For all the enthusiasm for predictive analytics and its perceived benefits, the plans of life insurers in the Americas will inevitably involve growing volumes of data and require greater processing power to handle the associated analysis. As it stands, these would stretch many carriers’ in-house IT facilities.
Many insurers of all sizes are one step ahead, already exploring solutions such as cloud-based environments and Hadoop, a framework for managing and using big data (Figure 6).
Figure 6. How life insurers in the Americas are preparing for growing volumes of data
| Large carriers (> $3 billion written premium) |
Midsize carriers ($1 - $3 billion written premium) |
Small carriers (< $1 billion written premium) |
||||
|---|---|---|---|---|---|---|
| Now | Exploring | Now | Exploring | Now | Exploring | |
| Cloud-based (Amazon Web Services, Azure) | 55% | 27% | 0% | 50% | 14% | 36% |
| Hadoop | 18% | 27% | 33% | 17% | 18% | 11% |
Perhaps the bigger challenge — one many insurers recognize — is to help business stakeholders understand enhanced model results so timely action can be taken. Only 13% of respondents think models are currently well or very well understood by those outside the data science and actuarial teams. By contrast, 40% say that broader understanding is very limited or nonexistent.
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| Title | File Type | File Size |
|---|---|---|
| Predictive analytics speeds innovation for life insurers | .5 MB |
