This discovery framework is designed to help facilitate a collaborative conversation regarding Salesforce Data Cloud. By exploring these operational scenarios, Marine Insurance Companies can accurately assess internal workflows and identify how specific Salesforce Data Cloud capabilities can support their unique underwriting and administrative needs, starting with areas of highest impact to business revenue and client care.
How is the executive team reconciling disparate risk exposure data across global fleets and varied policy types to prevent systemic revenue leakage?
Scenario A: Delayed Exposure Visibility
"We have separate databases for hull, cargo, and liability policies. By the time we manually aggregate the global risk data, the exposure profile of our insured fleets has shifted, leaving us vulnerable to unpriced risk."
Scenario B: Manual Warehousing
"We run quarterly exposure reports by having analysts manually export data from our regional underwriting platforms into a central warehouse before leadership can review it."
When expanding operations into new geographic maritime jurisdictions, how does the organization ensure compliance and standardize data governance across fragmented legacy systems?
Scenario A: Compliance Gaps
"Opening new regional hubs means dealing with strict local data regulations. We struggle to map local broker data back to our global standards, creating massive compliance gaps in our core systems."
Scenario B: IT Bottlenecks
"We rely heavily on regional IT teams to manually scrub and format new broker and policyholder data before it is allowed to enter our central underwriting platform."
How are underwriting teams synthesizing historical claims data, external weather models, and real-time vessel telemetry to price complex voyage risks?
Scenario A: Missed Risk Factors
"Underwriters pull claims history from one system and vessel tracking from another. The manual overlap means we miss subtle risk factors and incorrectly price high-risk routes."
Scenario B: Manual Cross-Referencing
"We give underwriters access to a dashboard that shows weather and claims, but they still have to manually cross-reference the data points in Excel before issuing a final quote."
In what ways are claims adjusters correlating historical incident reports with current cargo damage claims to identify patterns of fraud or systemic loss?
Scenario A: Duplicate Payouts
"Adjusters review cargo claims in complete isolation. We recently paid out three separate claims for the exact same voyage incident because the data lived in disjointed regional silos."
Scenario B: Time-Consuming Investigations
"Our adjusters can search historical claims to check for fraud, but it requires knowing exactly which vessel or broker to query in our legacy database."
What mechanisms are in place to feed real-time maritime loss data into actuarial models without requiring extensive manual data preparation?
Scenario A: Stale Risk Models
"Our actuaries spend weeks formatting claims and premium data before they can even run their models. As a result, our pricing adjustments are always a full quarter behind the market."
Scenario B: Custom Engineering Reliance
"We have a dedicated data engineering team that builds custom pipelines for the actuarial group every time we need to update our core risk models."
How is the organization tracking and responding to broker interactions across multiple product lines to ensure a cohesive distribution strategy?
Scenario A: Fragmented Communication
"Brokers constantly complain that they get pitched by our hull team and our liability team on the exact same day. We have no centralized view of our broker communications."
Scenario B: Incomplete Logging
"We ask our sales and underwriting teams to log all broker calls in a shared system, but adoption is low and the resulting data is rarely actionable."
How effectively does the organization utilize external IoT data, such as real-time AIS vessel tracking, to proactively alert policyholders of emerging navigational risks?
Scenario A: Reactive Data Usage
"We purchase expensive AIS satellite data, but it sits in an isolated silo. We only ever look at it after a vessel has been lost or damaged to verify the details of the claim."
Scenario B: Generalized Alerts
"We send weekly weather bulletins to our major fleet operators based on general maritime forecasts, rather than reacting to specific vessel coordinates."
What is the current strategy for resolving conflicting policyholder identities across legacy billing, claims, and underwriting platforms?
Scenario A: Inaccurate Account Valuation
"We have the exact same global shipping conglomerate listed under five different names across our core systems. We cannot accurately calculate their total lifetime value or loss ratio."
Scenario B: Temporary Data Cleansing
"We run an annual data cleansing initiative where analysts manually merge duplicate accounts, but the systems inevitably drift out of alignment within a few months."
How is the organization aggregating total insured value across cumulative maritime chokepoints to satisfy reinsurance treaty reporting requirements?
Scenario A: Slow Reporting Cycles
"Compiling the total exposure for vessels transiting the Panama Canal takes our team two weeks of manual database querying. Reinsurers are demanding faster, more accurate reporting."
Scenario B: Static Estimations
"We rely on a static report that estimates chokepoint exposure based on historical routing, but it entirely fails to account for real-time voyage diversions."
How does the technical infrastructure manage data retention policies and consent frameworks across varied international maritime jurisdictions?
Scenario A: Fragmented Deletion Processes
"Deleting a broker or policyholder record requires submitting tickets to four different database administrators. We are actively failing our data privacy audits."
Scenario B: Partial Governance
"We have a central privacy portal, but it only controls marketing communications. Core underwriting data and historical claims must still be managed manually."