APM for Insurance

Insurance companies have complex mixtures of legacy packaged and custom applications. Insurers migrating to packaged applications deal with high amount of change and uncertainty.

Complex core systems which are most often packaged applications are being coupled with other custom built software to build capabilities to do straight-through processing.Performance, usability and availability are key to these channels being productive.Many customers are directed through comparison sites meaning they have already gone through one hurdle to access service offerings.

Status

Are our critical policy and claims management systems up and running?

Capacity management

When performance degrades beyond 2 standard deviations from the calculated baseline for the ‘submit credit check’ business transaction, provision another private cloud instance of that web service tier.

Operational intelligence

If we are planning on adding a new e-commerce insurance digital channel next year, and we are expected to growing the number of claims we process by 25% next year, how much hardware and software will we need to support that growth?

Troubleshooting

1% of the requests in the ‘submit claims’ business transaction were categorized as very slow, investigate these transaction snapshots.

Remediation

  • Automatically restart the application server instance when the number of stalled threads exceeds a specific numerical threshold. 

  • Remove the server from the load balancing pool when the average response time is beyond 3 standard deviations beyond the rest of the cluster.

Business intelligence and performance monitoring

How many claims were processed per minute from our Tier 1 customers during the last hour and how did response time affect revenue during that period?

Utilization

Resource consumption has spiked on a cluster supporting the payment-processing tier… where is the performance bottleneck that is causing this?

Diagnostics

Create a thread dump when the number of ‘Add Claim’ business transactions perform 2 standard deviations below the baseline.

End user intelligence

How many customers clicked through from a comparison site in the last 12 hours who were not converted to customers? How does this compare to the average for this timeframe last week?


"AppDynamics and ThousandEyes have completely reshaped our approach to troubleshooting and experience optimization. "

Hari Vittal
Senior Engineer, FICO


Resources

BITMARCK

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Daman

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