
- Wealth and investment. Client outcome evidencing and suitability reporting increasingly need the same real time, auditable standard as core banking MI. The FCA’s Consumer Duty focus areas for 2025 to 2026 name vulnerability identification by wealth managers and advice suitability as an explicit supervisory priority, not a background concern.
The cost of leaving it as it is
A £44 million fine is a board level event, not a footnote. A substandard outcomes monitoring finding from the FCA is a supervisory relationship problem that outlasts the review itself. And every hour a compliance team spends reconciling spreadsheets instead of monitoring risk is an hour that risk goes unmonitored somewhere else.
What this will not do
A dashboard does not make a firm Consumer Duty compliant, and no vendor should imply otherwise. Governed, consistent, evidence ready management information supports the case that a firm is monitoring and acting on outcomes properly. It does not replace the judgement, the process, or the culture that Consumer Duty actually requires. Treat any claim that suggests otherwise with the same scepticism you would apply to any other overreach.
What changes with a single governed layer
Panintelligence sits across your existing systems, whichever core banking, policy administration, loan origination or wealth management platform you run, and builds one governed semantic layer on top. A metric means the same thing wherever it is viewed, whether that is a claims handler’s dashboard, a Head of Product’s roadmap review, or a board pack. PiDashboards deliver the day to day view, and because it is self service, a compliance manager or a product owner can build and explore their own view without submitting a request to an analyst or a central BI team. PiReports automate the recurring regulatory and board packs that currently eat a compliance team’s week. PiPredicts brings explainable, human in the loop AI to risk and attrition signals, running on a bring your own LLM model so client data never leaves your environment to reach a third party AI service.
| What matters to you | What Panintelligence does |
|---|---|
| Data sovereignty | Zero data movement. Panintelligence queries your data in place, in your own infrastructure or in PiSaaS. Nothing is duplicated into a separate warehouse. |
| Explainable AI | Predicts runs on a bring your own LLM model, inside your own governance boundary. Every output is interrogatable, not a black box. |
| Traceability | Every figure in every dashboard and report traces back to source through the same governed semantic layer, with a full permissions and access audit trail. |
| UK support | UK based implementation and support teams, in your timezone, with GDPR native data handling as standard rather than an add on. |
Built to sit inside the systems you already run
Panintelligence queries data where it lives rather than moving it, which matters more in financial services than almost anywhere else.
- Core banking and lending platforms, and the policy administration and claims systems that sit alongside them.
- Data warehouses already in place: Snowflake, Databricks, BigQuery, Postgres and SQL Server.
- Single sign on and role based access control that inherits from your existing identity and permissions setup.
- White label embedding into your own customer facing lending, wealth or insurance platform, with no separate login and no third party branding visible.
If you already run a broader BI platform somewhere
Most financial services IT estates already have a general purpose BI platform licensed somewhere, and procurement understandably defaults to extending what is already paid for. That is a reasonable instinct for enterprise wide reporting. It is a poor fit for the specific problem this page describes, which is fast, governed, embeddable analytics purpose built for regulated financial services, rather than a general reporting layer retrofitted to the job. Broader enterprise BI platforms genuinely offer more visualisation depth and ecosystem maturity, which matters for large scale, general purpose reporting teams. Where they tend to be weaker is exactly where this problem lives: deployment measured in months rather than weeks, embedding that requires significant custom development, and pricing that rarely becomes clear until well into a sales cycle.
What this looks like in practice
If you are running this inside your organisation
Consumer Duty and regulatory reporting
- Challenge: outcomes monitoring evidence is assembled manually, late, and inconsistently across product lines.
- How Panintelligence helps: automated, auditable dashboards that track the four Consumer Duty outcomes continuously rather than at reporting deadlines.
- Outcome: board reports that show a continuous evidence trail, not a point in time snapshot assembled under deadline pressure.
Board and investor reporting
- Challenge: board packs are built from spreadsheets, and finance teams rely on engineering support to pull the underlying numbers.
- How Panintelligence helps: self service dashboards let CFOs and COOs generate investor ready packs from live, governed data without an engineering request.
- Outcome: faster reporting cycles and consistent figures, whoever pulls the report and whenever they pull it.
Claims and risk exception monitoring
- Challenge: claims and risk exceptions are visible in the data, but only after they have already affected the customer or the loss ratio.
- How Panintelligence helps: governed, real time dashboards surface exceptions to the team that can act on them, rather than to a report someone reads next week.
- Outcome: earlier intervention, and a defensible evidence trail showing when an exception was identified and what was done about it.
If you are embedding this in your product
Embedded analytics for lending and wealth platforms
- Challenge: customers and intermediaries want better in-app reporting, and building it in house means diverting engineering from the core roadmap.
- How Panintelligence helps: white label, embedded dashboards ship as part of your platform, on the same governed data foundation as your internal reporting.
- Outcome: analytics becomes part of the product and the sales pitch, rather than a recurring support request.
White label reporting for insurance MGAs, brokers and claims platforms
- Challenge: insurers and brokers using your platform need their own governed reporting, but a generic export does not feel like part of your product.
- How Panintelligence helps: multi tenant, white label dashboards give each customer their own isolated view, carrying your brand rather than a third party analytics tool’s.
- Outcome: analytics stops being a recurring support request and becomes a reason customers renew.
What this is worth, in numbers you can check
| Measure | What Panintelligence delivers | How it is calculated |
|---|---|---|
| Time saved | Ebix Europe modelled 360 to 450 hours a year of support reporting time released through automation. | Hours saved multiplied by loaded hourly cost. Customer specific outcome, not a sector wide guarantee. |
| Time to value | Typical financial services deployments go live in weeks, not the months a broader BI platform rollout usually takes. | Time from contract to first live, governed dashboard on your own data. |
| Governance and audit readiness | Every figure traces to source through one governed semantic layer with a full permissions audit trail. | Reduction in time spent reconstructing evidence for an audit or regulatory request. |
| Commercial differentiation | Embedded analytics becomes part of the product for lending and wealth platforms, not a support cost. | Customer and intermediary adoption of in app reporting, and its role in retention and sales conversations. |
Proof, not promises
Running it inside their organisation
- MotoNovo Finance and Aldermore Bank. A PRA and FCA regulated specialist bank and one of the largest motor finance lenders in the UK, part of the FirstRand group.
Embedding it in their product
- Ebix Europe. Insurance broker software provider. Automated reporting was modelled to save an estimated 4 to 5 days per month.
- Mulberry Risk. A managing general agent with delegated underwriting authority. White label analytics is embedded directly in their Ada platform.
- Davies Group. A well known UK insurance claims, loss adjusting and outsourcing group, embedding analytics into the claims platform it provides to insurer clients.
- Eurobase. An insurance and reinsurance software provider, embedding governed reporting into the platform it sells to insurers and brokers.
Your first 90 days
| Stage | What happens |
|---|---|
| Before contract | A free 14 day proof of concept on your own data. We connect to your actual systems and build your first governed dashboard on day one. No demo environment, no hypothetical data set. |
| Day one of contract | Your dedicated Customer Success Manager is assigned. A named person who learns your deployment, your team and your regulatory context. |
| First 90 days | A structured, seven step onboarding programme takes you from contract to production, with your team capable and confident, not dependent on us to run it. |
| Ongoing | Your CSM stays with you. Regular check ins, proactive guidance as new features ship, and a direct line when you need one. |
Questions worth asking before you buy anything
Does our data ever leave our environment?
No. Panintelligence queries data in place, whether that is your own infrastructure or a PiSaaS instance we manage on your behalf. Nothing is duplicated into a separate proprietary warehouse.
Is this suitable for a PRA or FCA regulated firm?
Yes. Governance, permissions and full audit trails are built into the platform rather than bolted on, and UK based support means your implementation and escalation contacts sit in your own timezone and regulatory context.
Do we need an in house BI team to get started?
No. Most customers go live without one. The platform is built for self service, so business users explore and build their own views rather than submitting a request and waiting for someone else to build it. The 14 day proof of concept and structured 90 day onboarding are designed to get your existing team capable and confident, not to create a permanent dependency on specialist BI skills.
How does pricing work for financial services customers?
Pricing is in published bands, whether you are licensing this for internal use or embedding it in a customer facing lending or wealth platform. White labelling is included at every tier, not gated behind a premium plan.
What is the difference between this and a broader enterprise BI platform?
Broader platforms are strong, general purpose tools already licensed inside most financial services IT estates. Panintelligence is purpose built for embedding and for regulated reporting specifically, which typically means weeks rather than months to deploy and pricing you can see before a sales call.