Introduction

In the world of crypto lending, risk isn’t just a factor — it’s the foundation. And when you’re operating in an undercollateralized lending environment like Axcess, that foundation needs to be smarter, faster, and far more nuanced than the old rules of “lock up more than you borrow.”

At Axcess, we believe the future of credit lies beyond collateral.

We’ve built a modular risk management engine that merges the transparency of on-chain data with the precision of off-chain intelligence to evaluate borrower capacity — not just their crypto wallet size.

Beyond Collateral: Risk Reimagined

Most traditional crypto lending platforms rely on overcollateralization. It’s simple, but it’s capital-inefficient. Borrowers must lock up 150% of the loan amount, making it a poor fit for high-frequency traders, market makers, and capital-efficient trading firms.

Axcess flips the model.

We’ve built a risk scoring framework that evaluates who a borrower is — not just what they own. Think of it as a financial fingerprint, drawing on dozens of dynamic signals to build a real-time risk profile.

On-Chain Transparency Meets Off-Chain Intelligence

Axcess integrates live on-chain data — wallet activity, historical positions, asset flows — with off-chain insights like KYC/KYB profiles, reputation scores, API-integrated financials, and behavioral metrics.

This hybrid model gives us a panoramic view of each borrower’s intent, ability, and track record. Not only do we know what assets they hold, but how they use them — and why that matters.

Modular Risk Scoring Engine

Our risk engine doesn’t apply a one-size-fits-all rating.

Instead, it scores borrowers through modular components:

• Creditworthiness Signals: Real-world and crypto-native history of repayments, defaults, and exposures

• Capital Efficiency Indicators: How well they utilize borrowed capital to generate returns

• Behavioral Patterns: On-chain movement velocity, trading discipline, position management

• Counterparty Risk Exposure: Cross-platform activity and entanglements

• Liquidity Buffering Models: Capacity to respond to drawdowns and volatility events

Each module adapts in real-time as new data flows in — making the score a living signal, not a static label.