Fraud prevention in digital finance has long operated on a flawed assumption: that caution equals safety. Yet when over-aggressive risk controls produce hundreds of billions of dollars in false declines each year and permanently drive away the majority of first-time buyers, the cost of that caution becomes impossible to ignore.

Pascal Podvin, co-founder of nSure.ai, has spent years working at the intersection of payment risk and behavioral intelligence, and he argues the industry has been measuring success by entirely the wrong metrics. nSure.ai is a real-time fraud protection platform built specifically for high-risk digital asset and fintech environments, replacing static rule-based filtering with adaptive AI that assesses behavioral intent at the moment of transaction. It sits in a growing category of chargeback-guarantee and fraud-scoring vendors competing with legacy players like incumbents in payments risk, but its thesis is specifically aimed at digital asset, gaming, and remittance verticals that conventional engines tend to over-block.

In this interview, Podvin makes the case that legacy fraud engines are not a liability hedge but a commercial liability in their own right — destroying customer lifetime value, suppressing conversion, and inflating acquisition costs while compliance dashboards show clean numbers. From frozen accounts on six-figure ACH transfers to the economics of a single wrongly declined first-time buyer, he explains why the real risk in digital finance is not fraud tolerance but friction.

The tension is structural. Crypto exchanges and fintech platforms operate under anti-money-laundering and know-your-customer obligations — in the United States, the Bank Secrecy Act framework administered by FinCEN — that push them toward conservative onboarding and fund-holding practices. The friction Podvin describes is often the byproduct of compliance-driven design, not carelessness alone. His argument is that the tools used to satisfy those obligations were built for a payments era that predates real-time digital asset markets.

When a crypto exchange, remittance provider, or Web3 platform places a seven-day hold on a bank transfer or forces a first-time buyer through repetitive verification loops, management believes it is being careful. In reality, Podvin argues, it is being reckless.

Risk management is not a high-level policy document; it comes down to every single transaction. Protecting a single $100 credit card transaction or a $50,000 ACH deposit by freezing accounts or delaying settlement does not eliminate risk. It swaps a small, localized transaction risk for a catastrophic commercial loss, destroying the customer lifetime value of a new buyer potentially worth $5,000 over five years.

The central challenge in digital finance, he says, is recognizing that legacy, static fraud engines actively destroy far more top-line growth and long-term enterprise value than the fraud they were built to catch.

Risk management is often treated as a compliance requirement. Why does it actually come down to every single transaction?

Every single transaction carries a dual trade-off that impacts the entire P&L: immediate transaction value versus long-term LTV. When an exchange or remittance provider evaluates a payment — whether a $100 initial card purchase or a $50,000 ACH transfer — it is not just assessing that single dollar amount. It is deciding whether to preserve or destroy the customer's entire future revenue stream. If a static fraud rule flags a legitimate buyer because their behavior looks slightly unfamiliar, the platform might save $100 in potential chargeback exposure today but throws away $5,000 in CLTV over the next five years.

You argue that platforms trying to be "careful" on individual transactions are actually being "reckless." What does that look like in practice?

Platforms believe that adding multi-day holds, step-up friction, or repeated document re-submissions is reasonable risk mitigation. In reality, it can be commercial suicide, because it rests on a massive organizational blind spot.

Podvin says nSure.ai's field observations show that 87% of first-time buyers who are wrongly declined never come back. Recent market data confirms the trend: 71% of financial institutions admit their anti-fraud controls actively drive customer churn, and false declines burn over $400 billion in legitimate revenue annually.

Risk teams look at clean dashboards with zero chargebacks and celebrate. What they miss is the revenue destruction occurring behind the scenes. According to the Risk Solutions True Cost of Fraud Study, 71% of financial institutions admit their fraud controls are actively driving customer churn. Research from Aite-Novarica Group further shows that over-aggressive filtering results in $443 billion in global false declines every year — proving, in Podvin's view, that static fraud engines destroy far more enterprise value than the bad actors they were built to catch.

The result, he says, is an absurd dilemma: platforms either take unmitigated fraud exposure, or risk "losing both arms and legs" by locking down checkout and killing conversion.

Can you share a real-world example of how this legacy friction breaks the user experience on major platforms?

Every time nSure.ai onboards a major partner, Podvin personally opens an account, deposits substantial capital, and tests the real user flow.

When testing a major global exchange, he opened an account and initiated a six-figure ACH transfer of $200,000. Setting up the account took over 24 hours because the KYC engine failed and forced document re-submissions five separate times, with mandatory waiting periods between each attempt. Once the transfer was initiated, the platform did not place a standard hold on the funds; it froze the entire account for nine days. He could not trade, withdraw, or even view his dashboard while customer support routed him in circles.

The exchange believed it was being careful against ACH return risk, Podvin says. What it actually did was give a high-net-worth customer every possible reason to abandon the platform permanently — something that happens to tens of thousands of legitimate users every single day.

High-value transfers like a $15,000 or $50,000 ACH deposit carry inherent settlement latency. Why do legacy risk systems fail so severely on these flows?

Credit cards carry low limits, usually $1,500 to $3,000 for digital asset purchases or remittance funding. When serious investors or high-velocity users want to move real capital, they rely on bank rails: ACH, SEPA, and wires.

Because ACH transfers take days to reach settlement finality, legacy systems fall back on blunt, static defenses such as seven-day rolling holds. This is not arbitrary: ACH, as operated by Nacha in the United States, allows returns and reversals within defined windows after settlement, so platforms that cannot assess risk in real time simply wait out the exposure window. But investors move money to capture live market opportunities. Forcing a trader to wait seven days to buy an asset destroys the entire value proposition of digital finance. Legacy tools default to time-delay holds precisely because they cannot evaluate real-time behavioral intent at the moment of funding.

What is the single biggest operational misconception that crypto and fintech founders still hold about the risks they carry?

Founders confuse zero chargebacks with effective risk management, Podvin says.

If a fraud rate is zero, the risk policy is failing commercially. It means the filters are set so tightly that the business is turning away profitable, legitimate customers. LexisNexis data shows that every $1 lost directly to fraud costs financial institutions over $5.00 once friction, fees, and customer churn are factored in.

CFOs and founders therefore need to judge risk policies on net P&L contribution — factoring in fraud losses, approved margin, CAC waste, and LTV — rather than celebrating sterile metrics that conceal a shrinking business.

How does nSure.ai resolve this dilemma without forcing platforms to choose between high fraud exposure and mass customer drop-off?

By shifting focus from static identity verification to real-time behavioral intent.

Instead of freezing accounts or imposing multi-day holds, adaptive AI analyzes contextual telemetry in real time, evaluating session dynamics, device signals, transaction rhythm, and cross-merchant velocity. This enables high-risk digital platforms to approve high-value deposits and funding flows instantly, keeping legitimate customers trading immediately while identifying actual bad actors behind the scenes — without adding a single point of checkout friction. How widely behavioral-intent scoring can displace hold-based defenses across regulated finance remains an open question, and it is the metric worth watching as platforms weigh conversion against compliance obligations.