Case Study

Using Quantexa to understand your ESG exposure using network analytics

Sustainable Finance | Tech | Amsterdam & London

The Background

  • Environmental, Social, and Corporate Governance (ESG) has accelerated as a critical board-level topic

  • Consumers, businesses and regulators are now moving steadily towards a greener future

  • In early 2020, the SASB and TCFD converged on common ESG disclosure standards and recently, the EU approved regulation to improve
    taxonomy for sustainable activities

  • There are many providers offering ESG data feeds with a myriad of scores

  • However, there remains the question: How should Financial Institutions (FIs) incorporate these inputs into their risk processes, models and reporting? 
    For example, ESG risks are different from traditional risks, given they are:

    -Data intensive – containing granular spatial data, covering supply chains and physical locations

    -Multi-dimensional – discrete but correlated, interacting across products and risk siloes which can aggregate to create material concentration risks

    -Dynamic – constantly changing risks with an intersection of physical and entity risks

    -Prospective – future relationships are not present in historic data

    -Technology now exists for FIs to underpin decision-making with relevant granular ESG data  – this unlocks the ability to monitor, assess and report ESG risks to avoid negative outcomes, such as stranded assets or reputational damage, and decisively move towards more sustainable finance

Quantexa's Approach

  • The Quantexa platform enables banks to represent data dynamically as it exists in the real-world through events, entities and networks

  • Crucially, the software can overcome data quality issues by combining multiple data sources to correct data or complete missing data

  • The unique capability of the platform lies in joining internal data and enriching it with external data to create a single contextual customer or supply chain view

  • This advanced Entity Resolution goes far beyond basic matching approaches

  • Dynamic networks are created within the data using links such as ownership structures, transactions with counterparties, common KYC data, etc. 

  • These networks and single views are used as an input into risk analytical models, monitoring or reporting


Example: Applying ESG inputs

  •  Link internal customer data to external ESG data providers

  • Monitor network-based risks – connected shareholders, directors or entities

  • Connect corporate entity data to geographical locations and physical risk data

  • Use transaction data to create supply chains for scope 3 reporting

  • Connect risk drivers across siloed customer data to understand concentration risk

  • Score external data providers for data accuracy, relevance, recency and trust

The Impact

  • Quantexa’s platform can be deployed as a common data fabric to run multiple business outcomes

  • Banks have deployed the technology across their entire internal/external data estate, combining tens of billions of customer static and transaction data, allowing batch-based development and testing of models that can then be deployed in real-time operational environments to make more accurate automated or judgmental decisions

  • The platform provides granular security and its open technology approach allows it to co-exist with existing software investments whether on premise or in cloud-based data environments. This reduces the Total Cost of Ownership (TCO) via economies of scale on hardware, external data licenses and integration of internal data
  • Our open architecture-based technology ensures the platform can co-exist with current investments and can be deployed on-premise or cloud-natively

  • We offer proof of concepts lasting 8 - 12 weeks or agile approaches as part of a full implementation within six months


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