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AI is fundamentally reshaping the financial sector by automating processes, enhancing data analysis, and improving decision-making, leading to greater efficiency, accuracy, and risk management across a wide range of financial activities.
An AI Sandbox enables a financial institution to safely test their AI models to ensure that the models are making decisions that are compliant, upholding both market and customer integrity.
Our AI Sandbox will support you in deepening your understanding of the risks and opportunities AI presents to your consumers, and ensure that your models that making decisions that are fair and compliant.
We bring deep expertise in financial services
Sorai's AI Sandbox consists of two parts
1. AI auditing system designed to evaluate datasets, models, and outputs for fairness, transparency, robustness, and regulatory compliance. It runs explainability, robustness, and fairness checks without exposing underlying data or models, and generates audit reports creating a verifiable evidence trail for both internal compliance and external review.
2. Central AI Supervision System that aggregates audit results, benchmarks, alerts, and risk indicators. It provides a company wide view of systemic risks in AI-driven credit risk and mortgage decisioning. Finally it supports proactive supervision by identifying outliers, emerging risks, and compliance gaps.
OUR SANDBOX INCLUDES ESSENTIAL REGULATORY FEATURES
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The simulator recreates real-world and edge-case scenarios to stress-test AI models in a controlled environment. This helps validate performance, resilience, and decision quality before models go live.
Bias Mitigation
Simulator
The bias mitigation feature continuously scans models and data to identify disparate impacts across customer groups.
It allows teams to test alternative data, features, and thresholds to reduce bias before deployment. This ensures outcomes are fair, compliant, and aligned with regulatory expectations from day one.
Explainable AI and Risk scoring
Explainable AI is integrated into an AI sandbox to make model decisions transparent during testing. Teams can see why outputs are produced, which
features drive decisions, and how models behave under different scenarios. This ensures models are auditable, trustworthy, and safe to deploy.
OUR PROVEN APPROACH
We follow a robust process to deploy our AI sandbox and test your prioritised use cases, reporting on any non-compliant decision making
1.
Discover & Diagnose: Establish a prioritised use case, a validated understanding of needs, constraints and success metrics before building.
2.
Define and Design [Blueprinting]: Create a shared, testable blueprint of what the prototype must achieve and how success will be measured. Ensure the sandbox is intuitive, actionable and trusted by non-technical supervisory users.
3.
Develop [Prototype, Build, Test & Iteration]: Deliver working increments quickly, gather feedback continuously, and de-risk technical and regulatory challenges.
4.
Validate [Testing & UAT]: Provide evidence that the AI model meets functional, ethical and regulatory expectations.
5.
Scale & Demonstration: To scale the sandbox to include additional use case and enabling deployment at scale.
3 easy routes to getting started
Taster day
Our one day intensive working session helps you identify and prioritise high-impact AI use cases — giving you clarity and forward momentum for your AI sandbox journey
1 DAY
Use Case Sprint
Plug in company data into our AI sandbox to explore one key use case - delivering insights and results as well as showcasing the potential of the product.
6-8 WEEKS
Build & Scale
The implementation journey to develop an AI sandbox for your business - so that all internal AI tools and models can be tested.
See below ⤵
12+ WEEKS
ENGAGING ON THE AI SANDBOX IMPLEMENTATION JOURNEY
1.
Discovery Session:
‣ Align on AI ambition, priority business outcomes, and regulatory constraints.
‣ Get an overview of your existing AI models, data sources, tooling, and operating maturity.
‣ Gain an initial understanding of your high-value sandbox use cases and key risks to address early.
2.
Define a clear programme of work:
‣ Create a distinct programme of work with scope, timelines, and sprint structure.
‣ Confirm team model, roles, ways of working, and necessary environments.
‣ Set success metrics, governance approach, and regulatory alignment.
3.
Working with you to deliver an AI Sandbox:
‣ Deliver sandbox use cases in sprint cycles with tangible outputs every sprint.
‣ Embed explainability, bias mitigation, and simulation into live model testing.
‣ Build organisational confidence and readiness to scale AI into production.
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