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Home/Case Studies/Market Structure: Reduce Extreme Slippage With Batch Execution

Market Structure: Reduce Extreme Slippage With Batch Execution

JIL improved execution predictability using batch clearing and oracle bands with audit-grade batch receipts.

Scenario Profile
Liquidity + Routing Partner (Scenario)
Region
Global
Industry
Market Structure / Liquidity
Products Used
AMM V5 Batch Auctions + Oracle Bands + Receipts
Benchmark + Modeled Impact

Benchmark-based analysis

📊
Industry Benchmark (Institutional Standards)
The institutional requirement is defensible execution narratives with audit-grade evidence.
⚙️
Mechanism
Timed batch auctions + oracle bands + deterministic batch receipts.
📈
Modeled Impact
Batch mechanics can reduce extreme slippage events by 10-35% on large-notional swaps (modeled; depends on liquidity/routes).
🧮
Savings Formula
Estimated value improved = large-swap notional x slippage proxy x (10-35%).
Evidence Produced
Batch receipt ID + execution timeline + exportable pack.
$16.6B
FBI IC3 2024 Total Losses
$2.77B
BEC Losses (21K complaints)
79%
Orgs Hit (AFP 2024)
$4.60
Per $1 Fraud (LexisNexis)
Why JIL Wins

Institutions buy execution integrity and a defensible clearing narrative.

Problem

Large swaps suffered from MEV/sandwich behavior and unpredictable execution.

Expected Outcomes
  • Reduced extreme slippage events (target KPI)
  • Improved predictability with deterministic batch IDs
  • Produced audit-friendly execution receipts
The Industry Problem

Why this problem persists

In standard AMM pools, large swaps are visible in the mempool before execution. MEV bots sandwich these trades - buying before and selling after - extracting value from the trader. The result: unpredictable execution and extreme slippage. In this scenario, the liquidity partner was routing institutional-sized orders through standard AMM pools and consistently experiencing adverse execution. MEV extraction was measurable and growing, and clients were losing confidence in execution quality.

How JIL Solves This

The JIL approach

JIL's AMM v5 uses commit-reveal batch auctions with VRF-randomized ordering. Trades are committed (hidden), then revealed and executed in randomized batches. Oracle bands reject trades that deviate beyond expected price ranges. Every batch produces a deterministic receipt. The commit-reveal scheme prevents front-running by hiding trade details until the batch window closes. VRF-randomized ordering within each batch eliminates ordering manipulation. Oracle bands provide a price sanity check that rejects trades with extreme deviation, protecting against manipulation during volatile periods.

Scenario Parameters
CorridorAMM trading with large institutional orders
Monthly VolumePilot cohort
Risk ClassMedium
IntegrationsAMM v5 + oracle feeds + batch clearing engine
Evidence OutputsBatch receipt + execution log + oracle band verification
Receipts & Proof Produced

Every settlement event produces verifiable evidence

📜
Settlement Receipt
📝
Intent Attestations
📋
Policy Log
📦
Audit Export

Before vs After

Before JIL
  • Visible mempool orders
  • MEV/sandwich attacks
  • Unpredictable execution
  • No execution audit trail
After JIL
  • Hidden commit-reveal
  • VRF-randomized batches
  • Oracle band protection
  • Deterministic batch receipts

What Made the Difference

Commit-reveal scheme

hides trade intent until batch execution

VRF-randomized ordering

eliminates front-running opportunity

Oracle bands

reject trades with extreme price deviation

Batch receipts

audit-grade execution evidence for every batch

Next Steps

Deployment path

Expand oracle band integration to additional price feeds, deploy institutional RFQ venue alongside AMM, and integrate batch receipts with institutional reporting systems.

Benchmark-Based Modeled Impact: The "Modeled impact" estimates above are derived from public benchmarks and the control changes enabled by JIL Sovereign. Actual outcomes vary by corridor coverage, policy configuration, counterparties, and operating environment.

Ready to see JIL in your environment?

These scenarios demonstrate deployed JIL capabilities against documented industry problems. Define your corridor, configure your policies, and run a proof of concept.