Reports take days to reconcile
Finance teams match numbers across ledgers, payment platforms and loan systems by hand before every close.
Impact: Slower month-end, more analyst hours spent on reconciliation, and late decisions.
Financial services data analytics
Unify Financial Data and Automate Reporting Across Your Systems
Core banking, payments, lending, reconciliation and risk data often sit in separate systems with different definitions. We design governed data platforms that give finance, risk and operations one reconciled view, and reporting that does not depend on manual spreadsheets.
Why reconciliation keeps failing
Four systems, four versions of the same number
Financial data is among the most controlled data in any organisation, and among the most fragmented. Core banking, payments, lending and the general ledger each hold part of the picture, and each uses its own codes, timing and definitions. When finance, risk and operations pull numbers from these systems separately, the figures rarely agree, and the effort spent explaining differences grows with every close.
We design a governed financial data platform that brings these sources together under agreed definitions. Reconciliation rules run on every load, exceptions are routed to named owners, and each reported figure can be traced to its source. For teams already running reconciliation automation, the platform becomes the reliable base underneath it. Institutions that also need fraud and anomaly detection can use the same governed data for those models.
Where the time goes
The reporting problems finance and risk teams live with
Different teams report different numbers
Risk, finance and operations define the same metric differently, so leadership receives conflicting figures.
Impact: Lost trust in dashboards and repeated debates in management meetings.
Risk data arrives too late
Batch loads run overnight or weekly, so exposure, delinquency and unusual activity are visible only after the fact.
Impact: Slower response to credit deterioration and to unusual transactions.
Regulatory reporting is labour-intensive
Reports are assembled from extracts, with little lineage showing where each figure came from.
Impact: High effort per filing and weak audit trails when reviewers ask questions.
Source systems
Which systems the platform connects
We confirm what each system exposes, how often it can be refreshed and who owns the data, before the design is final.
| System | What it provides |
|---|---|
| Core banking and loan systems | Accounts, loans, repayments and balances |
| Payment and card platforms | Transactions, settlements and chargebacks |
| General ledger and ERP | Journals, cost centres and close data |
| CRM and onboarding tools | Customer and relationship context |
| Risk and collections systems | Exposure, scoring and recovery status |
Delivery plan
Five steps, starting with the reconciliation that hurts most
Each phase ends with a defined output. Timelines are estimated after the assessment, once sources and scope are confirmed.
- 1
Source and definition audit
We map the core, payment and ledger sources, confirm refresh options, and agree the definitions that finance and risk use for each key metric.
Output: Source inventory and metric dictionary
- 2
Reconciliation design
We define the matching keys, tolerances and exception rules for each reconciliation, and decide which breaks need human review.
Output: Reconciliation rule book
- 3
Pipeline and model build
We build the scheduled and change-based loads, the financial data model and the control totals that check every run.
Output: Working data model with control checks
- 4
Reporting and access
We publish certified dashboards and scheduled report packs, with role-based access for each team.
Output: Dashboards and access matrix
- 5
Run, monitor and improve
We monitor data freshness and exception ageing, and refine rules after each close with the finance team.
Output: Monitoring pack and improvement log
Platform design
A controlled path from ledger to dashboard
Ingestion
Scheduled and change-based pipelines from core, payment and ledger systems, with reconciliation checks at load.
Storage and modelling
A warehouse or lakehouse with governed financial data models, agreed metric definitions and historical snapshots.
Controls and lineage
Data quality rules, reconciliation totals and lineage so every reported figure can be traced to its source.
Serving
Certified dashboards for finance, risk and management, plus scheduled report outputs for regulatory packs.
Controls
Access, lineage and audit built in
Controls are agreed for each project, based on the sensitivity of the data and the rules that apply to your organisation.
- Role-based access to customer and account data
- Masking of personal identifiers in non-production environments
- Retention rules aligned with your record-keeping policy
- Audit logs for changes to metric definitions and reports
- Review of applicable data protection and financial regulations for your jurisdiction
Success measures
How a faster, more reliable close is measured
Each measure is baselined before the project starts, so improvement is shown against your own starting point.
| Measure | How it is calculated | Why it matters |
|---|---|---|
| Days to close | Working days from period end to final reconciled reports | Shows how quickly the business can act on its numbers |
| Unmatched item count and age | Open reconciliation exceptions, grouped by age and owner | Shows whether breaks are being resolved or accumulating |
| Analyst hours per close | Manual hours spent on reconciliation and report preparation | Measures the effort released for analysis |
| Metric agreement | Share of key metrics that match across finance, risk and operations | Shows whether teams are working from one set of numbers |
| Data freshness | Time between a source transaction and its appearance in reports | Shows whether risk and exposure views are current enough to act on |
Illustrative scenario
Month-end reconciliation: illustrative
This is an illustrative scenario, not a verified client result. Measured outcomes are shared only from real projects, with their baseline and method.
The challenge
A lender reconciles loan repayments, payment settlements and ledger entries across three systems. The process depends on spreadsheets and runs for several days after each close.
The approach
- Connect the three sources with scheduled loads and reconciliation totals
- Model agreed definitions for repayments, settlements and balances
- Publish a reconciliation dashboard that flags unmatched items by age and owner
- Track exceptions in a queue so analysts work only on unmatched records
What we measure: Days to close, unmatched item count and age, analyst hours per close. Measured against a baseline taken before the project starts.
What you receive
Controlled outputs for finance, risk and audit
- Reconciled finance data model with agreed metric definitions
- Automated month-end reconciliation reports
- Risk and exposure dashboards with defined refresh schedules
- Data quality monitoring and exception queues
- Documentation of lineage and report logic
Questions finance leaders ask
Financial data analytics: FAQs
What does a financial services data analytics project usually start with?+
A data audit of the core, payment and ledger sources, followed by agreement on the metric definitions that finance and risk need. That agreement is what makes the reports trustworthy.
Can you automate reconciliation between our ledger and payment platforms?+
Where the systems expose the data we need through extracts, APIs or database access, reconciliation rules can be automated with exception queues for unmatched items. Feasibility is confirmed during the assessment.
Does the platform support regulatory reporting?+
It can produce the data and scheduled reports that regulatory packs need, with lineage for audit. The reporting obligations themselves depend on your jurisdiction and should be reviewed with your compliance team.
How do you protect customer data?+
With role-based access, masking of personal identifiers outside production, retention rules and audit logs. The controls are agreed for your environment before build starts.
How long does a financial data platform take to deliver first value?+
A first reconciliation or reporting use case is usually the fastest route to visible value. The timeline depends on source access, the number of systems and the testing your finance team requires, and it is estimated after the assessment.
Will this replace our existing reporting tools?+
Not necessarily. The platform provides the governed data and calculations. Existing BI tools can often continue to be used on top of it, where they meet your access and performance requirements.
Find out where your reporting delays come from
A data platform assessment maps your systems, the reports that matter and the decisions they support, then gives you a practical first step.