AI layer for banks and NBFCs
AiBanker puts AI into every section of a bank or NBFC – onboarding, underwriting, credit decisions and monitoring – on the systems it already runs. And it gives every customer a personal AI banker, in the app, on WhatsApp or on the web.
AiBanker is software for banks and regulated lenders. It is not a bank or a lender.
Lenders, investors and programmes that work with Altiora, the team behind AiBanker. Hover a logo to see the relationship.
Paisa247, Client. Paisa247 uses Dhana, AiBanker's voice agent, to call its new leads.
Kerala Bank, Partner. Dhana's voice agents will serve Kerala Bank.
Mayyanad Regional Co-operative Bank, MoU. Mayyanad Regional Co-operative Bank has an MoU with Altiora.
Group Meeran, Investor. Group Meeran is an investor in Altiora, which builds AiBanker.
JAIN University, Investor. JAIN University is an investor in Altiora, which builds AiBanker.
Innovation Incubator, Incubator. Altiora is incubated by Innovation Incubator.
IIT Madras, Incubator. Altiora is incubated at IIT Madras.
Kerala Startup Mission, Recognised startup. Altiora is a startup recognised by Kerala Startup Mission.
Startup India, DPIIT recognised. Altiora is a DPIIT-recognised startup under Startup India (DIPP230717).
Amazon Web Services, Cloud. Dhana, AiBanker's voice agent, runs on AWS, in Mumbai.
Google Cloud, Cloud. Some of Dhana runs on Google Cloud.Each layer is a small model built for one job, working as one agent – plugged into the systems you already run, at a fraction of the cost of big general AI.
Connects to your core banking, loan systems, credit bureau and channels. Nothing to rip out.
Dhana, our voice agent, talks to every applicant in their own language – human-like, emotion-aware, hosted in India – and completes KYC on the way.
Reads the whole file – KYC, statements, bureau data – and builds the credit picture in minutes, not days.
Scores every application with reasons a credit officer can check. The officer signs.
Sorts applications, accounts and documents into the right queues and risk buckets, automatically.
Watches every loan and account, and raises the alarm on stress before a payment is missed.
Runs on your current servers or your own cloud. Each bank's models learn only from that bank's data.
/Underwrite · Classify/
UPI lines carry shop takings, rent, chit funds and informal loans. A basic statement parser files most of them under 'others'. AiBanker reads every line – and the picture changes.
Illustration · made-up statement
Bank statement · October ↓
| Narration | Amount | Read as |
|---|---|---|
| NEFT-HDFC0004127-ACME TEXTILES PVT LTD/SALARY OCT | +₹ 32,000 | Income · salaryIncome · salary |
| UPI/CR/402211874/SRI DURGA STORES/QR SETTLEMENT | +₹ 18,500 | Transfer / othersIncome · shop sales |
| UPI/DR/398812301/RAJESH K/RENT NOV | −₹ 9,000 | Transfer / othersObligation · rent |
| ACH-DR-NBFC LOANS LTD-EMI-55102 | −₹ 6,200 | Obligation · EMIObligation · EMI |
| UPI/DR/771203990/KAVITHA M/CHITTY 12 | −₹ 5,000 | Transfer / othersObligation · chit fund |
| IMPS/DR/556677/GOLD LOAN INT/ACC 8841 | −₹ 2,400 | Transfer / othersObligation · gold loan |
Monthly income
₹50,500
Monthly obligations
₹22,600
Debt-to-income (FOIR)
45%
Stretched. Flagged for the credit officer, with reasons.
Same applicant, two credit pictures: the basic reading missed ₹18,500 of income and ₹16,400 of obligations.
For their customers
In your app, on WhatsApp or on the web – one agent that knows the customer, speaks their language and gets things done, in chat or voice.
01 · On App
“Pay Rahul 2,000 for rent”
Ready to pay Rahul Menon ₹2,000 from Savings ••4821 by UPI. Confirm?
₹2,000 to Rahul · confirm to send
02 · On WhatsApp
“See where my salary went”
₹46,320 spent this month. Rent is the biggest item, and food is 12% lower than last month.
Spending explained · ₹46,320
03 · On Web
“Move 5,000 to savings every payday”
Done. ₹5,000 moves to your emergency fund on the 1st of every month.
₹5,000 every payday · set up
04 · On App
“I have 20,000 spare. Any ideas?”
Three options from your bank that suit a medium-risk, three-year goal. You decide.
3 options · from your bank
05 · On WhatsApp
“Do I need term insurance?”
With a home loan, many people choose cover that clears the loan. Two of your bank's plans to compare.
2 term plans · ready to compare
Sample conversation with made-up data. Investment and insurance guidance covers the bank's own products, within its licences.
Who it's for
Swap manual queues for an AI layer that adapts to how your institution lends.
The AI suggests. Your credit officer decides.
Most lenders weigh three other routes to AI. Here's how AiBanker compares – and what you only get with us.
AiBanker suggestsScore 82 / 100
Approve a ₹2,40,000 personal loan for Arun K.
Made-up applicant. Nothing moves without the officer.
Compare AiBanker with
| AiBanker | General-purpose AI | |
|---|---|---|
| Where it runs | On your servers or your own cloud, in-country | Usually in the AI vendor's cloud |
| Built for | Banking, from onboarding to monitoring – ready to use | Everything – you adapt it to banking yourself |
| Cost | Light models built for banking – a fraction of the cost | Billed by the word, growing with every customer |
| Explains decisions | Reasons on file for every score. The officer signs; you can audit or switch it off | Not by default – audit trails are yours to build |
| Languages and voice | 20+ Indian languages with a human-like, emotion-aware voice | Many languages; voice and banking tone are up to you |
| Your data | Learns only from your bank's data. Nothing is shared across banks | Depends on the vendor's terms |
| Getting started | One pilot: one journey, inside your bank, judged on your own data | Build a product around the API first |
Compares approaches in general, not named products.
A human-like, emotion-aware voice in 20+ Indian languages, hosted in India, that talks like your branch.
IT, risk and audit sign off once. Every new section reuses that approval.
Same servers, same data, same approvals – add underwriting, decisioning or monitoring as you go.
Every month inside your bank adds history that only your models learn from.
The same intelligence your staff use, as a personal AI banker for your customers.
Our conversational banking is the subject of patent application 202641000710 (published, not yet granted).
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