Welcome to the Roadmap!
Hello! Welcome to the final piece of the Problem Definition puzzle. By now, you’ve likely learned how to define a business problem and understand the data you currently have. But here’s a secret: the most successful actuaries don't just look at the data on their screen—they look for what’s missing.
In this chapter, we will learn how to identify additional information that could make our models better and how to lay out the next steps for a project. Think of this as checking your backpack before a long hike; you want to make sure you have everything you need before you start climbing the mountain of data analysis!
1. Identifying Data Gaps: "The Missing Puzzle Piece"
Before you dive into building a model, you must ask: "Is the data I have enough to solve the problem effectively?" Often, there is a gap between the information available and the information required to produce a highly accurate prediction.
What is a Data Gap?
A data gap occurs when a specific variable or piece of context that likely influences the outcome is missing from your dataset. For example, if you are predicting car insurance claims but don't have the "age of the driver," you have a significant gap!
Common types of additional information to look for:
1. Granular Data: You might have total annual sales, but you really need daily sales to see patterns.
2. External Data: Information from outside your company, like economic indicators or weather reports.
3. Qualitative Context: Insights from Subject Matter Experts (SMEs)—the people who work "on the ground" and know things the numbers might not show yet.
Analogy: Imagine you are trying to predict if it will rain today. You have a thermometer (internal data), but you don’t have a barometer or a window to look outside (additional information). You could make a guess, but it’ll be much better if you find those extra tools!
Quick Review: Questions to Ask
- Does this data cover the entire time period we are interested in?
- Are there specific populations (like new customers) missing from the data?
- Would a proxy variable (a substitute for something we can't measure directly) help?
2. External Sources and Subject Matter Experts
Don't worry if the data you were handed feels incomplete. On Exam PA, you are often expected to suggest where to find more information. Two of the best places are External Datasets and SMEs.
External Data
Sometimes the answer lies outside your organization. Common external sources include:
- Government Databases: Census data for demographic information.
- Economic Indicators: Interest rates or inflation figures (\( \Delta CPI \)).
- Industry Benchmarks: Comparing your company’s performance to the wider market.
Subject Matter Experts (SMEs)
An SME is someone who understands the business side of the data. For an actuary, this might be an underwriter, a claims adjuster, or a marketing manager.
Why talk to them? They can tell you if a "weird" spike in the data was actually a one-time system glitch or a change in company policy. This saves you from building a model based on "noise."
Did you know? In many predictive modeling projects, the most valuable "feature" (variable) in the final model is often one that wasn't in the original dataset but was suggested by an SME!
3. Constraints and Ethics: The "Can" vs. "Should"
Just because you can find more information doesn't mean you should use it. As you identify next steps, you must consider Constraints and Ethics.
Practical Constraints
- Time: Is the project due in two weeks? If so, you probably don't have time to wait for a third-party vendor to send you new data.
- Cost: Some data costs money. Does the potential increase in model accuracy justify the price tag?
- Technical Limits: Can your current software handle the size of the new data?
Ethical and Regulatory Considerations
This is a huge topic for the SOA! When identifying additional data, you must avoid:
- Protected Classes: In many jurisdictions, using variables like race, religion, or health status for pricing insurance is illegal or unethical.
- Proxy Discrimination: Even if you don't use "Race," using "Zip Code" might inadvertently act as a substitute for it. Always ask: "Is this variable fair to the consumer?"
Memory Aid: The "C.E.T." Check
Before adding data, check the Cost, Ethics, and Time!
4. Defining the "Next Steps" Roadmap
On the exam, you may be asked to outline what happens next. A structured approach shows the graders you have a professional "Project Management" mindset. Typically, the next steps after defining the problem and identifying data are:
Step 1: Data Acquisition & Cleaning
Now that you know what you need, go get it! Once you have it, you’ll need to "scrub" it (handle missing values and outliers).
Step 2: Exploratory Data Analysis (EDA)
Start visualizing the data. Use histograms, boxplots, and correlation matrices to see how your new variables relate to the target.
Step 3: Feature Engineering
Transform your raw data into something a model can use (e.g., turning a "Date of Birth" into "Age").
Step 4: Model Selection
Decide which algorithm (GLM, Tree-based model, etc.) fits the problem best.
Key Takeaway
Identifying additional information isn't just a "to-do" list; it’s about reducing uncertainty. By filling data gaps and respecting ethical boundaries, you set your project up for success before you even write a single line of modeling code.
Common Mistakes to Avoid
1. The "More is Better" Trap: Don't suggest adding 50 new variables if you only have a small dataset. This leads to overfitting.
2. Ignoring the Business Context: Never suggest data that is impossible to collect. (e.g., "We should ask customers how many miles they plan to drive in the next 10 years"—this is unrealistic and likely inaccurate).
3. Forgetting the "Next Steps": When the exam asks for next steps, don't just say "build the model." Be specific about cleaning and EDA first.
Quick Summary for Review
- Data Gaps: Look for what's missing (granularity, external data, or context).
- Sources: Use government data and SMEs to fill those gaps.
- Constraints: Always balance the need for more data against Time, Cost, and Ethics.
- Next Steps: Always move from Problem Definition to Data Cleaning/EDA before jumping into modeling.
Keep going! You're building a solid foundation. Once the problem is defined and the gaps are identified, the actual "modeling" part becomes much easier to manage!