Risk Concentration Assessments: How AI can help marine insurers

The ability to pinpoint risk concentration on board a specific vessel, warehouse, or port. It's the holy grail of underwriting because understanding and quantifying cargo exposures by location opens up multiple risk management opportunities and revenue streams for agile marine insurers by offering hyper-tailored policies to the actual risk landscape.

The problem is the ongoing need for more data transparency.

According to Zurich, marine insurers and shippers need clear insights into cargo locations and conditions to overcome significant challenges in proactive risk management and risk concentration assessments.

Aligning risk and insurance

The COVID-19 pandemic, the blockage of the Suez Canal by the Ever Given, and ongoing disruptions in the Red Sea continue to escalate risks for every stakeholder in the supply chain -- due to the reliance on critical maritime chokepoints. And this challenge is no closer to being resolved.

Time for change

"The dream is to have industry data detailed enough for underwriters to use in predictive models. How that dream can come true while still preserving security and confidentiality is a mystery," said Rahul Khanna, global head of marine risk consulting for Allianz Global Corporate & Specialty.

Let's take a closer look at this mystery.

The challenge of predictive risk models for marine insurers

1. Fragmented Data Sources & Inconsistent Formats

Essential information (cargo details, ship data, routes, storage locations) is scattered across different systems owned by insurers, brokers, shippers, ports, etc. There's no standardized data format for the entire industry, which makes consolidating data difficult, hindering a clear, big-picture view of risk concentration

2. Manual Processes

Many insurers still rely heavily on manual data entry and analysis. This is slow, inefficient, and prone to human errors. Assessing the risk of a specific vessel, warehouse, or port requires a lot of time and effort to collate information from various sources, inhibiting real-time insights.

3. The 'Broad vs. Deep' Data Dilemma

Insurers must choose between "broad" data (many ships/locations, but less detail) or "deep" data (specific ship/port, but limited scope). Technology and budget constraints make it hard to have both. Insurers choosing "broad but shallow" might miss a hazardous cargo item on a ship. While the "deep but narrow" approach might not show that the port has a broader concentration of the same type of risky cargo.

4. Security & Confidentiality Concerns

Cargo owners are understandably hesitant to share all details (what they're shipping, its value) due to competition and security risks in a heavily regulated industry. Even between insurers, competitive concerns limit data-sharing. The most critical data for assessing localized risk -- WHAT is WHERE -- is the hardest to get. This leaves insurers underwriting half-blindly, leading to under- or over-charging for their coverage.

Unraveling the challenge of predictive risk models for marine insurers

Here's how Stargo's suite of AI-powered tools addresses each of the challenges faced by marine insurers in conducting localized risk assessments:

Fragmented data sources & inconsistent formats

- StarDox can extract and structure relevant data from any source and format, including emails, documents, images, and unstructured voice recordings.

- This eliminates the need for standardized data formats, allowing insurers to consolidate information scattered across disparate systems owned by various stakeholders.

Manual processes

- StarDox automates data extraction, cleansing, and enrichment processes, lowering the need for manual data entry and analysis.

- With advanced natural language processing (NLP), StarDox can rapidly process large volumes of data, enabling real-time risk assessments for specific vessels, warehouses, or ports.

- Applying StarDox to survey reports reduces analysis time by 60%, enabling faster and more efficient risk assessments.

The 'broad vs. deep' data dilemma

- Stargo's Mycelium platform consolidates and analyzes an insurer's entire data ecosystem, including policy documents, cargo manifests, vessel tracking data, and geopolitical intelligence reports.

- This centralized data consolidation gives insurers a comprehensive view of risk factors, combining broad and deep data insights.

- Stargo's NLP models can accurately identify potential exclusions or coverage gaps across this vast corpus of unstructured data with 100% data accuracy.

Security & confidentiality concerns

- Stargo utilizes advanced anonymization and encryption protocols to facilitate secure data sharing between stakeholders, addressing confidentiality concerns.

- Marine insurers can access detailed cargo manifests and vessel tracking information without compromising proprietary or sensitive data.

- Stargo's non-disruptive data extraction capabilities ensure stakeholders' operations and cost optimization strategies remain unaffected.

- Stargo's GenneSys engine achieves 100% data accuracy by extracting, cleansing, and enriching data from any email inbox.

Let us show you how using your data more effectively improves predictive modeling. Schedule a demo.