Finance

Financial institutions are rarely short of data. The hard part is deciding which question to ask.

Defining the decision first

We start by defining the decision itself: which customer, through which channel, with which offer? Models built before that question is settled tend to work well and go unused.

Where we work

Customer segmentation and behavioural modelling. Grouping customers by what they are likely to do next rather than by what they have done.

Channel and campaign effectiveness. Measuring what a campaign actually produced, after filtering out the transactions that would have happened anyway.

Process and operations analytics. Making visible where operational processes stall and how long each step takes.

Reporting infrastructure. A reporting layer where a number can be traced and its refresh time is known.

Data sensitivity

In finance, data is as sensitive as it is plentiful. In the structures we build, where personal data sits, at which layer it is masked and who can reach it are defined upfront. Regulation requires this too, but we would do it anyway as a matter of good engineering.