E06 · Expertise

Data & Risk Analytics

Structure financial, operational and risk information into transparent analytical views that support accountable business decisions.

Discuss a Data & Risk Analytics Requirement

Led by Akanchha Choudhary

01The problem

A dashboard is only as defensible as its reconciliation.

Management and risk reporting often combines data from several systems, each with its own definitions and timing. When figures cannot be reconciled to source or explained, decisions based on them are hard to defend—whether or not AI is involved.

02What ARVEC does

The design and engineering work

  • Analytical specifications: measures, definitions, sources and refresh logic
  • Validated measures with documented calculations and ownership
  • Reconciliation controls between source systems and reported figures
  • ETL and reporting automation with checks built in
  • Dashboards and decision-support models designed around the decisions they inform

A reported figure with its control

  1. 01Source systemsRecords with their own definitions and timing
  2. 02TransformationDocumented logic and mappings
  3. 03Reconciliation checkReport totals compared with source, within agreed tolerance Control point
  4. 04Reported measureDefinition and lineage visible to users
  5. 05Owner sign-offAccountable manager approves Control point
Illustrative reporting control. Tolerances and owners are set with the client.

03Typical outputs

Illustrative artefacts

  • Analytic specifications
  • Validated measures
  • Reconciliation controls
  • Dashboards and decision-support models

Examples of what an engagement can produce. Scope, deliverables and responsibilities are agreed in writing for each engagement; outcomes are not guaranteed.

04Applications

Where this applies

Typical applications by sector, not descriptions of completed engagements.

Banking & Financial Services
Risk and financial reporting where figures must reconcile to source and definitions must be consistent.
International Trade, Logistics & Supply Chain
Operational measures combining shipment, document and exception data.
Cross-Industry Enterprise AI
Management reporting automation with documented controls.

05Responsibilities

Boundaries and human review

Analytics support decisions made by accountable managers; measures and models are documented so they can be reviewed. ARVEC does not provide regulatory sign-off or audit opinions.

How this connects to our Approach

06Prior experience

Selected experience of our founders. Prior professional experience of ARVEC’s founders, before ARVEC AI’s incorporation. Not ARVEC client engagements; organisation and product names omitted.

Banking reporting and analytics

Banking · Prior data analytics roles

Contributed to reporting automation, ETL pipeline development, dashboard creation and improvement, and KPI analysis and monitoring. Responsibilities also included managing data-product requests to support business reporting needs.

Prior experience of Akanchha Choudhary

Data & Risk Analytics

Start with the decision this work needs to support.

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