Frontier Intelligence forTailored Onboarding
AI-native decision systems for regulated finance. Grounded in your context, and governed by your rules.
KYC / KYB Agent
Document Extraction Agent
Credit Underwriting Agent
Asset Due Diligence Agent
Property Due Diligence Agent
Orchestration Agent
AI Decisioning
AI that already knows what to look for
Sharper decisions on every customer, every risk, every channel. At the speed the moment actually requires.
01ONBOARDING
Real customers in. Bad actors out
Voice, video, and digital signals read in real time across every channel. Deepfake, synthetic identity, and mule checks run quietly in the background.
02UNDERWRITING
1,000+ signals. One clear decision. In seconds
Underwriting agents pull data across multiple sources and return a verdict before manual review would even get started. No data dumps, no ambiguity.
03PRIVACY
Every signal. Inside DPDP
Privy by IDfy keeps every agent decision auditable and DPDP-ready. Consent in control, data fully discovered, compliance continuously monitored. Every call it makes, replayable.
Autonomous AI agents for onboarding, underwriting, and privacy teams
Choose one to watch it work, explore the agent stack, or create your own.
Don't see what you need?
Let's build the right agent for youLeading AI transformation, like we've done in many waves before
Every category-defining shift in Indian finance has had IDfy in it early, and often first. Two live outcomes, and four industry firsts.
1st
Video KYC
Largest Video KYC setup — for the largest private bank in India.
1st
Tech-enabled BGV
First tech-enabled background verification at scale.
1st
DPDP Live
First live DPDP implementation for a major Indian bank.
90%
IPO Diligence
Diligence on ~90% of India's mainboard IPOs.
We’ve been thinking about AI, before it got cool.
While everyone caught up, we were already shaping what sovereign AI looks like for India’s financial system. The research, frameworks and perspectives behind it, right here.
Frequently asked questions
Agentic AI is basically AI that can take a task and work through it on its own instead of waiting for someone to tell it what to do at every step. Traditional automation is usually built around fixed rules, if this happens, do that. It works well when everything follows the expected process, but struggles when something unusual comes up.
In banking, an AI agent could take a loan application, collect information from different systems, run the required checks, apply the bank's policies, and then give the underwriter a clear view of the case. So rather than automating just one part of the process, it can handle the whole workflow.
The main difference is that traditional software follows a process that someone has already defined. An AI agent can work out the next step based on what it finds.
Take a credit assessment. An agent could look at GST data, bureau records and the bank's own information, compare them, spot something that doesn't add up, and then check which credit policy applies. If there is an exception, it can flag it rather than simply stopping the process.
That's useful in banking because a lot of processes aren't as straightforward as a fixed set of rules makes them look.
An AI agent can take a loan application and work through the different checks that an underwriter would normally have to do manually. It can pull credit bureau data, check government records, look at the customer's existing information with the bank, and compare everything against the lender's credit policy.
It can also identify gaps or inconsistencies and bring those to the underwriter's attention. Once the checks are done, the agent can put everything together and provide a recommendation or summary for the human reviewer.
The big difference is that the analyst isn't spending most of their time collecting information and jumping between systems. The agent does that work in the background, leaving the person to focus on the cases where actual judgement is needed.
Yes. The agent can be configured around the lender's existing credit policies rather than making decisions based on some generic set of rules. For a bank or NBFC, that could include its MSME lending policies, risk limits, eligibility criteria, and documentation requirements.
This is important because every lender has its own way of assessing credit. The agent needs to follow those rules and, just as importantly, show how it arrived at a particular recommendation. That gives the credit team a clear trail to refer back to during audits or regulatory reviews.
IDfy has a range of AI agents covering different parts of the financial services workflow. For onboarding, its KYC and KYB agents can handle identity and business verification using digital, voice, and video signals. For lending, the Credit Underwriting Agent brings together different checks to assess a borrower and arrive at a credit recommendation. IDfy also has agents focused on areas such as asset, merchant, and property due diligence. On the data and privacy side, there are agents for data discovery and privacy compliance, including requirements related to the DPDP Act. These agents can be used individually, or connected together when a process requires several of them.
IDfy's AI agent platform is designed for banks, NBFCs, and other financial institutions that want to automate processes involving a lot of data, checks, and decision-making. The use cases cover areas such as customer onboarding and fraud checks, retail and MSME credit underwriting, corporate and merchant due diligence, property and asset assessment, and data privacy compliance. Financial institutions can use the agents that are already part of IDfy's platform and merge with their institutional knowledge, or work with IDfy to build an agent around a specific process or requirement they have.
IDfy has built auditability and privacy controls into the way its agents operate. Privy by IDfy is designed to keep track of consent, data usage, and the actions taken by an agent. This creates a record of what the agent did and why, which is important when you're dealing with regulated financial processes. It also makes it easier for institutions to review decisions and provide an audit trail when required.
For banks and NBFCs, the idea is that compliance shouldn't have to be added as a separate layer after deploying AI. It needs to be part of the system from the beginning.
Adding AI to an existing loan origination system usually means taking the process that's already there and adding an AI layer somewhere in the middle. That can make certain steps faster, but the overall workflow and its bottlenecks largely remain the same.
IDfy takes a different approach by starting with the underwriting decision itself. The agent figures out what information it needs, which policies it needs to apply, what checks are required, and what the final outcome should look like.
So the idea isn't just to give an underwriter another AI-powered tool. The agent handles much of the underwriting process itself, while the human steps in when a decision needs review or judgement.