What
Layered protocol risk
Research into how DeFi markets can separate first-loss retention, primary cover, excess protection, and tail events.
Risk transfer research
Reinsurance for DeFi
Strong Tower studies how on-chain markets can define first-loss retention, primary cover, excess layers, event evidence, and tail-risk backstops.
Reinsurance
layered risk model
Research
Stageresearch
ScopeDeFi + trading
Modelloss waterfall
Contactx.com/StrongTowerFI
Evidence
Evidence before capital
Layered reinsurance depends on reliable event data, trigger definitions, claims evidence, protocol telemetry, and clear settlement records.
Boundary
No coverage offer
No policies, premiums, commitments, coverage terms, brokerage, underwriting, or placement services are offered through this page.
Boundary
This page is informational only. Strong Tower is not offering, brokering, underwriting, or placing insurance or reinsurance coverage here.
Layer design
Risk should attach in explicit layers.
DeFi and trading systems already operate with implicit loss waterfalls: protocol reserves, insurance funds, socialized losses, governance intervention, or user haircuts. The research question is how to make those layers explicit, evidenced, and reusable.
layer 01
Protocol retention
layer 02
Primary cover
layer 03
Excess layer
layer 04
Retro layer
01
Protocol retention
The protocol, DAO, exchange, or market system keeps a defined first-loss layer through reserves, insurance funds, fees, or treasury capital.
02
Primary cover
A dedicated pool or coverage program absorbs specified losses after controls, evidence, and claim rules are satisfied.
03
Excess layer
Additional capital attaches above the primary layer for larger events, correlated failures, or multi-market loss accumulation.
04
Retro layer
A final backstop can spread tail exposure across specialist capital, funds, or risk partners instead of concentrating it in one protocol.
Candidate markets
Protocol markets that may need layered backstops.
Layered reinsurance is most useful where risk is measurable, events are attributable, losses can exceed protocol-level reserves, and multiple market participants need confidence that tail losses will not be handled ad hoc.
Perpetuals and derivatives venues
Insurance funds, auto-deleveraging events, oracle gaps, liquidation cascades, and settlement shortfalls are natural candidates for explicit layered loss waterfalls.
DEXs and AMM ecosystems
Route failures, liquidity-pool exploit losses, sandwich/ordering incidents, and abnormal price-discovery events can be modeled as distinct covered perils.
Lending and credit protocols
Bad debt, collateral auction failure, oracle manipulation, liquidation congestion, and cross-collateral contagion can require protection beyond a protocol reserve.
Bridges and messaging systems
Validator compromise, relayer failure, message replay, signer-key exposure, and finality mismatch can create severe low-frequency loss events.
Stablecoin and RWA systems
Reserve-reporting gaps, redemption stress, custodian failure, collateral impairment, and oracle-dependent valuation events can be structured into layers.
Keeper and automation networks
Missed liquidations, failed rebalances, delayed settlement, oracle update gaps, and operational downtime can become measurable service-failure risks.