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technological transformation in 97 % of London Market insurers .
The gap between data entering the system and it being made available for analysis as part of an application should be as small as possible – and ideally in realtime . That process must also be able to accommodate not just one underwriter , but the plethora of teams and systems across an insurer ’ s infrastructure .
Ultimately , insurers are only as accurate , fast , and informed as the data they have available .
Data capture isn ’ t just financial . Every data point , from a preferred name to a customer service history , can potentially inform us how a customer would like to be treated .
Risk and reward
On the other hand , many insurers are – quite rightly – risk averse . Data transformation demands that they move away from systems that aren ’ t fit for purpose in the modern day , but extracting those systems from your business is tricky . Many , as a result , hold
Richard Jones , VP of Sales , Northern Europe , Confluent on to legacy tech in lieu of an upgrade . They see comprehensive transformation as a long , expensive , complicated process . And what happens if their longterm objectives change ?
For starters , new systems are a huge logistical challenge . They need to integrate with other systems across the business – often requiring a long and thorough audit process . And employees in multiple departments trained on the new technology . Part of the problem in the data
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