Banking & Financial Services
AI fraud detection in banking, AML monitoring and credit risk scoring.
5+
Years of AI Delivery
100+
Projects Delivered
< 40 sec
AI Fraud Verification
Zero
External Data Exposure






























Fraudsters use automation. Regulators keep raising the bar. Static rules and manual reviews leave you exposed.
Custom-built around your data, workflows and regulators. Not templates.
Clear reasons behind every risk score.
Run in your infrastructure, AWS, Google Cloud or Azure.
Works with your existing systems from day one.
Transactions, documents and images checked in seconds.
Catches new fraud patterns that rules miss.
Detects tampered photos, IDs and documents at pixel level.
Learns normal user behaviour, flags the unusual.
AI agents triage alerts and draft reports, with human review.
Clear reasons behind every risk score.
Run in your infrastructure, AWS, Google Cloud or Azure.
Works with your existing systems from day one.
Transactions, documents and images checked in seconds.
Catches new fraud patterns that rules miss.
Detects tampered photos, IDs and documents at pixel level.
AI fraud detection in banking, AML monitoring and credit risk scoring.
Lending fraud checks, KYC automation and regulatory compliance.
Claims fraud detection and policy risk assessment.
Subsidy fraud detection and secure on-premise AI.
Stop account takeover, promo abuse and payment fraud.
Claims anomaly detection and data compliance monitoring.
10,080× faster fraud verification for the PM KUSUM Scheme, zero external data exposure for the Indian Navy and 75–80% lower review cost for Mittsure.

Replacing manual field photo audits with AI that detects pixel-level fraud no human reviewer can see.

Built entirely inside the client's own infrastructure, the system grounds every answer in verified internal documents instead of open internet knowledge.

How automated auditing replaced a five-specialist manual review chain for India's fastest-growing Ed-Tech publisher.
100+ AI and software projects shipped to production.
We test models, RAG and agent frameworks before we recommend them.
Built around your data, risk rules and regulators.
On-premise deployment with zero external data exposure.
100+ AI and software projects shipped to production.
Tech stack: PyTorch · TensorFlow · Hugging Face · LangChain · LangGraph · MLflow · AWS · Google Cloud · Azure · Kubernetes
We were mainly concerned about AI accuracy for our use case. After testing, the solution achieved around 98% accuracy on our evaluated workflow. KriraAI also worked closely with us during testing and fine-tuning, which made a big difference.
Prashant Gupta
Working with KriraAI from another country was surprisingly smooth. The team communicated clearly, understood our requirements without too much back and forth, and was comfortable working across time zones. The quality of the AI development was exactly what we were looking for.
Alexander Weber
The technical knowledge of the KriraAI team was probably the best part of the project. We could discuss AI models, APIs, data, architecture, integrations, and deployment with the same team. They were able to explain technical decisions clearly without making things unnecessarily complicated.
Rohan Shah
KriraAI delivered a custom AI solution that matched our business requirements well. From AI architecture and development to integration and deployment, the team handled the project professionally. We particularly appreciated their focus on reliability, scalability, and making the technology useful for our actual business.
William Harris
I'll be honest, I wasn't sure any team could really handle the technical complexity of our customer support workflow. KriraAI proved me wrong. They took the time to understand our process, built a solution that fit our actual needs, and tested it thoroughly before going live. The result was about a 30% improvement in processing time, but what impressed me more was how accurate and reliable it was from day one. My concerns about technical skill turned out to be completely unfounded.
Roshan Patel
AI fraud detection uses machine learning to analyse transactions, user behaviour and documents in real time. It learns from data instead of fixed rules, so it catches new fraud patterns, reduces false positives and blocks suspicious activity within seconds.
AI scores every transaction for risk, compares it with normal behaviour, links related accounts and detects tampered images or documents. High-risk activity then triggers alerts, blocks or extra verification automatically.
AI automates KYC checks, AML screening, transaction monitoring and regulatory reporting. It cuts manual work, keeps processes consistent and creates full audit trails, lowering compliance cost and penalty risk.
AI-based AML compliance uses machine learning to detect money laundering, mule accounts and suspicious transactions. It ranks alerts by risk, reduces false positives versus rule-based AML software and speeds up STR/SAR preparation.
AI risk management uses artificial intelligence to identify and predict credit, fraud, operational and third-party risk. It turns data from many sources into risk scores and dashboards so teams can act before losses happen.
Fraud detection software finds suspicious activity as it happens. Fraud prevention software stops fraud before it succeeds through blocks and step-up checks. KriraAI's AI solutions combine both.
Yes. It handles high transaction volumes, adapts to digital fraud tactics and supports AML, KYC and monitoring rules. KriraAI builds custom solutions for large institutions and growing fintechs, with on-premise options.
It is usually more accurate than rule-only systems because it learns complex patterns and improves with feedback. Accuracy depends on data quality and tuning; KriraAI clients report around 98% accuracy on evaluated workflows.
KYC automation uses AI to verify identity during onboarding. It extracts ID data, checks authenticity, matches faces with liveness detection and screens sanctions lists, cutting onboarding from days to minutes.
At KriraAI, most AI projects take 4 to 16 weeks from discovery to deployment. A focused pilot, such as real-time fraud detection for one product, can go live sooner and then scale.