Parrallel-insurance

Parallel Insurance Protocol is a high-throughput, on-chain parametric insurance system built specifically for Arcology Network's parallel execution capabilities. It protects DeFi users against token rug pulls and project collapses while demonstrating the power of blockchain-native concurrency. Traditional insurance protocols face a critical bottleneck: they can't process multiple policies simultaneously due to sequential state updates. When hundreds of users try to buy coverage during market volatility, standard smart contracts create transaction queues and race conditions. Our solution leverages Arcology's concurrent primitives to enable true parallel execution: Unlimited Concurrent Policy Creation: Multiple users buy policies simultaneously without blocking each other ParallelCoin: Custom ERC20 using AddressU256CumMap for conflict-free premium transfers Concurrent State Tracking: U256Cumulative counters and per-user stats with AddressU256CumMap Automated Rug Detection: Parametric triggers using Pyth oracle price feeds to detect >70% drawdowns The Innovation: While the original "Insure The Rug" concept provides excellent insurance design, our implementation adds massive parallelism. We achieve 100% success rate on concurrent transactions where standard implementations would fail due to write conflicts. This creates a scalable insurance primitive for Web3 — capable of handling thousands of simultaneous policy purchases during market crashes when protection is needed most.

How it's Made We built Parallel Insurance Protocol as a showcase of Arcology's concurrent execution capabilities: Smart Contracts (Solidity, Arcology DevNet) ParallelCoverageManager.sol - Core insurance contract using concurrent primitives: U256Cumulative for global counters (totalPolicies, totalCoverage, totalPremiums) AddressU256CumMap for per-user state tracking All state updates use delta operations for conflict-free parallel execution Pyth Network integration for real-time price monitoring Automated drawdown detection (checkTokenDrawdown, isPriceBelowThreshold) ParallelCoin.sol - Parallel ERC20 implementation: Replaces standard mapping(address => uint256) with AddressU256CumMap Enables concurrent token transfers without write conflicts Critical innovation: standard ERC20 is the biggest bottleneck in parallel DeFi MockPyth.sol - Oracle mock for parametric triggers on DevNet Parallel Execution Features Concurrent Policy Creation: 20+ users buying policies in the same block No Write Conflicts: Delta-based operations allow simultaneous state updates Parallel Premium Transfers: ParallelCoin enables concurrent safeTransferFrom calls Per-User Isolation: AddressU256CumMap tracks individual stats without contention Benchmarking & Testing The transaction generator creates pre-signed policy purchase batches Demonstrated 20/20 transactions (100% success rate) in a single block Test suite validates concurrent primitive behavior Network monitor tracks real-time TPS Technical Challenges Solved ERC20 Bottleneck: Standard token contracts cause write conflicts. Solution: ParallelCoin with concurrent balance maps Getter Transactions: U256Cumulative.get() is a transaction, not a view function — requires .wait() pattern Race Conditions: Setup transactions need explicit .wait() to avoid validation failures Bounds Management: All concurrent primitives require upper/lower bounds on the first write Arcology-Specific Implementation Uses @arcologynetwork/concurrentlib for U256Cumulative and AddressU256CumMap Follows Arcology guardrails for concurrent programming Delta operations instead of absolute state updates Proper bounds initialization for safety What Makes This Special? This isn't just insurance — it's a reference implementation for building parallel decentralized finance (DeFi) protocols. The patterns demonstrated here (parallel tokens, concurrent counters, per-user maps) are reusable primitives for any high-throughput blockchain application. By combining insurance use cases, Arcology parallelism, and Pyth oracles, we demonstrate that DeFi protocols can scale beyond sequential execution limits while maintaining security and correctness.