Case study · Banking & Fintech

Leads management software with predictive scoring

The pipeline reordered itself around intent rather than arrival time, and reps stopped spending their first hour deciding who to call.

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Challenge

A lending team was working its pipeline in submission order. High-intent applicants waited behind enquiries that were never going to convert, and the sales team's own sense of which was which had never been tested against the outcomes.

Approach

We built the leads platform covering capture, routing, activity tracking and reporting, then layered a scoring model trained on historic conversion data on top. The queue is now ordered by likelihood to convert, with the reasons for each score visible so the team can disagree with it and have that disagreement feed back into training.

Outcome
The pipeline reordered itself around intent rather than arrival time, and reps stopped spending their first hour deciding who to call.

More work

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Approach: We built an analyser that parses statements from any layout, classifies each transaction into income, committed spend and discretionary categories, and surfaces the patterns lenders actually underwrite against: salary regularity, existing obligations, returned payments and month-end position. Every derived figure links back to the transactions it came from, so an assessor can audit the number rather than accept it.

68%

reduction

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Outcome

Affordability assessment became a review of a structured summary rather than a manual read of a PDF, and each decision carries the transaction-level trail an auditor asks for.

Sector: Banking & Fintech

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E-commerce application with demand-aware merchandising

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32%

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Outcome

Merchandising decisions moved from a weekly meeting to a daily ranked list, and the stockouts that used to end campaigns early became a pre-campaign warning instead of a post-mortem.

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