Risk, reporting, and controls that survive an audit.

Financial institutions work under model risk rules, reporting deadlines, and fraud that changes weekly. We build the models, pipelines, and controls to meet all three, with the documentation examiners expect.

Talk to usSee the problems we solve
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Problems we solve in financial services

The questions financial services teams bring us, what is usually behind them, and what we do about it.

  • “Would our risk models survive an examiner’s questions?”

    The problem

    Credit, market, and operational risk models are built by different teams to different documentation standards.

    How we solve it

    Risk frameworks, stress testing and scenario analysis, and model governance documented to stand up to review.

  • “Why does regulatory reporting take so many people?”

    The problem

    Reports are assembled from spreadsheets and manual reconciliations every cycle.

    How we solve it

    Automated reporting pipelines with lineage from each reported figure back to its source.

  • “How much fraud are we missing?”

    The problem

    Rules-based monitoring catches yesterday’s patterns and buries analysts in false positives.

    How we solve it

    Real-time transaction monitoring and fraud models, AML analytics, and identity verification tuned to your own cases.

  • “Which customers are about to leave?”

    The problem

    Churn is noticed at account closure, too late to do anything about it.

    How we solve it

    Customer lifetime value and churn prediction, with journey analytics across digital channels.

Each of those answers depends on data someone can trust. Here is how we make it trustworthy in financial services.

Governed from the first pipeline

Data engineering, analytics, and AI built in order, on governance designed for this sector’s rules rather than retrofitted to them.

Data governance in financial services

In finance, an undocumented model is a finding waiting to happen.

  • Model inventory, validation, and monitoring in line with model risk management guidance.
  • Payment and customer data tokenized and access-controlled.
  • Every reported figure traceable to its source for auditors and examiners.
  • SR 11-7
  • Basel III/IV
  • PCI DSS
  • SOX
  • GDPR

Related problems we solve

The method is the same everywhere. The gates are what keep it honest.

Phase by phase, starting small

Each gate is agreed before its phase begins, so nothing scales on optimism.

  1. Assess

    Inventory the data and systems, and pick the one problem worth solving first.

    Gate: First problem and success measure agreed

  2. Pilot

    Solve it end to end at limited scope, measured against the threshold set in Assess.

    Gate: Threshold met, or pilot stopped

  3. Deploy

    Roll out with training and handover, inside your security perimeter.

    Gate: Your team running it

  4. Extend

    Add the next problem on the same data foundation and governance.

    Gate: Ongoing

A good first pilot

Automating one regulatory report end to end, or a fraud model tested against last year’s confirmed cases.

Everything we do in financial services

Risk & compliance

  • Credit, market, and operational risk frameworks
  • Stress testing and scenario analysis
  • Regulatory capital analysis
  • Basel III/IV, GDPR, and MiFID II compliance

Fraud & security

  • Real-time transaction monitoring and fraud models
  • Identity verification and AML analytics
  • Payment security, tokenization, and incident response
  • Third-party risk management

Trading & investment

  • Algorithmic trading analytics
  • Portfolio construction and market microstructure analysis
  • Alternative data integration

Customers & digital banking

  • Customer lifetime value and churn prediction
  • Personalized product recommendations
  • Customer journey analytics

Tell us which model or report is hardest to defend.

We will review it with you and say what it would take to make it audit-ready.

Talk to us

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