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ANALYTICS · AUDIT · FINANCIAL RISK

Analytical depth.
Financial insight.

A flexible analytical platform for audit, banking and financial risk—with specialised tools, plugins and connected workflows.

Workbench connects Lens, SQL and Python with saved analyses, workspaces and shared resources for large datasets, locally or with remote sources and execution.

Explore WorkbenchDiscover the suites
THE VIEWSQL PLATFORM
Financial expertise.
An adaptable foundation.
01
ANALYTICAL FOUNDATION

Analyse

Portfolios, models & financial risk

SQL, visualisation & analytical methods
From analysis to professional workflows
02
MODULAR EXTENSIBILITY

Connect

Audit, IFRS 9 & Credit Risk

Specialised tools, plugins & integrations
DOMAIN-SPECIFIC ENVIRONMENTSProfessional suites
FOR AUDITORS, BANKS & FINANCIAL INSTITUTIONS
Audit & assuranceIFRS 9 & credit riskAnalytical flexibility

WORKBENCH / THE ANALYTICAL WORKSPACE

Data, tools
and connected work.

Workbench brings visual exploration, code and work organisation into one environment. Switch perspectives with Lens, save complete workflows as analyses and organise resources and access in workspaces.

  • 01
    Lens: multiple perspectives on your data

    Work with tables, SQL, Python, charts, pivots, measures and models within the context of your analysis.

  • 02
    Analyses that preserve your workflow

    Save tables, queries, scripts, visualisations and their relationships as an analysis you can reopen and continue.

  • 03
    Workspaces and projects

    Organise analyses, tables, files and resources by team or purpose, and collect related references in projects.

  • 04
    Shared resources and access management

    Manage workspace members and permissions and control access to shared resources.

  • 05
    Multi-format import and preparation

    Import spreadsheets, text files, columnar data, databases and banking formats; select rows and columns, filter, create derived fields and combine tables.

  • 06
    SQL and Python inside your analysis

    Query and transform with SQL; use Python for statistics, automation and models, then incorporate results into saved work.

  • 07
    Very large files and millions of records

    Use chunked processing, selective queries and disk-backed temporary storage to analyse large data volumes locally or through remote environments.

  • 08
    Graph, timeline and reuse

    Explore the analysis relationship graph and timeline; recover work, reuse results, export data and connect specialised utilities.

Open Workbench
viewSQL WorkbenchSaved analysis

ONE ANALYSIS, MULTIPLE TOOLS

Data analysis

Workspace · Analysis team

Lens
DataSQLPythonChartsPivotsModelsMeasuresStatistics

WORKFLOW

  1. 01Sources
  2. 02Preparation
  3. 03Analysis
  4. 04Results

ANALYSIS OBJECTS

TableSource data
SQL queryPreparation and transformation
ChartResults exploration
ModelAnalysis and estimation
RESOURCES AND ACCESS

Members · Permissions · Shared resources

Illustrative structure

Lens connects perspectives. Analyses preserve your work. Workspaces organise resources and access.

SQL + PYTHON / ONE WORKFLOW

From query to analysis.

Summarise and transform data with SQL. Continue in Python using the query result to calculate indicators, explore and model.

01 / PREPARE WITH SQLanalysis.sql
SELECT category,
       COUNT(*) AS records,
       SUM(value) AS total
FROM source_data
GROUP BY category;
02 / CONTINUE WITH PYTHONanalysis.py
summary = sql_result.copy()
summary["average"] = (
    summary["total"] / summary["records"]
)
summary.sort_values("total", ascending=False)

Illustrative example: the SQL query result is used as a DataFrame in Python.

MODELS LENS / CONNECTED STATISTICS

From the question to reproducible results.

Build models on your tables and measures, configure variables and assumptions, inspect diagnostics and compare results within the same analysis. Combine visual exploration with SQL and Python to turn results into reusable work.

Regression and classification

Linear models, regularization, logistic and Poisson regression, and nonlinear curves to explain relationships and estimate outcomes.

Time series and econometrics

Autoregressive models, ARIMA, smoothing, decomposition and multivariate time-series analysis to study dynamics and generate forecasts.

Components and clustering

PCA, k-means and hierarchical clustering to reduce dimensions, reveal structure and build segments.

Tests and experimentation

Parametric and nonparametric tests, ANOVA, experimental designs, comparisons and power analysis to study differences and plan measurements.

Questionnaires and scales

Frequencies, cross-tabulations, Likert scales, recoding and reliability to analyse responses and instruments.

Simulation, scenarios and optimization

Distributions, processes, bootstrap, Monte Carlo and optimization to explore uncertainty, assess scenarios and search for solutions.

  1. 1Prepare data
  2. 2Define the model
  3. 3Estimate and diagnose
  4. 4Compare runs
  5. 5Reuse results

Keep definitions, parameters and results linked to the analysis. Recover runs, turn predictions and transformations into new tables and share resources with controlled access.

A consistent way to work through Lens, APIs, the terminal and Python. Integrate statistical methods into programmable workflows and local or remote execution.

LARGE DATA VOLUMES

Very large files. Analysis at scale.

Process millions of records with chunked reading, selective queries and disk-backed temporary storage. Prepare, filter, join and aggregate data with SQL; combine local work with remote sources and execution.

GENERAL, BANKING AND AUDIT DATA

Many formats. One environment.

Spreadsheets and data
Excel · ODS · DBF · CSV/TSV · Parquet · JSON/NDJSON
Databases and text
DuckDB · SQL dumps · XML · fixed-width
Bank statements
OFX/QFX · QIF · MT940 · CAMT (ISO 20022)
Audit and financial reporting
SAF-T · XBRL · iXBRL

01 / THE PLATFORM

Analytical flexibility.
Financial depth.

Bring analytical methods, specialist financial tools and professional workflows together in a platform that can adapt to different data, teams and requirements.

01

Analytical capability

Explore portfolios and financial data with SQL, visualisation, models and programming tools. Connect questions, calculations and results.

02

Specialised tools & integrations

Extend audit and risk workflows with analytical utilities, plugins and external connections, combining focused methods with a common working environment.

03

Flexibility for your organisation

Work with different data sources and formats, assemble tools around your requirements and adapt the environment to specialised professional contexts.

SPECIALISED FINANCIAL ANALYTICS

IFRS 9
for banking.

Analyse expected credit losses across configured scenarios, using portfolio data and calibrated PD, LGD and CCF curves. Compare the effect of risk assumptions and scenario weights on ECL results.

  • 01
    Portfolio-based ECL calculations

    Bring instrument data and calibrated risk curves into a defined calculation workflow.

  • 02
    Scenario analysis & weighted outcomes

    Configure scenario weights and risk assumptions, compare scenario-level estimates and inspect their contribution to weighted ECL results.

  • 03
    Results with a record

    Inspect granular estimates, instrument summaries, quality diagnostics and the parameters used in each run.

  • 04
    Sensitivity and model assessment

    Connect scenario results with sensitivity analysis, reconstruction and backtesting workflows.

  • 05
    Portfolio reporting

    Examine results by instrument and portfolio, and bring them into analytical reports and management review.

Explore the analytical platform
IFRS 9 ECL ENGINEIllustrative structure
BANKING ANALYTICS

From inputs to ECL results

PortfolioInstrument & exposure inputs
Risk parametersPD · LGD · CCF curves
ScenariosConfigured assumptions & weights
AnalysisScenario-level & weighted ECL
Run recordQuality diagnostics & parameters

Trace scenario assumptions, weights and results alongside the inputs used in each calculation.

02 / SPECIALISED SUITES

A common platform.
Purposeful environments.

Audit Suite, Asset Management and IFRS 9 for Banking connect analytical work with specialised workflows, controlled evidence and clear review responsibilities.

FOR AUDIT PROFESSIONALS

Organised case files.
Traceable evidence.

Manage each audit case file from opening and engagement assignment through review and closure. Connect sources, procedures, results and conclusions with named responsibilities and change history.

  • 01
    Case file and engagement management

    Create case files, link them to engagements and organise scope, owners, status and supporting documentation.

  • 02
    Evidence and workpapers

    Bring together tables, files, queries and results; identify the source and version used in each test.

  • 03
    End-to-end traceability

    Connect source data, procedures, results, findings and conclusions to reconstruct the basis of the review.

  • 04
    Data testing and analysis

    Run reconciliations and SQL/Python analysis; incorporate utility outputs, ECL reconstruction, sensitivity and backtesting into the case file.

  • 05
    Findings, responses and follow-up

    Record observations, responses and corrective evidence; retain links to the test and follow-up history.

  • 06
    Independent review and closure

    Separate preparation and review, document acceptance or observations and consolidate conclusions and PDF reports for closure.

How the suites work together
CASE FILE TRACEABILITYIllustrative structure
AUDIT SUITE

From source to conclusion

Case file and engagementScope, status and responsibilities
Source and versionIdentified data and evidence
Test and resultLinked procedures and analysis
Finding and responseFollow-up and corrective evidence
Review and closureDecision, authorship and conclusions

Each conclusion retains its supporting evidence.
Review and acceptance are documented.

Illustrative structure connecting a case file to its sources, tests, findings and review decisions.

03 / CONNECTED WORK

Connect the work.
Preserve the boundaries.

Connect institution-led financial analysis with independent examination through a controlled evidence framework.

01THE INSTITUTION

Prepare & retain

Maintain operational information and analytical results in Credit Risk, under institutional responsibility.

Asset Management · Credit Risk
02CONTROLLED HANDOVER

Release a reference

Identify an authorised revision and the result made available to the engagement. Access remains permission-based.

Revision-specific evidence
03THE AUDITOR

Examine & review

Use the evidence in documented procedures. Record an independent review and the engagement’s conclusions.

Audit Suite

A shared reference does not transfer ownership. Receipt does not mean acceptance.

04 / FLEXIBILITY WITH CONTEXT

Flexible by design.
Accountable in practice.

Adapt tools and methods to the work while keeping its sources, responsibilities and review context clear.

Traceable context

Keep source revisions, analytical work and review records connected to their purpose.

Controlled access

Work within defined permissions. An evidence reference does not create access rights.

Distinct accountability

Organise responsibilities within each specialised environment, including independent preparation and review where required.

THE COMMON FOUNDATIONviewSQL Workbench

Data exploration, SQL, Python and analytical tools in the browser.

05 / PROFESSIONAL PERSPECTIVES

Financial expertise.
Connected perspectives.

A shared analytical environment for the teams producing financial information, the professionals auditing it and the authorities examining it.

01Analysts & specialist teams

Work with financial data, models and analytical methods. Combine specialised tools and integrations around your organisation’s questions, extending workflows as requirements evolve.

Explore analytical capabilities
02Auditors

Connect engagement procedures to the evidence examined, with a clear distinction between management information, audit analysis and independent review.

Explore Audit Suite
03Banks & financial institutions

Bring IFRS 9 ECL calculations and credit risk analysis into an institution-led environment. Retain responsibility for data, assumptions, methods and decisions.

Explore IFRS 9 for banking
04Regulators & supervisors

Examine the relationship between source data, analytical methods, identified results and review responsibilities to support structured supervisory dialogue.

Review the evidence framework

EXPLORE VIEWSQL

Financial complexity.
A clearer analytical view.

Connect financial analysis, specialised tools and professional evidence in a platform designed to adapt to your work.

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