From catastrophe modelling to pricing, from bordereaux
management to performance analysis, data preparation underpins the lifecycle of a risk.
Frequently this process is done manually, either in-house by highly-skilled workers or
outsourced to vast teams on the other side of the world.
Your valuable in-house team could be spending their time
on business generating activities.
Less time wrangling spreadsheets, more time acting on insights from your data.
The cost of outsourcing is rising, faster than the skill level.
While outsourced and offshored work used to be effective, shifting economic forces are
now driving diminishing
efficiency gains.
1/
Crowdsourced learning
Data preparation choices made across your organisation inform Quantemplate's cognitive
decision-making models.
2/
Mapping suggestions
Quantemplate makes data mapping suggestions based on learning algorithms.
Manual adjustments are fed back into the decision-making model.
4/
Straight-through processing
Any new data fed into the platform flows straight through, minimising
the need for manual data work.
3/
Automation
Above a defined strength threshold, suggested mappings can be applied automatically,
dramatically accelerating the speed of bringing in new data sources.