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Category: Finance and Insurance

  • Automating UNSPSC Expense Classification

    Automating UNSPSC Expense Classification

    Automating UNSPSC Expense Classification

    Automating UNSPSC Expense Classification

    Overview

    We worked with the Department of Finance to automate mapping expenses to United Nations Standard Products and Services Code (UNSPSC) with a bespoke machine learning model, delivering high-accuracy classifications and freeing teams from manual coding.

    Problem

    Analysts spent significant time reading expense descriptions and assigning precise UNSPSC codes. Existing manual processes slowed throughput and introduced inconsistency.

    Solution

    • Custom ML classifier: Automated categorisation of financial transactions to UNSPSC codes.
    • Technique blend: Combined text processing, a custom learning mechanism, and semantic matching to improve coverage for future cases.
    • Accuracy focus: Tailored to the department’s data to maximise precision.

    Result

    • Over 99% classification accuracy, a 15% uplift versus out-of-the-box machine learning techniques.
    • Productivity and cost savings from automating a previously manual task.

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  • Credit Card Fraud Detection

    Credit Card Fraud Detection

    Credit Card Fraud Detection

    Credit Card Fraud Detection

    Overview

    We worked with a university finance department to identify fraudulent corporate credit card transactions by building a bespoke, unsupervised pattern-discovery system that learns normal behaviour and flags meaningful deviations.

    Problem

    Periodic consultant reports and rule-based alerts were in place, yet a fraudster managed to bypass these controls for an extended period of time. The team needed a more accurate and timely way to detect suspicious activity.

    Solution

    • Unsupervised pattern discovery: Instead of pre-coded rules, the system learns the norm from cardholder data and surfaces deviations.
    • Risk-based ranking: Cardholders are ranked according to the number and utility of deviating patterns identified in their transactions, helping analysts focus on the highest-risk profiles first.

    Result

    • The prosecuted fraudster ranked second, with all suspicious patterns correctly identified, which made the behaviour easy to spot.
    • When run periodically, the tool provides peace of mind and early alerts to deviating patterns, enabling detection before significant losses occur.

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