Blackjack Insurance is a mathematical trap, but how do you handle the RTP distortion in your data?

I’ve been analyzing some Blackjack operational datasets over at Oncastudy, and the impact of Insurance bets is pretty glaring. It’s a textbook case of negative EV the 2:1 payout just doesn't hold up against the actual probability of a 10-value card appearing, so it's basically just a slow erosion of the player's bankroll.

The real headache isn't the math itself, though; it’s how these side bets skew the overall RTP (Return to Player) metrics. We usually separate the datasets main game vs. side bets to keep our risk monitoring precise. But lately, we've seen spikes in high-risk side betting volume that threaten to make the main system stability metrics look... unreliable.

When you see a sudden surge in high-risk side betting, what specific indicators are you using to re-verify the system's overall health? Are you looking at the standard deviation of the payout distribution, or is there a better metric to ensure the volatility isn't masking a deeper structural issue?

Curious to hear how those of you in fintech or gaming data handle these kinds of "noisy" structural mismatches.