Data Science
We use statistical and machine learning techniques to uncover patterns in your data and turn them into concrete actions your teams can understand and use.
Mining data for patterns
Find customer, portfolio and operational patterns that are invisible in standard reports and spreadsheets.
- Segmentation and clustering
- Behavioural analysis of accounts and clients
- Identifying drivers of profit and loss
Statistical modelling
Build robust models to explain relationships in your data and answer "why" questions, not just "what happened".
- Regression and classification models
- Survival / duration and hazard models
- Hypothesis testing and inference
Predictive models
Forecast outcomes such as default, churn, conversion or claims using machine learning frameworks in Python and R.
- Predictive risk and behaviour models
- Uplift and response modelling
- Model performance tracking and recalibration
Monitor business processes
Set up monitoring of key business processes so you see abnormal trends before they become serious problems.
- Process KPIs and early warning indicators
- Alerts for unusual patterns or drift
- Regular review of stability and performance
Clean outliers and anomalies
Identify and treat outliers or corrupted records so decisions are based on reliable, well-understood data.
- Outlier detection and treatment strategies
- Missing data handling
- Robust documentation of data cleaning rules
Data Engineering
We design the data pipelines and structures you need so analysis, modelling and reporting can run reliably and at scale.
Scalable data architecture
Design data schemas and storage that grow with your business and support advanced analytics.
- Logical and physical data models
- Data warehouse and data mart design
- Schema design for analytics and reporting
Streamlined data acquisition
Automate data collection from internal systems, files and external sources with clear, repeatable processes.
- File and API based ingestion
- Scheduling and orchestration
- Source system mapping and documentation
Data integration & quality
Bring multiple data sources into one consistent view and repair corrupted or inconsistent data.
- ETL / ELT pipelines
- Master and reference data alignment
- Data quality rules and remediation
Cloud-ready solutions
Design and implement solutions that are ready for modern cloud environments and infrastructure.
- Cloud storage and compute patterns
- Environment and access considerations
- Deployment patterns for analytics workloads
Quantitative Risk Management
We build quantitative models and frameworks for credit, market, operational and enterprise risk – from design and implementation to validation and reporting.
3.1 Credit Risk
We help you build and maintain IFRS 9 and risk management models that are explainable, documented and fit for purpose.
Models & frameworks
- Credit risk models for different product types and portfolio sizes
- Probability of Default (PD) models:
- Point-in-Time (PIT) PD
- Through-the-Cycle (TTC) PD
- 12-month PD and lifetime PD
- Scorecard development:
- Application scorecards
- Behavioural scorecards
- Collections scoring
- Stressed and unstressed PD and stress testing frameworks
- LGD and EAD models, including downturn LGD and EAD
- Implementation in Python, R and Excel with clear documentation and handover
Analytics & insight
- Credit risk analysis across products, segments and vintages
- Vintage analysis and loss curves
- Loss forecasting and backtesting
- ROU rate and related analyses where required
How credit scores drive decisions across the lifecycle
This example shows how application scores and behavioural scores can work together: from accepting or rejecting a new customer, through collections, to marketing and possible write-off or sale of debt.
3.2 Market risk
Quantitative models and measures to understand and manage market risk exposures.
- Market risk models:
- Value-at-Risk (VaR)
- Conditional VaR / Expected Shortfall (CVaR)
- Foreign exchange risk measurement
- Interest rate risk measurement
3.3 Operational risk
Quantitative and semi-quantitative approaches to operational risk where data and judgement need to be combined.
- Scenario-based and loss-data based analysis
- Support for key risk indicators (KRIs)
- Linking incidents to financial impact and controls
3.4 Portfolio & enterprise risk
Help you understand portfolio risk/return trade-offs and connect risk metrics to decisions at enterprise level.
- Portfolio risk management and optimisation
- Enterprise risk management (ERM) frameworks
- Risk appetite metrics and reporting support
Data Analysis & Reporting
We turn raw data into narratives and visuals that management, boards and regulators can understand and act on.
Descriptive & diagnostic analysis
Clear summaries of what is happening, and why, in your data.
- Descriptive statistics and summaries
- Root cause and driver analysis
- Trend and variance explanations
Business intelligence & dashboarding
BI layers and dashboards that non-technical stakeholders can use without a data science background.
- Dashboard design and build (e.g. Power BI)
- Metric definitions and documentation
- User-friendly layouts and interactions
Reporting packs
Monthly and quarterly packs for management, boards and regulators, with the right level of detail and explanation.
- Standardised reporting templates
- Automated production where feasible
- Commentary and data story support
Accounting & Regulatory Services
We support technical accounting and regulatory projects where strong quantitative modelling, data and validation are required.
IFRS 9 support
IFRS 9 frameworks that are technically sound and auditable.
- ECL frameworks (PD, LGD, EAD)
- Model implementation and validation
- Documentation and audit support
Staging & ECL Logic
Lifetime ECL
Lifetime ECL
12-month ECL
IFRS 17 support
Support for actuarial and finance teams on the data and modelling side of IFRS 17.
- Data preparation and checks
- Model and control support
- Bridging outputs into reporting packs
Financial Modelling
We build financial models that explain value, risk and performance in a way stakeholders can understand and use.
Actuarial & equity evaluations
Support for product, portfolio and business evaluations using sound quantitative methods.
- Actuarial-style evaluations
- Equity and business valuation models
- Sensitivity and scenario testing
Budgeting & forecasting
Scenario-based budgeting and forecasting tools that can be updated without rebuilding from scratch.
- Income statement and balance sheet forecasting
- Scenario and stress testing
- Linkage to operational drivers
Cashflow & financial analysis
Analyse timing and size of cashflows and link them to financial performance.
- Cashflow timing and liquidity analysis
- Balance sheet and income statement analysis
- Investment and project evaluation
Add-ons & ongoing support
Once your tools are in place, we can help keep them updated and aligned with your business reality.
Monthly analytics update pack
We refresh your dashboards and models with new data and provide a short commentary on what changed.
Quarterly strategy review
Review key metrics, risk indicators and model outputs to support strategic decisions.
Dashboard clean-up & training
Fix or redesign legacy dashboards and train your team to use the models and tools correctly.
Not sure where to start?
Many clients begin with a focused project (for example, an IFRS 9 model or a risk
dashboard) and then add a light support package. Tell us what decision you're trying to
make, and we'll suggest a starting point.