Every compliance head in Indian fintech has a story about a vKYC call that dropped at the worst possible moment. What they rarely agree on is why it dropped. And that disagreement is costing the industry billions. At scale, even a 5% preventable drop-off translates into crores in lost revenue.
The era of volume-driven onboarding is over. For years, financial institutions measured success by the sheer number of users entering the top of the funnel. But with tighter RBI controls and increasingly rigorous internal audits, the 2026 benchmark has shifted: it’s no longer about how many users you onboard, but how many high-value users you verify without incurring compliance risk or burning through operational budgets.
Video KYC (vKYC) sits at the center of this pressure. It’s mandatory, it’s expensive, and it’s one of the highest-friction steps in the customer journey.
Having processed 10 million anonymized vKYC verifications, HyperVerge has accumulated enough data to identify a clear pattern: the institutions struggling with high drop-offs aren’t losing because of bad strategy. They’re losing because they’re optimizing against myths.
TL:DR: In this blog, we summarize the myths we have uncovered in our 2026 Video KYC benchmarks report, to clarify what’s really happening when video KYC calls drop off.
Myth 1: Agent connectivity is the primary cause of dropped calls
When a call fails, the instinctive reaction is to audit the agent’s internet connection or the office lease line. Operations heads have been doing this for years. The data, however, points elsewhere.
Across 10 million verifications, the single biggest cause of call failure isn’t agent infrastructure — it’s the customer’s network.
Call it the 3G reality: in India’s geographically and technologically diverse landscape, a significant portion of users — particularly savings account customers — are connecting from areas with inconsistent bandwidth.
Most platforms rely on standard WebRTC implementations, which are surprisingly fragile under real-world conditions. These systems tend to freeze video or drop audio entirely when packet loss reaches just 15%.
That threshold gets crossed constantly in semi-urban and rural areas, and the result is silent attrition: users who tried to complete verification but couldn’t, and never came back.
High-performing systems handle this differently. Rather than treating signal degradation as a terminal failure, they’re engineered for resilience. They keep the audio channel alive even when video quality drops to 1 frame per second, and prioritize audio packets during bandwidth dips so the interaction can continue.
Institutions using standard WebRTC setups often lose 15–30% of potential volume compared to those running optimized stacks, with roughly a third of savings account drop-offs attributable to network issues alone.
The same fragility shows up in document capture. PAN-related issues account for 10.09% of drop-offs in lending, often because systems require manual button-clicks or specific lighting conditions that users in the field simply can’t provide. Computer vision that captures a document the moment it enters the frame removes that friction entirely. It’s a small change with an outsized impact on completion rates.
Myth 2: You should aim for uniform conversion rates across all products
Applying a single conversion target across an entire product portfolio is intuitive but wrong. It treats a credit card applicant and a savings account opener as interchangeable. BUT they aren’t.
The difference is intent. A credit card applicant is actively seeking a line of credit. They’ve made a decision, they want to complete the process, and the data reflects that: credit card vKYC achieves an 89% end-to-end conversion rate.
A savings account, by contrast, is often a low-intent product, something a user signed up for during a promotional campaign or at a partner’s suggestion.
Here’s what the benchmarks actually look like:
| Vertical | E2E Conversion | Call Started Success | Agent Approval |
| Credit Cards | 89% | 95% | 66% |
| Lending | 65–75% | 75–85% | 67% |
| Savings Accounts | 45% | 63% | 37% |
The most telling figure is the savings account approval rate: 37%. That number alarms a lot of product managers. It shouldn’t.
A high rejection rate in a low-intent product typically reflects a compliance filter doing its job, which is screening out incomplete applications and poor-quality feeds. It is not a technical failure.
The more productive question isn’t “why is our savings conversion low?” It’s “Are we interpreting our metrics through the right product lens?”
Myth 3: Longer calls mean more thorough compliance
This one is intuitive and almost entirely wrong. The belief that call duration correlates with diligence — that an agent who stays on longer is doing more thorough checks — ignores what actually drives long calls: inefficiency.
The AHT benchmarks for high-performing institutions tell a clear story:
- Credit Cards: 1 minute 17 seconds
- Lending: 1 minute 57 seconds
- Savings: 2 minutes 02 seconds
If your average handle time is consistently over 2 minutes, the problem is almost certainly the script, not the agent. One of the most common causes: agents asking questions that the user already answered in the lead form. That redundancy adds 20–30 seconds of friction per call, frustrates users, and adds operational cost, with zero compliance benefit.
Real diligence in 2026 doesn’t come from longer conversations. It comes from systems that capture geolocation, timestamps, and full audit trails automatically, and that flag compliance gaps in real time rather than surfacing them three days later in a manual QC review. Concurrent auditing, where breaches are caught while the call is happening, lets institutions maintain 100% compliance without keeping the customer on the line a second longer than necessary.
A quick diagnostic for your vKYC dashboard
If you’re sorting through competing metrics, three questions will tell you quickly whether your process is meeting the current standard:
Is your credit card E2E conversion below 80%?
High-intent users are dropping off before they reach the video stage. Look at your pre-call funnel — document capture friction and complex forms are the usual culprits.
Are network-related savings drop-offs above 40%?
Your video stack is likely failing the 3G reality test. The configuration is too heavy for the bandwidth conditions your users are actually operating in.
Is your call duration more than 30 seconds above the industry average?
Audit your agent scripts. That time is operational budget being spent on conversational filler that adds no regulatory value.
One more metric worth tracking: re-attempt rates. The data points to a sweet spot of 1.4 attempts per user. A rate of 1.0 suggests the funnel is too strict — legitimate users are being rejected after a momentary signal drop. A rate of 2.0 or above suggests a broken technical process, one where users are retrying just to clear system bugs rather than resolve genuine issues.
What you actually need in 2026
The institutions winning in this environment aren’t winning on features. They’re winning on stability. The smallest downtime during a peak period, a Diwali sale, a product launch, can translate to millions in lost onboarding revenue.
We have often observed a 10x traffic spike during festival periods. Systems not built for that reality become a liability precisely when it matters most.
The model that’s emerging pairs human agents with real-time AI assistance, not to replace the human interaction, but to make it more consistent. AI that prompts agents in real time, ensures mandatory script lines aren’t missed, and flags compliance gaps as they happen can reduce drop-offs by nearly 60% and cut manual audit load by 40%.
As the RBI moves past the sandbox phase, real-time breach flagging and concurrent auditing will shift from differentiators to baseline requirements.
The gap between market leaders and laggards isn’t primarily about technology investment. It’s about whether you are optimizing for the conditions your customers actually face or for the conditions that look good in a testing lab.
Where do you think you stand as per these benchmarks? Let’s talk about how we can optimize your vKYC workflow!
