OnEMI Technology Solutions Limited (Kissht)
Key Metrics at a Glance
| Metric | Value | YoY Change |
|---|---|---|
| AUM (Mar-26) | ₹7,066 Cr | +73% |
| PAT (FY26) | ₹281 Cr | +75% |
| GNPA (Mar-26) | 2.12% | –77 bps |
| RoAE (FY26) | 23.97% | +622 bps |
1. Summary
OnEMI Technology Solutions Limited, operating under the consumer brand Kissht, is a full-stack digital lending platform founded in 2016 and headquartered in Mumbai. The company listed on NSE and BSE on 8 May 2026, one of India’s first technology-led digital lenders to enter public markets. The IPO was priced at ₹162–171/share, raised ₹850 crore in fresh capital, and listed at ₹191 (an 11.7% premium), with QIB subscription at 25.9x.
FY2026 was a defining year:
- AUM grew 73% YoY to ₹7,066 crore
- Total income surged 63% to ₹2,209 crore
- PAT expanded 75% to ₹281 crore
- GNPA declined 77 bps to 2.12%
- RoAE of 23.97% and RoAAUM of 5.05% — rare in NBFC/fintech at this growth rate
Kissht’s competitive edge rests on three pillars:
- Proprietary AI/ML underwriting engine processing 7,000+ variables
- Technology-led collections infrastructure achieving 97%+ collection efficiency
- Balanced on-book/off-book AUM model (~50/50 split) optimising capital deployment
The company services 11.76 million cumulative customers across 17,000+ pin codes with a median customer CIBIL score of 746.
FY27 management guidance: 40%+ AUM growth | GNPA <2.25% | RoAAUM 4.5–5.0% | RoAE 19–21% | 10–15% YoY reduction in impairment cost.
2. Company Overview
2.1 Business Model
Kissht operates as a full-stack digital lending NBFC with two core segments:
Personal Loans (PL) — 92.7% of AUM (₹6,548 Cr)
- 100% digital origination
- Ticket sizes up to ₹5 lakhs, tenures up to 5 years
- Serves salaried and self-employed borrowers
Loan Against Property (LAP) — 7.3% of AUM (₹518 Cr)
- Secured lending for property owners, MSMEs, self-employed
- Ticket sizes up to ₹15 lakhs, tenures up to 10 years
- 98 branches across 8 states/UTs | Conservative 48% LTV
The technology stack is cloud-native: unified sourcing engine, AI/ML credit & fraud decisioning, loan origination & management system, and a proprietary Automated Collections System (ACS).
2.2 Market Opportunity
India’s combined PL + LAP market stood at ₹11.6 trillion in FY25 and is projected to reach ₹33.4 trillion by FY30 (24% CAGR). Digital/new-age lenders accounted for ₹0.6 trillion of this in FY25 and are projected to grow at a ~48% CAGR to ₹4.1 trillion by FY30, a massive secular tailwind.
2.3 Customer Profile
| Metric | Data |
|---|---|
| Average Customer Age | 32 years |
| Customers aged under 35 | 73% |
| Median CIBIL Score | 746 |
| Customers with 700+ CIBIL | 95.5% (FY26) |
| Salaried vs Self-Employed | 48% / 52% |
| Customers in Top 100 Cities | 81% |
| FOIR (New Customers) | 30.2% (–14% YoY, improved) |
Registered user base: 68.55 million (up 29% YoY) - a deep funnel for cross-sell of PL, LAP, and insurance products.
2.4 Journey & Milestones
- 2016: Founded in Mumbai
- 2019–2026: AUM CAGR of ~560% (ex-COVID period)
- 2023: LAP product launched
- 2025: AI-led transformer-based underwriting deployed
- 2025: Sachin Tendulkar onboarded as brand ambassador
- May 2026: Listed on NSE & BSE
3. Financial Performance
3.1 Full Year P&L
| Particulars (₹ Cr) | FY2024 | FY2025 | FY2026 | YoY Growth |
|---|---|---|---|---|
| Total Income | 516.82 | 1,352.49 | 2,209.25 | +63.3% |
| Finance Cost | 25.68 | 199.33 | 282.25 | +41.6% |
| Net Total Income | ~491 | 1,188 | 1,927 | +62.2% |
| Operating Expenses | — | 645 | 1,091 | +69.2% |
| Pre-Provision Operating Profit | — | 543 | 836 | +53.8% |
| Impairment Cost | — | 327 | 459 | +40.4% |
| Profit Before Tax | 53.85 | 216.27 | 376.68 | +74.2% |
| Profit After Tax (PAT) | 62.62 | 160.62 | 281.45 | +75.2% |
Key insight: Impairment cost grew at only 40% vs income growth of 63% - a clear sign of improving credit quality and operating leverage taking hold.
3.2 Quarterly Performance (Q4 FY2026)
| Particulars (₹ Cr) | Q4 FY25 | Q3 FY26 | Q4 FY26 | QoQ | YoY |
|---|---|---|---|---|---|
| Total Income | 373 | 601 | 625 | +4.1% | +67.5% |
| Finance Cost | 52 | 75 | 77 | +2.0% | +46.4% |
| PPOP | 146 | 226 | 224 | –0.6% | +54.1% |
| Impairment Cost | 76 | 122 | 114 | –6.5% | +51.2% |
| PAT | 54 | 77 | 82 | +6.6% | +51.7% |
| RoAAUM (annualised) | 5.9% | 5.4% | 5.0% | –32 bps | –82 bps |
Q4 FY26 saw sequential PAT growth (+6.6% QoQ) while impairment cost actually declined 6.5% QoQ - a strong asset quality signal.
3.3 DuPont Analysis (% of Avg. AUM)
| Metric | FY2025 | FY2026 | YoY Δ |
|---|---|---|---|
| Total Income / Avg. AUM | 40.4% | 39.6% | –0.8% |
| Finance Cost / Avg. AUM | 4.9% | 5.1% | +0.2% |
| Net Total Income / Avg. AUM | 35.5% | 34.5% | –1.0% |
| Operating Expenses / Avg. AUM | 19.3% | 19.5% | +0.2% |
| Pre-Provision Operating Profit / Avg. AUM | 16.2% | 15.0% | –1.2% |
| Impairment Cost / Avg. AUM | 9.7% | 8.2% | –1.5% |
| RoAAUM (PAT / Avg. AUM) | 4.8% | 5.05% | +0.25% |
The AI-underwriting flywheel in action: better credit selection → lower defaults → 150 bps reduction in impairment/AUM → 25 bps RoAAUM expansion, despite slight NTI yield compression.
3.4 Balance Sheet Summary
| Particulars (₹ Cr) | Mar-25 | Dec-25 | Mar-26 |
|---|---|---|---|
| Loans (Net) | 2,158 | 2,679 | 3,189 |
| Cash & Equivalents | 133 | 189 | 209 |
| Total Assets | 2,701 | 3,569 | 3,989 |
| Total Borrowings | 994 | ~2,048 | 1,928 |
| Total Equity (Net Worth) | 1,006 | 1,254 | 1,343 |
| Debt-to-Equity (On-book) | 1.5x | 1.6x | 1.8x |
| Book Value Per Share (Diluted, ₹) | 75.7 | 95.0 | 101.5 |
| CRAR | — | — | 25.28% |
Net worth has grown 2.4x over three years purely through retained earnings. Post-IPO ₹850 Cr provides ample headroom for AUM growth without near-term equity dilution.
4. Asset Quality
4.1 Stage Classification Trends (On-book AUM)
| Stage | Mar-25 | Jun-25 | Sep-25 | Dec-25 | Mar-26 |
|---|---|---|---|---|---|
| Stage 1 (Current) | 93.6% | 92.5% | 93.0% | 93.2% | 95.5% |
| Stage 2 (SMA) | 3.5% | 3.9% | 4.1% | 3.9% | 2.4% |
| Stage 3 (NPA) | 2.9% | 3.6% | 2.9% | 2.9% | 2.1% |
Stage 1 hit a historical high of 95.5% in Mar-26.
4.2 Key Asset Quality Metrics
| Metric | Mar-25 | Mar-26 | Change |
|---|---|---|---|
| GNPA (Stage 3) | 2.89% | 2.12% | –77 bps |
| NNPA (Net NPA) | 0.25% | 0.29% | +4 bps |
| Provision Coverage Ratio | 91.48% | 86.15% | –533 bps |
| Collection Efficiency (DPD 30) | 97.01% | 97.15% | +14 bps |
| Bounce Rate | 13.1% | 13.1% | Stable |
| 1st EMI DPD 90+ | 1.5% | 0.7% | –80 bps |
Provision coverage at 86.15% remains conservative, with ₹136 Cr management overlay. Combined Stage 2 & 3 provision coverage including overlay stands at 166% — a substantial buffer.
5. Technology & AI Strategy
5.1 Underwriting - The Core Moat
Kissht’s underwriting engine has evolved: decision trees → gradient boosting (XGBoost/GBT) → AI-led transformer models (since 2025).
| Underwriting Metric | Detail |
|---|---|
| Variables used | 7,000+ (up from 25 in 2019) |
| Model iterations | 40+ |
| Training data | 10 million+ decisions over 5 years |
| Model AUC (V40, 2026) | 74% (+8pp since 2023) |
| Risk separation vs bureau score | ~2.5x better |
| Fraud triggers | 200+ (across 6 fraud types) |
The multi-agent fraud system combines Vision (document forgery), Text (NLP on bureau/KYC), Tabular (multivariate anomaly), and Graph (network/relationship fraud) agents.
5.2 AI Across the Lending Lifecycle
Customer Acquisition: Conversational AI voice agent for loan offer clarification — drives +10pp uplift in offer-selection page conversion.
Collections: Voice agents for DPD 1–10 achieve >70% of human recovery rates. Self-pruning dial queues reduce wasted calls.
System Development: 80%+ of code written with AI assistance — accelerating product/analytics cycles.
Customer Support: 100% call quality control by AI (vs. ~10% industry standard). Average time-to-answer reduced from ~200s to under 90s.
LAP Smart Documents: +30% First-Time-Right rate and ~50% reduction in document-stage credit queries via LLM-assisted credit memos and 30+ image analytics features for property valuation.
Management notes the 400-person tech & analytics team is a fixed cost that will not scale proportionally with 2–3x AUM growth - a key operating leverage driver.
6. Liability Profile & Capital Adequacy
6.1 AUM Mix
Kissht deliberately maintains a ~50/50 on-book/off-book split (₹3,556 Cr on-book, ₹3,510 Cr off-book as of Mar-26).
| Off-book Arrangement | Share | Description |
|---|---|---|
| 100-0 Arrangement | 65% | Entire loan on partner’s books |
| Co-lending | 35% | 5–20% retained on own books |
| Direct Assignment | ~1% | Post-disbursement assignment |
6.2 On-book Debt Profile
| Metric | Value |
|---|---|
| Total Borrowings | ₹2,396 Cr |
| Average Cost of Borrowings | 14.16% |
| Debt-to-Equity | 1.78x |
| Lending Partners | 45+ |
| Credit Rating (Long-term) | A–/Stable (CRISIL, India Ratings, Acuité) |
Borrowing mix: 54% term loans, 37% NCDs, 8% pass-through certificates. Lender mix: 46% banks, 33% NBFCs, 20% fund houses. Key lenders include SBI, ICICI Bank, YES Bank, IDFC First Bank, Northern Arc, Piramal Finance, and Goldman Sachs.
6.3 Capital Adequacy
| Metric | Value |
|---|---|
| CRAR (Total) | 25.28% (vs. 15% RBI minimum) |
| Tier 1 Capital Ratio | 24.4% |
| Net Worth | ₹1,343 Cr (+33% YoY) |
| ALM Position | Positive surplus in all maturity buckets |
7. Risk Management
7.1 Enterprise Risk Architecture
- Underwriting Models: 40 deployed models, 400+ key variables, 41 data scientists
- Fraud Detection: 200+ real-time triggers, 20 million+ KYC-verified customers; covers impersonation (45+ checks), device fraud (90+), documentation (60+), geo-location (30+), UPI (10+), and e-commerce (10+)
- Early Warning: 450+ pin codes with disbursements paused based on risk signals; 0.57% applications rejected for risky investment behaviour
- Automated Collections (ACS): 97%+ efficiency, 7,000+ field agents, 1,000+ tele-callers, >95% in-house
7.2 Collections Infrastructure
| Metric | Value |
|---|---|
| NACH/UPI Mandate Availability | 100% |
| Digital Collection Share | 99.2% |
| Collections via Agency | <5% |
| Collections In-house | >95% |
| Field team | 7,000+ |
| Tele-calling team | 1,000+ |
| Pin codes covered | 17,000+ |
8. Management & Governance
| Name | Role | Background |
|---|---|---|
| Ranvir Singh | Founder & CEO | McKinsey, IIM Bangalore; ‘Most Promising Business Leader of Asia 2023–24’ |
| Krishnan Vishwanathan | Founder & CFO | McKinsey, Yale School of Management |
| Neha Shivran | Chief Data & Analytics Officer | TransUnion, RBS, Cranfield University |
| Sandeep Kadam | Chief Technology Officer | Yahoo, Al Baraka Bank, USC |
Board: 6 directors - 2 executive (founders), 1 nominee, 3 independent NEDs. Board committees: Audit, NRC, CSR, Risk Management, Stakeholders’ Relationship (SEBI LODR 2015 compliant).
9. FY2027 Management Guidance & Strategic Outlook
| Metric | FY26 Actual | FY27 Guidance |
|---|---|---|
| AUM Growth | 73% | 40%+ |
| GNPA | 2.12% | <2.25% |
| Impairment Cost | — | 10–15% YoY reduction |
| RoAAUM | 5.05% | 4.5–5.0% |
| RoAE | 23.97% | 19–21% |
The moderation in return ratios is deliberate: lower loan pricing attracts better-quality borrowers → lower impairment → superior risk-adjusted returns over the medium term.
Key Growth Drivers
- LAP Expansion: From 98 to ~180 branches by end FY27
- Operating Leverage: Fixed tech/analytics costs (~400 staff) not expected to scale with 2–3x AUM growth
- Cost of Funds: Credit rating improvements to reduce the 14.16% average borrowing cost
- Cross-sell: 68.55 million registered users as funnel for repeat PL, LAP, and insurance
- GenAI Feature Store: LLM-driven feature synthesis to further boost underwriting AUC
Key Risks to Monitor
- Regulatory: RBI policy changes on digital lending, FLDG norms, or NBFC capital requirements
- Credit cycle: Macro deterioration could reverse improving GNPA trend (India’s mass-market credit saw stress in FY25)
- Concentration: 92.7% PL exposure is unsecured - AI underwriting helps but systemic shock remains a tail risk
- Competitive intensity: Incumbent banks and fintechs also investing in AI underwriting
- Borrowing cost: RBI rate actions could widen/narrow spreads materially
10. Valuation & Market Context
| Metric | Value |
|---|---|
| Current Market Price | ₹265/share |
| Market Capitalisation | ~₹4,465 Cr |
| IPO Price Band | ₹162–171 |
| Listing Price | ₹191 (+11.7% premium) |
| Price since listing | ~+55% |
| Book Value Per Share (Diluted) | ₹101.5 (Mar-26) |
| Price to Book | ~2.6x |
Trading at ~2.6x book - a premium validated by 25.9x QIB subscription in the IPO, with participation from HDFC MF, ICICI Prudential MF, Goldman Sachs, Citi, and BNP Paribas.
Valuation re-rating catalysts:
- Consistent GNPA below 2%
- RoAE sustaining above 20%
- LAP AUM reaching 15%+ of total (secured-lending re-rating premium)
11. Conclusion
OnEMI Technology (Kissht) presents a compelling picture of a technology-first digital lender that has successfully navigated a sector-wide stress cycle and emerged with strengthened fundamentals.
FY2026 delivered across all fronts:
- 73% AUM growth
- 75% PAT growth
- 77 bps GNPA improvement
- 25 bps RoAAUM expansion — achieved in a challenging consumer credit environment
The medium-term thesis rests on three pillars:
- Operating leverage - fixed-cost technology platform serving a rapidly growing AUM base
- Continued GNPA improvement toward sub-2% guidance
- LAP scaling - providing portfolio granularity and a secured-lending re-rating
Management’s guided 40%+ AUM growth for FY27 in a large, digitally under penetrated market is credible given the infrastructure already in place.