Banking analytics dashboards
Deposits, loan book, product uptake, digital channel usage and branch performance in one view, refreshed daily instead of monthly.
Data Analytics · Finance
Financial Data Analytics for Banking & Insurance
One trusted view of deposits, loans, claims and collections, with forecasts that show where risk and revenue are heading before month-end, not after it.
Overview
Financial data analytics your risk and finance teams can trust
Your finance team rebuilds the same numbers every month. Loan data comes from the core system, claims from the policy administration system, collections from a dialer report and customer details from the CRM. Each department reconciles them differently, so the board pack takes days and the risk committee debates whose figure is right. Financial data analytics fixes the foundations first, then puts current numbers and forecasts in front of the people who decide.
We build pipelines from Temenos, Finastra, Mambu, Guidewire, Duck Creek, Salesforce and your spreadsheets into a governed warehouse such as Snowflake or BigQuery. Definitions are agreed once: what counts as 30 days past due, an active customer or an open claim. Dashboards in Power BI or your preferred tool then show portfolio quality, delinquency roll rates, collections results, claims frequency and severity, loss ratios, channel performance and branch or agent productivity.
On top of clean data, AI financial forecasting projects cash flow, deposit balances, delinquencies and claims volumes, and risk analytics flags concentrations, early-warning signs and unusual patterns for your analysts to investigate. Every figure traces back to its source records, access follows role and row-level permissions, and models are documented so they can be reviewed. If you already capture customer conversations, as AA JoyLand does through the analytics panel we built for its WhatsApp assistant, we bring those signals in as well.
Use cases
AI Data Analytics use cases in Finance
Credit risk and early-warning analytics
Risk analytics that track delinquency trends, concentrations and early-warning signals by segment, so credit teams act before accounts default.
Collections performance analytics
Roll rates, contact rates, promises kept and recoveries by strategy and agent, to show which approach works for which customers.
Insurance analytics
Loss ratios, claims frequency and severity, renewal and lapse rates, and fraud indicators by product, channel and region.
AI financial forecasting
Forecasts of cash flow, deposits, delinquencies and claims volumes, with ranges rather than single numbers, for treasury and planning teams.
Customer and contact-center insight
Groups calls, chats and complaints by reason and sentiment, and links them to churn and product data.
How it works
How it works
- 01Connect your sourcesSecure pipelines pull data from core banking, policy, CRM, collections, contact-center and finance systems.
- 02Agree the definitionsData is cleaned, de-duplicated and modelled against shared definitions, so every team's numbers reconcile.
- 03Build the dashboardsRole-based views for the board, risk, finance, operations, collections and claims, with drill-down to account level where permitted.
- 04Add forecasts and risk signalsForecasting and early-warning models run on the clean data, with their assumptions documented.
- 05Deliver alerts and reportsScheduled reports and threshold alerts reach the people who need to act, by email, Teams or WhatsApp.
Capabilities
Key features
Governed warehouse with lineage
One trusted store for financial, risk and customer data, with every figure traceable to its source.
Role and row-level security
Each user sees only the portfolios, branches or customers they are entitled to, and every query is logged.
Forecasting and early-warning models
Machine-learning forecasts and risk signals built on your own history and reviewed with your analysts.
Natural-language questions
Ask "which branches had the biggest rise in 30-day arrears this quarter?" and get the answer with the chart.
Board and regulatory reporting support
Reconciled datasets and automated data preparation for board packs and regulatory returns your team signs off.
Automated alerts
Thresholds on arrears, claims spikes, liquidity or concentration trigger alerts to the owners.
Integrations
Works with your Finance stack
- Snowflake
- BigQuery
- Databricks
- PostgreSQL
- Power BI
- Tableau
- Temenos
- Salesforce Financial Services Cloud
- Guidewire
- Excel / Google Sheets
Using something else? If it has an API, a database or a webhook, we can connect to it.
Outcomes
Benefits for Finance teams
Faster month-end
Reports build themselves from reconciled data, so finance spends its time on analysis instead of assembly.
Earlier view of risk
Early-warning signals show emerging problems in the portfolio weeks before they appear in month-end figures.
One version of the numbers
Board, risk, finance and operations work from the same definitions and the same data.
Better use of staff
Collections and claims teams focus effort on the accounts and cases where it makes the most difference.
Our work
Related Work
FAQ
AI Data Analytics for Finance: FAQs
What is financial data analytics?
It's the practice of combining data from core, policy, CRM and finance systems to understand performance and predict what comes next. Typical uses are portfolio monitoring, credit risk, collections, claims and cash-flow forecasting.
What does insurance analytics cover?
Usually loss ratios, claims frequency and severity, claims turnaround, renewal and lapse rates, distribution performance and fraud indicators, broken down by product, channel and region.
Can you combine data from our core banking and CRM systems?
Yes. We build secure pipelines from core banking, CRM, collections and contact-center systems into one warehouse, with shared definitions so the numbers finally match across teams.
Can AI forecast cash flow and delinquency accurately?
It depends on the history and how stable your business is. We forecast with ranges, test against past periods before go-live, and show how accurate each forecast has been so planners know how much to rely on it.
How do you protect customer financial data in analytics?
We mask or tokenize personal data where identities aren't needed, apply role and row-level security, encrypt data in transit and at rest, and log every access. Hosting can stay in your own cloud and region.
Do you help with regulatory reporting?
Partly. We build the reconciled datasets and automate the data preparation behind board and regulatory reports. Review, sign-off and submission stay with your finance and compliance teams.
Can we keep using Power BI?
Yes. We can deliver into Power BI, Tableau or the tools your team already uses, or build custom dashboards where you need something more tailored.
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Stop arguing about whose number is right.
Tell us the three questions your board or risk committee asks that take days to answer. We'll show you the pipeline and dashboard that answers them.