Case study 01 · Financial services · 10 months
Ninety-two percent of late payments, prevented.
A consumer-credit platform needed to help customers understand and lift their scores at scale. Algobain built the data pipeline, the ML scoring engine and the controls around both.
Customers could see a score, but not what moved it.
The client wanted a platform that helps people understand, monitor and improve their credit scores, with recommendations they can act on. What stood in the way:
- iUsers did not understand the factors affecting their credit scores.
- iiThere was no single place to monitor credit across multiple bureaus.
- iiiIt was hard to identify the specific actions that would improve credit health.
- ivCredit reports were complex and hard for an ordinary consumer to interpret.
- vNothing warned users ahead of events that would affect their score.
Four workstreams, one senior team.
Credit analysis engine
A system that reads credit reports and turns them into actions a customer can take.
- Integrated the major credit bureau APIs: Experian, Equifax and TransUnion.
- Parsing algorithms that extract and normalise credit data across formats.
- A scoring algorithm that identifies improvement opportunities.
- A machine-learning model that predicts how a score will change.
- A recommendation engine that suggests specific actions.
Real-time monitoring dashboard
A web application for following a score and the factors behind it.
- Responsive dashboard in Next.js and TypeScript.
- Real-time score tracking with historical trends.
- Interactive visualisations built with D3.js and Chart.js.
- A factor breakdown showing the impact of each credit component.
- Secure document upload for manual credit report analysis.
Financial account integration
Secure connections to a customer's accounts for a complete picture.
- Bank account connections through the Plaid API.
- Payment history tracking and analysis.
- A debt-to-income ratio calculator and a credit utilisation optimiser.
- Automated alerts ahead of payment due dates.
Backend and security
The services and controls underneath the product.
- Python services on Flask and FastAPI.
- PostgreSQL for structured financial data, Redis for caching and sessions.
- Encryption for sensitive data and multi-factor authentication.
- Approval gates on every action that touches a customer's account, with a full decision record.
03 · Architecture
From three bureaus to one recommendation.
Bureau data, bank accounts and uploaded reports are parsed into one normalised record. The model predicts what will move the score; anything that would touch an account waits for a named person.
Fig. 1 Reference architecture as built. Component names are generic; client systems are withheld under NDA.
Every recommendation shows the factor it moves and the predicted effect. Actions that change an account are queued for a person, not executed by the model.
The numbers the platform is held to.
| Measure | Result | Basis |
|---|---|---|
| Revenue unlocked for the client's customers | $2.1M+ | Through improved scores. [Measurement window] |
| Users helped | 25K+ | Customers scored and advised by the platform. |
| Late payments prevented | 92% | Due-date alerts and payment reminders. [Baseline and period] |
| User satisfaction | 4.8/5 | [Survey source and sample size] |
Figures from the engagement record. Where the basis of a figure is not yet documented, it is shown as a placeholder rather than invented.
What it runs on.
- Interface
- Next.jsTypeScriptD3.jsChart.js
- Services
- PythonFlaskFastAPI
- Data
- PostgreSQLRedis
- Integrations
- Plaid APICredit bureau APIs
- Controls
- Encryption at restMulti-factor authenticationApproval gates
A scoring system you would have to defend?
Tell us what you are trying to run in production. A senior engineer replies within 48 hours.
- 1Within 48 hoursA senior engineer replies, not a sales desk.
- 2NDA, then a working sessionWe sign first, then look at the system together.
- 3Two-week fixed-fee assessmentWritten roadmap, ROI and build-versus-buy. No lock-in.