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Arixent

Industries

Banking, Financial Services & Insurance Engineering

Onboarding, claims and risk reviews stall when staff must read documents and re-key evidence across systems. We build controlled document workflows and data platforms with confidence checks, reviewer handoffs and traceable decisions.

Talk about your sector
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Overview

Banks, lenders, insurers and fintechs run on documents, decisions and trust. AI can read the documents and support the decisions, but only inside the controls that regulators, auditors and customers expect. That combination of ambition and control is where our financial services work lives.

We build document intelligence for onboarding and claims, risk and fraud models with explainability, data platforms fit for regulatory reporting, and the digital products customers actually use. Our teams work to the standards financial institutions require in the regions our clients operate in.

AI Agents & Intelligent Automation: Agentic systems and automation that complete multi-step work under human oversight.

Data Science & Predictive Modeling: Forecasting, churn, risk and optimization models, delivered by embedded data science teams.

AI Consulting, Strategy & Governance: Use-case discovery, readiness, a roadmap and the governance that keeps AI defensible.

For a representative delivery approach, read Document intelligence for faster customer onboarding.

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Challenges

What gets in the way

Manual document handling

Onboarding, KYC, underwriting and claims still depend on people reading and re-keying paperwork.

Models that cannot be explained

Risk and fraud models face scrutiny from regulators and internal validation teams.

Legacy core systems

Decades-old platforms make new products slow to launch and integrations fragile.

Data lineage for reporting

Regulatory reports require traceable, reconciled data across many source systems.

Customer experience expectations

Digital-first competitors set the bar for onboarding, servicing and support.

Fraud that adapts

Rules-based detection lags behind new fraud patterns and creates false positives.

Bring the use case. We will bring the engineering point of view.

Where it fits

Typical use cases

  • Automated KYC and onboarding document processing
  • Claims intake, triage and fraud screening
  • Credit decision support with explanations
  • Transaction monitoring and anomaly detection
  • Customer service assistants for banking and insurance
  • Regulatory reporting data pipelines with full lineage

FAQ

Questions about Banking, Financial Services & Insurance

Documentation, validation support, explainability, monitoring and change control are built into how we develop models, so your model risk team gets what it needs without a separate project.

Yes. We build inside your cloud or on-premises environment, use private model endpoints and follow your data classification and residency rules.

Yes. Fintechs use us for product engineering and AI features at speed; banks and insurers use us for modernization, data and governed AI.

We design controls to support requirements such as model risk guidance, data protection laws including GDPR and DPDP, and sector rules in the regions our clients operate in, working with your compliance team on specifics.

Next step

Ready when you are.

Tell us what you are trying to build and we will come back with a point of view, not a pitch.