Monzo applies Recurrent Neural Networks to predict customer support questions and detect signup fraud
Monzo's two-person data team had many promising machine learning applications to evaluate but traditional methods required weeks of data manipulation before a model could be trained, making rapid experimentation impractical.
A traditional data exploration approach to fraud detection — manually constructing features and looking at correlations — failed because of too much noise in the data and difficulty finding time-based patterns.
RNNs achieved 30% top-1 and 53% top-3 accuracy predicting customer support question categories, and detected 40% of fraudulent accounts with a 42% false discovery rate.
New ideas can now be tested in one or two days, more than ten times quicker than manually designing signals.
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Frequently asked questions
What did this team achieve with this AI workflow?
RNNs achieved 30% top-1 and 53% top-3 accuracy predicting customer support question categories, and detected 40% of fraudulent accounts with a 42% false discovery rate.
What tools did this team use?
GRU, TFLearn, Tensorflow.
What results were reported?
Support question top-1 accuracy: 30%; Support question top-3 accuracy: 53%; Fraudulent accounts detected: 40%; False discovery rate for fraud detection: 42% (source-reported, not independently verified).
What failed first in this deployment?
A traditional data exploration approach to fraud detection — manually constructing features and looking at correlations — failed because of too much noise in the data and difficulty finding time-based patterns.
How is this customer support AI workflow structured?
User app events recorded → RNN finds temporal signals automatically → Support question category predicted → Proactive help page served → Signup fraud scored.