Systemic Stability in the Fintech Lending Era: Network Linkages, Liquidity Risk, and Macroprudential Policy

Abstract

This study assesses the systemic stability of the fintech lending
ecosystem by linking three analytical pillars: (i) a multilayer network of
linkages (platform–investor–custodian bank–payment rails–data
providers), (ii) liquidity risk through the Liquidity-at-Risk (LaR)
framework, and (iii) a flow-based macroprudential policy evaluation (e.g.,
dynamic cash buffers and circuit breakers). We construct a network map
of bipartite investor–platform exposures, platform–custodian bank
linkages, and dependencies on payment rails, then calculate
concentration and centrality metrics, as well as investor overlap across
platforms. Next, we estimate daily LaR (14-day horizon, α=99%) from
cash-in/out, settlement, and disbursement flows, and develop the
Platform Run Index (PRI)—a nowcasting indicator that combines
redemption pressure, settlement queue length, pricing spread deviation,
and operational stress. Contagion dynamics are measured by loss
propagation from platforms to banks/rails based on an exposure matrix,
while policy effectiveness is identified using stepwise Difference-in-
Differences and event studies on staggered rollouts of liquidity rules. The
main results show that funding concentration (high HHI) and reliance on
a few banks/rails increase loss amplification and potential spillover to
banks. LaR peaks with a surge in cash-outs and settlement queues,
marking a run-prone zone even without a significant increase in defaults.
PRI exceeding the p90 threshold predicts a spike in withdrawals the
following day, making it a suitable trigger for adaptive policy. Agentbased
simulations show that funding shocks and operational outages
increase run probabilities and lengthen queues, and—when combined—
result in material loss amplification. Causal evaluations show that the
combination of dynamic cash buffers and flow-based circuit breakers
significantly lowers PRI, reduces LaR violations, shortens queues, and
mitigates early contagion. The implication is that systemic resilience in
fintech lending requires diversified escrow and rail systems, real-time
PRI-based monitoring, multilayer stress testing (LaR + ABM) with
periodic backtesting, and operational resilience standards
(SLA/latency/failover). These findings support the design of dynamic,
flow-based macroprudential policies to balance innovation, inclusion,
and stability.