You business handles hundreds or thousands of transactions per day but some of these may not fit with the expected pattern of behaviour of the customer and require further investigation.
How can you identify suspicious transactional activity so that you're able to protect your customers from criminal activity and cybercrime?
We build fraud detection models that tailored are tailored to your individual use case - whether this is identifying hacked accounts, fake accounts or transactional inconsistencies.
The models use machine learning to learn which features of customer behaviour or transactional history strongly indicate that the account needs flagging as a risk and output a score that represents fraud likelihood.
We implement the system into your existing business as usual processes and create a trigger mechanism or front-end dashboard so that your team can take action against fraudulent activity before it's too late.
Transactional data, labelled as fraudulent / non-fraudulent
Customer behaviour data (e.g. clickstream)
1. A model trained on historical data that continually assesses accounts for fraudulent activity, and outputs a score between 0 and 1 for each account.
2. A trigger than monitors the scores and sends an action (e.g. email or updates a dashboard) if a particular account scores above the threshold.
3. Analysis of the most important features that indicate fraud and identify theft.
Your customers' accounts will be protected by a system that can catch identify theft early and your company won't lose out from the many kinds of payment tricks fraudsters can use to attack your business.