Due to a high number of variables and complexity behind modern machine learning algorithms, it is hard to interpret the reasoning and decisions made by machine learning models. AI Superior can help to overcome this issue. We can provide a tool that gives an explanation either over the whole population or for an individual customer. We work with a wide variety of methods to name a few: Neural Networks, Gradient Boosting, Random Forest.
Case studies
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Insurance · Machine Learning
Telematics-based driver scoring for usage-based insurance
Per tripdriving score and personal discount
A deep learning model that analyses telematic data from drivers’ phones to detect driving behaviour, score each trip and calculate personalised discounts and safe-driving recommendations.
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Credit scoring model for an SME lending company
800+ features from 14 data sources
A machine learning model that predicts borrower default and fully automates underwriting, improving loan portfolio quality and cutting decision time from hours to a fraction of a minute.
Risk estimation model for a medical insurance company
Outperformed statistical baseline models
A neural network model, trained on five consecutive years of historical medical data, that estimates the risk of economic loss so a niche health insurer can optimise its pricing policies.
Player churn prediction for an online gaming platform
11.3% churn rate after new retention strategies
A machine learning model that learns player behaviour during the game and predicts the probability of churn over a given time horizon, so the platform can apply the most relevant retention strategy.
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Share a few details and our AI team will take it from there. Here is what happens next:
- We review your request and reply by email.
- A call with an AI expert to understand your problem, data and goals.
- A clear recommendation: the approach we suggest and a high-level estimate.