Can we use a credit bureau score in scoring?

You can store it on the lead record and it feeds the Fit score through your ICP configuration. Leadkaun will not route leads on it, assignment is manual, but the grade a rep sees will already reflect it.

Does it integrate with our LOS (Loan Origination System)?

Not in Phase 1. Export and import via CSV.

How is DSA/agent performance tracked?

Source-attribution with agent ID; per-agent rollups on analytics.

Does Leadkaun replace my CRM?

No. Leadkaun runs alongside your CRM. Your CRM stays the system of record for what happened; Leadkaun is the behaviour layer that decides what happens next, grading every lead A–F, ranking each rep's Priority Queue, and surfacing the ₹ at risk. Most teams import a CSV and run both in parallel to measure the difference first.

Does it work with WhatsApp?

Yes. WhatsApp is a first-class signal. Reps log each exchange in three taps (stage, intent, outcome) from any regular WhatsApp account, no Business API needed, and it feeds the Intent Score. Automated sending via a BSP is on the roadmap; today the logging is manual.

Do I pay extra for each sales rep?

No. Pricing is flat per account, so a five-rep team and a fifteen-rep team on the same tier pay the same. That's deliberate: it means you can put every rep on Leadkaun without the per-seat maths that makes teams ration access.

Fintech · Mumbai

Fintech Lead Management in Mumbai.

Mumbai-based fintech teams use Leadkaun to grade every lead A–F, build each rep's Priority Queue, and surface missed revenue in rupees. Setup the same day.

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Quick answer

How does Leadkaun help fintech teams in Mumbai manage leads?

Leadkaun grades every fintech lead in Mumbai A–F across fit, intent, and quality in real time, then builds each rep a live Priority Queue so the highest-intent enquiries get worked first. WhatsApp is a first-class 3-tap signal, and missed revenue surfaces in rupees. Setup happens the same day, at flat INR pricing.

Typical deal size

₹50k–₹5Cr disbursement / ₹5L–₹5Cr AUM

Sales cycle

3 days to 120 days

Primary channels in Mumbai

aggregators · Google Ads · Facebook Ads

01Why Mumbai fintech teams lose deals

The patterns we see every week.

Document-collection drop-off

Application started, KYC documents pending, applicant goes silent. ₹ lost in 14 days.

Agent-channel blind spots

DSA network sends leads; agency performance not visible centrally.

Cross-sell misses

Personal loan applicant is a mutual-fund prospect; nobody connects the dots.

02How Leadkaun helps fintech teams in Mumbai

What we configure on day one.

01
KYC/document-stage tracking inside lead record
02
Agent-performance rollups per DSA
03
Cross-product flag (loan applicant → wealth prospect)
04
Credit-score-aware assignment (high score → prime; low → sub-prime team)
05
Compliance audit export

Mumbai context

Fintech teams in Mumbai, Maharashtra typically source leads from aggregators, Google Ads, Facebook Ads. Financial capital, strongest BFSI + fintech concentration. All of those channels flow into Leadkaun via CSV upload or manual entry in minutes (a Google Sheets connector is on the roadmap), where every enquiry is scored and graded A–F before it reaches a rep.

Commercial activity in Mumbai clusters around BKC, Lower Parel, Nariman Point, Andheri, with the local economy built on financial services HQs, media, IT offices, and diamond trade, the areas a fintech pipeline here most often draws from.

03How the grade works

How every fintech lead is graded A–F.

No black box. Each grade is three transparent 0–100 scores combined against fixed, auditable thresholds, and stale leads surface as ₹ at risk before they go cold.

Fit

0–100

How closely the lead matches the ICP you set, industry, state, business type, decision-maker role and budget band. This is the part you shape: you configure who a good customer is, not the maths behind it.

Intent

0–100

Engagement and signal events, source strength, WhatsApp replies, pricing-page visits, callbacks. Intent decays as a lead goes silent, so a hot lead that stops responding cools automatically instead of sitting falsely high.

Quality

0–100

Data reliability, completeness, phone and email validity, junk and duplicate detection. If Quality is too low the lead is capped down the grades, so bad data can never masquerade as a good lead.

The threshold

A lead is Grade A when Fit ≥ 65, Intent ≥ 60 and Quality ≥ 60, the rest step down through B–F on the same fixed cut-offs. Because the weights are identical for every account, the grade stays explainable and comparable: no per-customer tuning, no hidden model.

In fintech, Fit is ticket size and regulatory segment, an enquiry outside the segment you're licensed for cannot convert regardless of intent.

04Who signs off

Who decides, on a fintech lead in Mumbai.

The people who decide what happens to a fintech enquiry in Mumbai are not one audience. Each is asking their own question of it.

Head of Sales

senior

Cares less about the single lead than about whether the team is working the right ones this week.

Founder

senior

Wants to know whether this enquiry is worth anyone's afternoon before the team spends one.

Branch Manager

senior

Is answerable for what the branch did with the enquiry, which makes a written trail worth more than a recollection.

This is why Fit is scored separately from Intent: how keen someone sounds and whether they are the person you can actually sell to are two different questions. Both are published with their weights.

05What a team here uses most