All autopsies

// STARTUP COMPARISON

Kueski (2022 crisis) vs LendingClub (2016 crisis)

Kueski (2022 crisis) failed in 2022 due to Unit Economics. LendingClub (2016 crisis) failed in 2016 due to Founder Chaos. Different causes, different sectors, different eras — but the same simulation outcome.

METRIC🔥 Kueski (2022 crisis)🔥 LendingClub (2016 crisis)
SectorFintechFintech
CountryMexicoUSA
Founded20122006
Died20222016
Raised$202M$1.3B
Peak$202M raised$9B valuation
Primary CauseUnit EconomicsFounder Chaos

// WHY EACH FAILED

🔥 Kueski (2022 crisis)
Unit Economics
Kueski, Mexico's largest buy-now-pay-later platform, raised $202M and reached 1M monthly users. In 2022 rising interest rates globally compressed BNPL margins — the cost of capital exceeded the yield on consumer loans. Kueski laid off 15% of its workforce in 2022 and restructured its lending model to survive. It remained operational but at significantly reduced scale.
// LESSON
BNPL models are levered bets on low interest rates. Model your unit economics at 3x current rates before raising. If the model breaks at 3x, the business breaks when rates normalize.
🔥 LendingClub (2016 crisis)
Founder Chaos
LendingClub CEO Renaud Laplanche resigned in May 2016 after an internal review found that $22M in loans had been sold to an investor with falsified application dates, and that Laplanche had failed to disclose a personal conflict of interest. The stock fell 50% in a single day. LendingClub survived but spent years rebuilding institutional trust.
// LESSON
For marketplace lenders, loan data integrity is the product. Falsifying origination dates is not a compliance technicality — it invalidates every institutional investor's credit risk model and destroys the trust that marketplace lending is built on.

// IN THE SIMULATION

Kueski triggers BNPL_MARGIN_COMPRESSION at a RATE_RISE macro event. The simulation flags BNPL models as structurally rate-sensitive — a 200bps rate increase compresses margins to negative in consumer credit with thin spreads.

LendingClub triggers FINTECH_FOUNDER_DATA_MANIPULATION — the simulation models loan data integrity as a hard constraint for marketplace lenders. When origination data is falsified, every institutional investor's credit model becomes invalid simultaneously.

// EXPLORE FURTHER