Actuarial lens📈 (@actuariallens)

Hi, I’m Oghogho Perfect — an aspiring actuary and 200-level actuarial science student at the University of Benin. I’m passionate about data, precision, and smart decision-making. Here on TikTok, I break down actuarial science — from exam strategies to real-world applications in finance and…

Stepping into November with faith, purpose, and expectations of greater things. 💙 May this month be kind to us all, and may our efforts yield beautiful results. Happy New Month, ActuarialLens community! #ActuarialLens #HelloNovember #500kviews

A clothing store offers a 20% discount, and sales increase. But did the discount cause the increase, or was something else responsible? That's the difference between correlation and causation! Risk. Data. Finance. Simplified. #ActuarialLens #ActuarialScience #DataAnalysis #Statistics…

How much data do we actually need before making an important decision? That’s one of the questions Power Analysis helps us think about. I’m learning that actuarial work isn’t just about analysing data. It’s also about planning for the right amount of information before making decisions. Risk…

What if an insurer wants to know whether a new fraud-detection system is working, but simply doesn’t have enough data yet? That’s where sample size matters. I’m learning that sometimes, a result isn’t clear because the effect isn’t there — we may simply need more information to see it. Risk. Data…

What happens when the numbers don’t give a clear answer? An actuary doesn’t just stop at “the result isn’t significant.” They ask a deeper question: Was our test powerful enough to detect the effect in the first place? That’s where Statistical Power becomes important. I’m learning that actuarial…

What if an insurer rejected a useful idea simply because the data couldn’t detect the improvement? That’s where Statistical Power comes in. I’m learning that “no significant result” doesn’t always mean “no effect.” Sometimes, we need to ask whether our test was powerful enough to detect it. Risk…

What if an insurance company finds a pricing model that looks better than the current one? Should they use it immediately? Not quite. Before making decisions that could affect thousands of policyholders, actuaries first decide how much evidence is enough to trust the new model. That threshold is…

An insurance company notices that health insurance claims have started to fall. Good news... right? Not so fast. Before celebrating or investing more money into the wellness program, one important question needs to be answered: Did the program actually reduce claims, or could the result simply be…

What if a company's biggest decision was based on a coincidence? An insurer launches a wellness program and later notices that health insurance claims have dropped. Success... or just chance? This is where Hypothesis Testing comes in. As an Actuarial Science student, I'm learning that good…

One sample. One estimate. Smarter decisions. 📈 Point Estimation - the starting point for pricing risk when you don't have all the answers. Learning something new every day. 🚀 #ActuarialLens #FutureActuary #AspiringActuary #ActuarialScience #Insurance #RiskManagement #Statistics #DataAnalytics…