This discovery framework is designed to help facilitate a collaborative conversation regarding Tableau. By exploring these operational scenarios, Marine Insurance Companies can accurately assess internal workflows and identify how specific Tableau capabilities can support their unique clinical and administrative needs, starting with areas of highest impact to practice revenue and client care.
How does the executive team aggregate global hull and machinery premium data against real-time claims exposure to monitor aggregate portfolio profitability?
Scenario A: Portfolio Exposure Blind Spots
"We review aggregate exposure reports weeks after the end of the quarter. If a major storm hits a congested port, we cannot see our total financial liability until the actuaries run manual spreadsheets."
Scenario B: Siloed Executive Dashboards
"We have basic premium reports, but matching them against active claims requires downloading data from our policy system and matching it in a separate tool."
When evaluating entry into new maritime territories, how do you model the correlation between historic catastrophic weather events and port-specific cargo risk?
Scenario A: Delayed Territorial Expansion
"Entering new shipping corridors is entirely based on gut feeling and static historical reports. We lack a dynamic way to model catastrophic risks against projected premium growth."
Scenario B: Static Market Analysis
"We get risk reports for new ports, but the data is completely static. Changing variables like piracy risk or severe weather requires a completely new manual report request."
What is the process for visualizing accumulated risk across specific shipping routes to prevent overexposure in localized geographic zones?
Scenario A: Geographic Overexposure
"Underwriters do not realize we have accumulated too much risk in the South China Sea until the quarter concludes. A single localized event could trigger massive simultaneous payouts."
Scenario B: Reactive Accumulation Tracking
"We track vessel locations, but cross-referencing that with our active hull policies requires running a batch script overnight."
How do claims adjusting teams track the cycle time of complex salvage operations versus standard freight damage to identify bottlenecks in settlement?
Scenario A: Unidentified Bottlenecks in Salvage
"Major casualty claims take months to settle, and we have no way to visually pinpoint whether the delay is with the salvage assessment, the adjusters, or our legal team."
Scenario B: Average Cycle Time Reliance
"We know our average claims cycle is forty days, but we cannot easily drill down to see if cargo claims are moving faster than protection and indemnity cases."
In what ways are historical loss ratios blended with real-time global fleet movement data to adjust predictive pricing models?
Scenario A: Disconnected Pricing Models
"Our actuaries build pricing models based on outdated loss ratios. They cannot easily integrate new external datasets like global shipping traffic changes into their analysis."
Scenario B: Manual Data Preparation
"We spend eighty percent of our time cleaning and joining claims data with external maritime risk databases before we can even begin analyzing pricing models."
How do you monitor compliance with international maritime sanctions by cross-referencing insured vessels against restricted port registries?
Scenario A: Sanctions Compliance Violations
"We recently found out we insured a vessel that docked at a sanctioned port. The data was buried in a log that nobody checks because the volume of port calls is too high."
Scenario B: Manual Registry Checking
"Risk managers have to manually query the maritime registry lists and do a lookup against our active policyholder list every single month."
What mechanisms are in place to evaluate broker performance based on the profitability and loss ratios of the specific maritime portfolios they bring in?
Scenario A: Unprofitable Broker Portfolios
"We pay high commissions to brokers bringing in high volumes of cargo policies, but we cannot easily see that their specific book of business has a terrible loss ratio."
Scenario B: Disconnected Commission Tracking
"We know how much we pay our brokers, but correlating those payouts directly to the long-term profitability of the marine assets they insure takes days of spreadsheet work."
How are internal operational costs tracked against premium volumes across different marine insurance lines like protection and indemnity versus freight?
Scenario A: Unseen Operational Drag
"Processing inland marine claims is costing us significantly more in administrative overhead than deep-water hull policies, but the data is buried in different cost centers."
Scenario B: High-Level Cost Averages
"We track total operational expenditure, but we lack the visual tools to allocate those costs granularly across different marine insurance lines."
How is the effectiveness of our reinsurance treaty structures analyzed when major maritime casualty events trigger multiple policies simultaneously?
Scenario A: Treaty Exhaustion Blind Spots
"When a major hurricane hits a primary shipping hub, we struggle to calculate exactly how close we are to exhausting our catastrophic reinsurance treaties across multiple affected policies."
Scenario B: Retroactive Treaty Analysis
"We eventually figure out our reinsurance recovery amounts, but the calculations are done retroactively rather than monitoring our treaty limits dynamically."
What methods are used to report on regulatory capital requirements in relation to our shifting exposure across global maritime jurisdictions?
Scenario A: Jurisdictional Capital Risk
"Different global maritime jurisdictions require different capital reserves. We cannot quickly visualize if a shift in our shipping portfolio puts us out of compliance in a specific region."
Scenario B: Tedious Regulatory Reporting
"Compiling the monthly regulatory capital reports takes our team two weeks of aggregating policy data from different regional offices."