A risk score becomes more useful when you can inspect the evidence behind it.

Imagine two token assessments returning the same score. One has broad data coverage; the other is missing important inputs. The headline number alone won t tell you that difference.

At Hodler Suite, we make that context visible:

• Score explanations: Component scores and contributing risk factors help explain the result.

• Data confidence: A separate indicator shows how much supporting data was available. This measures coverage, not the probability that the assessment is correct.

• Sources and freshness: Contributing providers and data timestamps help reviewers assess the evidence.

• Visible gaps: Partial-data indicators identify components that lacked source data and used a neutral default.

• Clear failure states: If the assessment engine fails, the result displays Score Unavailable.

• 99% Coverage rate: We test and verify our providers' data coverage and ensure 99% of data availability at all times.

A token risk score gives you a starting point. For a review someone else needs to inspect, the explanation matters just as much.

Five questions worth asking:

• What was assessed? Confirm chain and contract address, not just token name.

• What drove the result? Look at contract controls, liquidity, and holder concentration separately.

• Where did the evidence come from? Findings should be traceable to their sources.

• How fresh is it? A report needs enough timing context to judge whether findings still apply.

• What couldn t be checked? Missing data should remain visible.

One distinction from Hodler Suite s scoring model: confidence measures data-source coverage, not the probability that a token is safe. High coverage and low risk mean different things.