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    Financial services

    How do we make better decisions without taking on more risk?

    Banks, insurers and capital markets institutions need to decide quickly without taking on more risk. We support credit, risk, customer service and compliance with reliable data, explicit rules and an audit trail.

    Starts with: Data and AI

    Find where to start →

    A finance team assessing risk in a meeting

    What we offer

    • Credit and risk on reliable data

      Credit, risk and fraud prevention models backed by governed data and explicit rules.

      Also known as: Risk analytics · fraud prevention

    • Operations and service with AI agents

      Agents carry out onboarding, document review and service steps, with human approval for exceptions.

      Also known as: Agentic AI · process automation

    • Compliance and audit trail

      Versioned rules, recorded decisions and evidence organized for regulators and audits.

      Also known as: AI governance · Safetix

    • Core system modernization

      Integration and modernization of legacy systems through APIs, without interrupting operations.

      Also known as: Legacy modernization · LinkAPI

    Processes Semantix AI Suite runs in your sector

    Examples of application

    • Customer onboarding and verificationDocuments and watchlists checked, with the trail ready for the regulator.
    • Transaction disputesCustomer request analyzed, refund prepared and doubtful cases sent to the analyst.
    • Fraud alert analysisFalse positives discarded and the case file built for whoever decides.
    See the business areas →

    Cases

    Results from real projects

    Clients are not named, for confidentiality.

    • Financial servicesRisk and fraud

      Fraud prevention in payments

      Challenge
      Manual review of transactions and of fraud committed by users, merchants and corporate clients.
      What we did
      Unsupervised models for each product, business rules and automatic collection of company registration data.
      • −70%samples sent to manual review
      • +25%fraud detected

      Measured result

    • Financial servicesRisk and fraud

      Fraud score in auto insurance

      Challenge
      Claims fraud, misuse of roadside assistance and underwriting rules without data behind them.
      What we did
      Supervised and unsupervised models, explainable decisions and monitoring of the model life cycle.
      • −20%loss ratio

      Measured result

    • Financial servicesData governance

      Data-driven credit policies

      Challenge
      A new cloud data platform the credit team barely used, and heavy dependence on IT.
      What we did
      Data consumer profiles, access policies, lineage, a data catalog and automated data quality.
      • +60%of credit policies based on data
      • +82%of the team using the platform within 30 days
      • −35%duplicate data

      Measured result

    • Financial servicesData platform

      Moving models to MLOps in the cloud

      Challenge
      On-premise architecture, SQL-centred development and no CI/CD flow for models.
      What we did
      A Google Cloud platform with a feature store, training pipelines, a model registry, separate environments and infrastructure as code.
      • 22models running on the new platform
      • 12feature sets migrated

      Measured resultGoogle Cloud

    • Financial servicesProcess automation

      Sales cancellation back office

      Challenge
      Cancellation requests arriving by email, manual checks of attachments and a risk of errors and duplicates.
      What we did
      Automation that reads the emails, checks the attachments, records the cancellation and notifies the merchant, with an analytical report.
      • 60→2 minper request
      • 168→1,000emails handled per month
      • −82%audit findings

      Measured result

    • Financial servicesSales and customers

      Cross-selling payment solutions

      Challenge
      A base of more than 100,000 business clients using few products in the portfolio.
      What we did
      Recommendations based on similarity between clients, with a business-rule layer and a propensity score.
      • 50%of the base with a recommendation
      • 41%of recommendations are cross-sell

      Measured result

    See all cases

    Clients in this sector

    • Banco do Brasil
    • Itaú
    • B3
    • Caixa Seguradora
    • Banco Original
    • Alper

    Display of brands subject to usage authorization.

    Every sector has its own bottleneck

    The method does not change: we understand the problem, map the workflow, check what already exists and define a first step with real impact.

    Talk to Semantix

    Not sure what a term means? That’s fine

    Nobody should have to speak technology to understand a transformation.

    Enterprise AI Company
    A company that brings AI into the operations of large enterprises: it delivers the platform, builds with the client’s team or runs the processes with AI agents and specialists.
    Hybrid workforce
    AI agents handle the volume; specialists decide the exceptions and the risky cases.
    Operation that learns
    Every human decision becomes knowledge, so the next similar case is faster and cheaper.
    Outcome-based contract
    You pay for what is delivered and measured, such as tickets resolved or cost per transaction.
    Context
    Your company’s knowledge (data, processes, rules and decisions) that lets AI act safely.
    AI
    Computers able to perform tasks that would normally require human intelligence.
    Agent
    An AI system that can carry out tasks using tools.
    Workflow
    The path a piece of work travels inside a company.
    API
    A way of letting systems talk to each other.
    Cloud
    Computing and storage reached over the internet.
    Data Platform
    An environment that organizes and serves the company’s data.
    Agentic AI
    AI that can understand goals and carry out tasks within defined limits.
    AI Harness
    The structure that supplies the context, tools, rules and controls an agent needs to act safely.