Industries / Banking and fintech
AI for banking and fintech, with controls first.
Document checks, transaction reviews and support that move faster, with people approving every decision that affects a customer or a regulator.
What we can build for banking and fintech. We have not yet delivered an AI project for a bank. These are capabilities, backed by related fintech and AI work.
KYC document checks
Identity and onboarding documents read and checked, with anything unclear routed to a person.
Transaction flags for review
Unusual transactions flagged with the reasons, for your team to review and decide.
Loan and onboarding files
Data extracted from applications and supporting documents, with exceptions routed to people.
Support assistants
Answers to routine account and product questions, with a handover to staff.
Compliance reporting
Evidence gathered and reports drafted for your compliance team to review.
Knowledge assistants
Answers from policies and procedures, with a link to the source.
Audit trail
Every recommendation and approval recorded, so decisions can be traced.
Access controls
Role-based permissions and approval checkpoints for high-stakes actions.
Built on your systems
Designed to work with the core, card and CRM systems you already run. Integrations are agreed during discovery.
Match the autonomy to the task. AI prepares and flags. People decide.
- L1WorkflowA fixed, auditable path.
- L2AssistantAI drafts. A person reviews.
- L3Supervised agentAI acts and flags exceptions.
- L4AutonomousRoutine, low-risk, monitored.
- Human-ledPeople decideAI supports the analysis.
Document intake and extraction
A fixed, auditable workflow, with AI reading the documents.
L1 WorkflowKYC checks
A governed workflow, with an agent flagging anything unclear.
L1 WorkflowL3 Supervised agentTransaction monitoring
Agents flag unusual activity with the reasons. Analysts review.
L3 Supervised agentCredit, fraud and account decisions
Stay with people. AI prepares the evidence.
Human-led
Related work. We have not delivered an AI project for a bank yet. These fintech and AI engagements built the same kinds of systems.
Centralized crypto exchange
Matching engine, custody and KYC/AML built from scratch in a 20-week build.
99.9% uptime since launch Web3 · PaymentsStablecoin payment rails
Multi-currency stablecoin settlement, built and shipped in five weeks.
<1 sec settlement AI · InsuranceFull-stack AI platform for insurance
Fragmented policy, claims and correspondence data connected into one structured workflow.
~2.5x faster processingHave a banking or fintech process that runs on manual checks?
The technology behind our AI systems. The right stack depends on the model, the data, the architecture and where it runs.
Generative AI and large language models
Models, chosen per task
- OpenAI
- Anthropic Claude
- Google Gemini
- Meta Llama
- Mistral AI
- Cohere
- Hugging Face
- OpenRouter
- LangChain
- LlamaIndex
AI agents and orchestration
Tools, steps and handover
- LangGraph
- AutoGen
- CrewAI
- Semantic Kernel
- Agentic workflows
- Tool calling
- Function calling
- Multi-agent systems
Vector databases and semantic search
Grounded answers from your content
- Pinecone
- Weaviate
- Milvus
- Qdrant
- Chroma
- pgvector
- Elasticsearch vector search
AI integration and APIs
Connecting to your systems
- REST APIs
- GraphQL
- Webhooks
- Microservices
- API gateways
- SDK integrations
- Third-party AI APIs
Cloud and AI infrastructure
Where it runs
- AWS
- Microsoft Azure
- Google Cloud
- Amazon SageMaker
- Azure Machine Learning
- Google Vertex AI
- Databricks
AI evaluation and observability
Quality and cost, measured
- Model evaluation
- Prompt testing
- Hallucination detection
- Latency monitoring
- Cost tracking
- Output quality assessment
- AI observability
- Langfuse
Show the full stack 12 more layersShow fewer layers
Programming languages
What the code is written in
- Python
- Java
- JavaScript
- TypeScript
- C++
- R
- Go
- SQL
- Swift
- Kotlin
AI and machine learning frameworks
Training and running models
- TensorFlow
- PyTorch
- Scikit-learn
- Keras
- XGBoost
- LightGBM
- JAX
- Hugging Face Transformers
Natural language processing
Understanding text
- Text classification
- Sentiment analysis
- Named entity recognition
- Text summarization
- Semantic search
- Question answering
- Embeddings
Computer vision
Understanding images and video
- OpenCV
- YOLO
- Detectron2
- Image classification
- Object detection
- OCR
- Image segmentation
- Facial recognition
- Video analytics
Speech and voice AI
Listening and speaking
- Speech-to-text
- Text-to-speech
- Voice recognition
- Voice assistants
- Conversational voice AI
- Audio processing
Data engineering and processing
Moving and preparing data
- Apache Spark
- Pandas
- NumPy
- Apache Kafka
- Apache Airflow
- Databricks
- dbt
Databases and data storage
Where the data lives
- PostgreSQL
- MySQL
- MongoDB
- Redis
- Microsoft SQL Server
- Amazon DynamoDB
- Elasticsearch
- Snowflake
Containers and orchestration
Deploying and scaling
- Docker
- Kubernetes
- Amazon EKS
- Azure Kubernetes Service
- Google Kubernetes Engine
- Terraform
MLOps and model operations
Keeping models healthy
- MLflow
- Kubeflow
- DVC
- Model versioning
- Model monitoring
- CI/CD pipelines
- Automated retraining
- Performance tracking
Backend development
Services and business logic
- Node.js
- Django
- FastAPI
- Flask
- Spring Boot
- .NET
- Express.js
Frontend and application development
What people use
- React
- Next.js
- Angular
- Vue.js
- React Native
- Flutter
- Android
- iOS
Development and collaboration tools
How we ship
- Git
- GitHub
- GitLab
- Bitbucket
- Jenkins
- Jira
- Postman
Used in our delivered platforms
From first use case to production. Four stages, with a working version reviewed at each one.
-
Discovery
Map the process, the systems and data involved, and what good looks like.
Discovery notes
- The decisions and data involved
- Where manual checks sit today
- Regulatory and approval rules
-
Scope & architecture
Agree the architecture, success measures and delivery milestones.
Solution design
- Architecture and data access
- Approval checkpoints
- Success measures
-
Build
Build against real data, with a working version reviewed at each milestone.
Working build
- Working version on real data
- Compliance review
- Accuracy tests
-
Ship & support
Roll out to production with monitoring and a defined support window.
Production rollout
- Production rollout
- Monitoring
- Support window
Why Avinya for banking and fintech. Fintech systems with compliance built in, already in production.
KYC/AML built in
Our crypto exchange shipped with KYC/AML checks at onboarding, ready at launch.
Payments at speed
Our stablecoin rails settle across multiple currencies in under one second.
Card and wallet apps
A crypto-to-Visa card app with Apple Pay and Google Pay. Client name withheld.
Audit trail by design
Our insurance platform logs every record change, with role-based access.
Engagement models. Pick the shape that fits where you are.
Dedicated team
A dedicated team that works as an extension of yours, from first release to scale.
Project-based
A defined scope, milestones and delivery date for a specific product or workflow.
Consulting & advisory
Architecture and readiness advice before you commit to a build.
What our clients say.
“Avinya Labs built our full-stack AI solution, delivering scalable infrastructure, optimized AI workflows, and a seamless user experience. Their expertise accelerated our launch and significantly reduced execution risk.”
“We needed a platform that could handle complex construction workflows without breaking down every week. Avinya Labs delivered exactly that.”
“Avinya Labs built our AI travel chatbot … A highly capable team that delivers ahead of the curve.”
AI for banking and fintech FAQs.
Have you built AI for a bank before?
Not yet. We have built fintech systems, a compliant crypto exchange, stablecoin payment rails and a card app, and AI platforms with audit trails in insurance. Those engagements are linked on this page.
Will AI make credit or fraud decisions?
No. AI prepares the evidence and flags what needs attention. Credit, fraud and account decisions stay with people.
Do you handle regulatory compliance?
We design compliance into the system: KYC/AML flows, audit trails and access controls. We work alongside your legal counsel. We do not give legal advice.
Where should we start?
With one task where the data exists and the risk is contained. We map it, agree what good looks like, and build a working version you can review before anything scales.
How do you handle our data?
Access is scoped to what the system needs, with role-based permissions and audit trails where records change. We agree data handling during discovery.
Ready to turn your vision into reality?
Tell us the problem, the data, and where manual effort sits today. We'll map the path from MVP to scale.
Or email contact@avinyalabs.co