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Risk Management

Finance

Finance Portfolio Analytics

Identifies exposures to factors like value, growth, momentum, helping isolate sources of alpha and optimize portfolio construction. Widely used by institutional quants.

Factor Analysis

Breaks bond portfolio returns into components driven by interest rate movements and curve changes, clarifying fixed income risks.

Yield Curve Decomposition

Quantifies portfolio tilts towards growth, value, small-cap, quality, helping investors monitor style drift and align to mandates.

Style Analysis

Tests how fast assets can be sold and at what cost under stressed conditions, ensuring readiness for redemptions or crises.

Liquidity Stress Testing

Attributes performance across equities, bonds, alternatives, and FX, giving a full picture of diversified portfolio results.

Multi-Asset Attribution

Detects if multiple portfolios hold the same securities, helping avoid unintended concentration risks across accounts.

Holdings Overlap

Compares your portfolio's returns, volatility, and ESG or factor tilts versus industry peers or benchmarks to find competitive advantages.

Peer Benchmarking

Rates portfolios on environmental, social, governance metrics to comply with regulations, investor demands, and mitigate long-term ESG risks.

ESG Analytics

Models portfolio outcomes under stress events such as recession, inflation shocks, or geopolitical crises, supporting resilience planning.

Scenario Analysis

Data science is an interdisciplinary field that uses scientific methods, processes, algorithms and systems to extract knowledge and insights from structured and unstructured data,[1][2] and apply knowledge and actionable insights from data across a broad range of application domains.

Data Science

Perform Data Aggregation and/or Manipulation, usually the first steps in Data Science

Data Wrangling

Data science is an interdisciplinary field that uses scientific methods, processes, algorithms and systems to extract knowledge and insights from structured and unstructured data, and apply knowledge and actionable insights from data across a broad range of application domains.

Dig deep into your data and gain insight. Uncover patterns and compute stats you can trust

Data Analysis

Build graphs, KPIs and interfaces which can be used to take business decisions

Data Visualization

Big data is a field that treats ways to analyze, systematically extract information from, or otherwise deal with data sets that are too large or complex to be dealt with by traditional data-processing application software.

Big Data

A data pipeline, is a set of data processing elements connected in series, where the output of one element is the input of the next one. The elements of a pipeline are often executed in parallel or in time-sliced fashion.

Data Pipelines

A mining operation today is highly dependent on thousands of connected IoT sensors, strong analysis and imagery tools. The exploration process is dependent on large cloud-based datasets and data-gathering tools. 

Mining

This subsector involves the extraction of metal ores such as iron, copper, gold, and silver from the earth for various industrial purposes.

Metal Ore Mining

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