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Tealeaf is great at identifying suspicious sessions. The best approach is to create a fraud score. This used to be the same thing as you did for struggle but with different events. Could we use the same cognitive engine for struggle score with different events to highlight suspicious behaviours? It would help the claims department of insurance customers as an example.
How will this idea be used?
In exactly the same way as struggle analytics but instead of looking for struggle, looking for suspicious behaviour e.g. people changing fields; going back to change a value to reduce a quote value. Also would work for banking/finance. The idea could be extended as a generic add-on; use the cognitive engine to highlight any event correlation with weighting.
|What is your industry?||Insurance|
|What is the idea priority?||Low|
|Link to original RFE|