AI in financial services has to be accurate, auditable and compliant, no exceptions. We build systems for banks, lenders and insurers that hold up under regulatory scrutiny - RBI, SEBI, IRDAI - and still make the day-to-day work faster.
Manual underwriting takes days. Customers expect minutes. That gap sends business to fintechs willing to move faster with less rigour.
Fraud patterns shift faster than fixed rule-based systems can keep up. A static model misses the new attack it has never seen before.
KYC, AML and reporting requirements eat thousands of hours a year. Automating that work without a clear audit trail just creates new regulatory risk.
Old interfaces and manual processes push customers away. AI-powered service is no longer a nice-to-have - it's what people expect by default.
AI that reads applicant data, scores the application, runs the compliance checks and produces a decision report you can hand to an auditor.
Transaction anomaly models that update as fraud patterns change. Network analysis surfaces mule accounts and coordinated rings, not just single bad transactions.
Document verification, identity matching and sanctions screening, automated with a full audit trail for reporting and examination.
Alternative data scoring for thin-file customers, with outputs a regulator and a credit committee can both follow and explain.
Claims intake and document processing, with straight-through handling for the simple cases so adjusters can focus on the ones that need judgment.
Product recommendations, proactive alerts and AI-assisted service that take load off the call centre without making service feel impersonal.
IT governance, cyber resilience, digital lending guidelines and data localisation requirements, designed in from the start, not added later.
Cyber capability index requirements, incident reporting and controls for algo trading systems under regulated market rules.
IRDAI information and cyber security guidelines - data governance, customer data handling and claims AI kept within the rules.
India's Digital Personal Data Protection Act - consent management, data principal rights and breach notification handled properly.
Yes - underwriting AI produces a decision report alongside the score, built so a regulator and a credit committee can both follow how the decision was reached, not just a black-box number.
RBI IT governance, cyber resilience, digital lending guidelines and data localisation are designed in from the start, alongside SEBI cyber framework, IRDAI guidelines and the DPDP Act depending on whether you're a bank, insurer or market participant.
No - our models update as fraud patterns change, and network analysis surfaces mule accounts and coordinated rings on top of flagging single suspicious transactions, which is as far as a static rule-based system gets.
Document verification, identity matching and sanctions screening are automated with a full audit trail built in, so automation speeds up the process without leaving you exposed at examination time.
Yes - alternative data scoring is built specifically for thin-file customers, with outputs a regulator and credit committee can both follow and explain, not just a score with no reasoning behind it.
No - AI handles the straight-through, simple cases and produces the analysis for the complex ones, so your underwriters and adjusters spend their time on the decisions that actually need judgment.
Build compliant AI
Book a free discovery session. Tell us where the process is slowest, and we'll map what a compliant first project looks like before you commit to anything larger.
