For startup founders venturing into the world of insurance, the path to creating new insurance products can be challenging. Actuarial feasibility studies often come with hefty price tags and a veil of secrecy. Understanding terms like “loss ratio” can be baffling. In this article, we aim to shed light on the process of creating new insurance products, offering a roadmap that includes market research, feasibility studies, capacity submissions, and identifying capacity structures.

We'll delve into the complexity of feasibility studies using an example of a “Car Warranty Product” and clarify key data variables to help founders grasp estimated loss ratios and premium calculations. Our ultimate recommendation: partnering with companies like Zala can expedite the journey, making it far more accessible and successful for startups.

## The Roadmap to Creating Insurance Products

### Step 1: Market Research

Begin by thoroughly researching your target market. Understand the specific needs and risks your insurance product should address. Identify potential competitors and gaps in existing offerings.

### Step 2: Feasibility Study

A feasibility study is crucial. It involves examining existing insurance products and their limitations. Stakeholder analysis, including insurers and potential policyholders, is essential. Dive into the technical and economic aspects of your proposed insurance product.

### Step 3: Capacity Submission

Once you've gathered your findings, submit your proposal to capacity providers. These are entities that have the financial strength to underwrite your insurance product.

### Step 4: Identifying Capacity Structures

Work with capacity providers to define the structures of your insurance product, such as primary, contributory, quota sharing, and more. These structures determine how risk is distributed.

## The Complexity of a Feasibility Study

### Data Variables

In the example of developing an incident-specific Car Warranty insurance product, several data variables come into play:

#### Incident Types:

- Definition: These are categories of events or incidents that the insurance product covers, such as mechanical failures, overheating, accidents, theft, etc.
- Calculation: Actuaries define incident types based on historical data, industry standards, and specific policy terms. They identify and classify events that the insurance product will address.

#### Probabilities (ρ):

- Definition: Probabilities represent the likelihood of each incident type occurring within a specified time frame. It's a key factor in estimating future claims.
- Calculation: Actuaries calculate probabilities using historical data analysis, statistical models, and risk assessment. They use tools like frequency distributions and probability density functions to determine these values.

#### Severity of Loss (X)

- Definition: Severity of loss refers to the financial impact of a specific incident type. It's the amount of money required to cover damages or losses resulting from an event.
- Calculation: Actuaries estimate the severity of loss by analyzing historical claims data. They use statistical methods to determine the distribution of loss amounts for each incident type, often fitting data to probability distributions like the log-normal distribution.

Based on the same example of a car warranty:

#### Indemnity Functions

- Definition: Indemnity functions specify the payout amount the insurance company will provide for each incident type. They define the insurer's financial obligation to the policyholder.
- Calculation: Actuaries design indemnity functions based on the insurance policy's terms and conditions. These functions may involve deductibles, policy limits, and various coverage details.

Example based on the same scenario:

#### Retained Loss

- Definition: Retained loss represents the portion of financial loss that the policyholder must cover themselves, typically through deductibles or self-insured amounts.
- Calculation: Actuaries calculate retained loss based on the policy's deductible structure. This amount is the policyholder's responsibility before the insurance coverage kicks in.

#### Premium

- Definition: The premium is the amount policyholders pay to the insurer for coverage. It's calculated to cover expected losses, administrative costs, and provide a profit margin for the insurer.
- Calculation: Actuaries calculate premiums using complex formulas that consider various factors, including probabilities, severity of loss, expenses, and desired profit margins. Premium calculation often involves mathematical models like the aggregate loss model or the expected loss ratio method.

#### Incident-Specific Loss Severity

- Definition: This represents the expected loss amount for each specific incident type, considering all the parameters and conditions set forth in the insurance policy.
- Calculation: Actuaries estimate incident-specific loss severity by combining the probability of an incident occurring (from ρ) with the severity of loss (from X). The calculation incorporates policy terms, such as deductibles and policy limits.

## Crafting the Insurance Product

To create a profitable insurance product, you need to assess the risk and potential loss severity of covered incidents. This involves:

**Identifying Incident Types****Collecting Data****Analyzing Data****Statistical Testing**

**Chi-Square Test:**Used to compare observed and expected frequencies within contingency tables. Actuaries might use it for testing relationships between variables)**Incident-Specific Loss Severity Modeling**

**Distribution Fitting**

**Model Selection and Validation**

**Additional Tests**

****The final step is to set up indemnity functions that define how much the insurance provider pays for each incident type. This process optimizes the contract to balance risks for both parties.

## Capacity and Implementation Plan

Once your insurance product is developed, it goes through three phases:

**Development:**Assemble a team of industry experts to create the product.**Pilot:**Test the product with a select group to evaluate effectiveness and make adjustments.**Launch:**Officially introduce the product, emphasizing customer education and support.

Artificial Intelligence (AI) is transforming the field of statistical analysis by offering more sophisticated and efficient methods for understanding data patterns. For instance, in insurance, AI can replace standard statistical analysis to calculate metrics like the average loss severity per claim. Instead of manual calculations, AI-powered tools can process vast datasets rapidly. In Python, libraries like NumPy and Pandas can automate this task. Here's an example of Python code that calculates the average loss severity per claim using Pandas:

###### PYTHON CODE EXAMPLE

This Python code uses Pandas to load and manipulate the data. With AI and machine learning techniques, more complex analyses, including predictive modeling, can be automated to gain deeper insights into insurance data, enhancing decision-making and risk management.

Substituting standard actuarial analysis with Python programming and machine learning in the creation of new insurance products offers several advantages and drawbacks:

### Pros:

- Efficiency and Speed: Python and machine learning algorithms can process and analyze vast datasets much faster than traditional actuarial methods. This efficiency enables quicker product development, which is crucial in responding to evolving market needs.
- Enhanced Accuracy: Machine learning models can identify complex patterns and relationships within data, improving risk assessment and pricing accuracy. This leads to more precisely tailored insurance products, reducing the chances of underpricing or overpricing policies.
- Flexibility and Adaptability: Python and machine learning allow for rapid iteration and adjustment of insurance products in response to changing market conditions or emerging risks. This flexibility is vital in today's dynamic insurance landscape.

#### Cons:

- Data Dependency: Machine learning models heavily rely on high-quality data. Inaccurate or biased data can lead to flawed models and unreliable insurance products. Ensuring data quality and diversity is a significant challenge.
- Interpretability: Some machine learning models, like deep neural networks, are considered "black boxes" because it's challenging to interpret their decision-making processes. This lack of transparency can be problematic in regulated industries like insurance, where explainability is crucial.
- Expertise and Resources: Implementing machine learning in insurance product development requires specialized knowledge and resources. Companies may need to invest in training or hire data scientists and machine learning experts, which can be costly.

Creating new insurance products is a multifaceted process, but with the right guidance and resources, startups can navigate it successfully. Partnering with experienced companies like Zala can significantly expedite the journey, ensuring startups reach their goals more efficiently and effectively.

Reach out to the Layla Atya next time you need to create a new insurance product!