AI in Fintech Isn’t Optional Anymore. Neither Is Getting It Right.

It is the deployment of machine learning, computer vision, and conversational AI inside regulated financial-services environments — fraud detection, underwriting, KYC document automation, AI customer service, and predictive personalization. AddWeb engineers production-grade systems with explainability, audit trails, and compliance built in from day one — not bolted on after the fact.

Discovery Sprints start at $15,000 USD. Single-use-case production builds typically range from $85,000 to $145,000 USD. Multi-model platform engagements start at $360,000 USD. Annual MLOps support contracts start at $9,500 USD per year. Final pricing is calibrated during your Discovery Call.

Yes. AddWeb Solution maintains its US headquarters in Greenville, South Carolina, with additional offices in New Jersey and Melbourne, Australia. Our delivery center is in Ahmedabad, India. This US-registered structure simplifies procurement, contracting, and IP transfer for North American and European fintech clients.

Every model we ship includes SHAP-based explainability for every decision, full audit logs of training data and inference calls, bias detection across protected attributes, model versioning with rollback, and documentation packages your compliance team can hand to regulators. We are ISO 27001 certified and have worked with institutions answering to OCC, FCA, ECB, and RBI.

A 2 to 3 week Discovery Sprint validates feasibility and produces a fixed-scope proposal. Standard production builds run 12 to 18 weeks from PO to live deployment — covering data audit, model training, MLOps setup, compliance review, parallel testing, and production rollout. Multi-model platform engagements run 4 to 6 months.

Digital banks and neobanks, traditional retail and commercial banks, lending platforms (consumer, SME, BNPL), payments and PayTech, insurtech, capital markets and wealth tech, and embedded finance. Any regulated financial workflow with repeatable decisions, document processing, or customer interactions is a candidate. We assess fit during Discovery.

Yes — fully. On final payment, all source code, trained AI models, training pipelines, calibration data, MLOps configurations, and dashboards transfer to your team. No retention. No lock-in. Master Service Agreement assigns 100% of IP to your company.

Yes. We have integrated AI into Snowflake, Databricks, BigQuery, Redshift, and on-prem PostgreSQL or Oracle environments. We have shipped models on AWS SageMaker, Azure ML, GCP Vertex, and self-hosted Kubernetes. We adapt to your stack — we do not force you onto ours.

Data residency is a first-class architectural decision. We support EU, UK, US, India, and Australia data-residency requirements. Default architecture: customer data never leaves your environment. We use federated learning, on-device inference, and synthetic data generation where regulators require it. GDPR, CCPA, and DPDP-compliant by design.

Production fraud models typically achieve sub-100ms inference latency at false-positive rates of 0.3 to 1.5 percent — calibrated to your specific transaction profile. KYC document AI typically achieves 99 percent or higher accuracy on supported document types. Final accuracy is calibrated per use case during model validation.

Bias detection is built into the model pipeline — not audited after the fact. We test for disparate impact across protected attributes (age, gender, ethnicity where regulated), document champion-challenger frameworks for risk team approval, and provide fair-lending compliance reports. Outputs map to ECOA, Fair Lending Act, and equivalent UK and EU regulations.

Packaged solutions like Sift, ComplyAdvantage, or Onfido are excellent for standardized risk problems but break down on custom fraud patterns, multi-product portfolios, or institution-specific edge cases. AddWeb builds custom systems on top of your data — typically delivering materially better accuracy on non-standard problems while owning the full stack. The right answer depends on your scale and complexity, which we assess in Discovery.

For fraud detection: typical payback in 6 to 12 months, driven by reduced fraud loss and lower false-positive customer friction. For AI underwriting: payback in 9 to 14 months from improved approval rates without raising defaults. For KYC automation: payback in 6 to 9 months from manual review reduction. Discovery includes a calibrated ROI model based on your actual loss rates, volumes, and current processes.

Yes. Schedule a Discovery Call with our AI for Fintech practice. We will review your data, your regulatory environment, your build-versus-buy options, and tell you directly whether AI is the right tool for your specific problem. If a packaged vendor would serve you better, we will say so. No sales pitch.