The discussion focused on Digiterre’s collaboration with BITA Risk to build the ESG functionality for the BITA Wealth platform.  This innovative management application allows ESG considerations to be incorporated into the construction, analysis, monitoring and reporting of private client portfolios.

Ian Murrin, Founder and CEO of Digiterre, emphasised the importance of data to the implementation of ESG at scale in wealth management.  He discussed the challenges of data management across data silos, volume, value, fidelity and timeliness.  Data is the new water, he said, as without it no organisation can survive.  According to Ian, digital-enabled transformation equates to data-enabled transformation, and this in turn relies on having the right tools, processes and culture in place for data management across the data maturity curve.  This approach was put to work on the BITA Risk project.

BITA Risk’s President and Global Head, Daryl Roxburgh, explained why data management is crucial to the effective incorporation of ESG into wealth management portfolios.  The first step is to select a data vendor which involves due diligence on the methodology used and the coverage of the data.  For example, managers may look at whether the data provider undertakes its own surveys, uses quantitative methods, or aggregates data from other providers to produce a consensus score.  Data sets often contain at least four tiers – a high-level score for a company, followed by three pillars for each of environmental, social and governance factors, and then various subfactors.  There could be upwards of 2,000 data points per company.

Investment managers need to decide what data they will use in the investment process and at what level of granularity a client can express ESG preferences.  A high degree of granularity can be provided for a large client, but replicating this in a mass-customised way at scale is challenging.  Managers may choose to bias a portfolio towards clients’ ESG preferences or remove conflicting assets from the portfolio.  The chosen investment management approach drives the data management strategy that will be adopted.  The framework created for BITA Wealth ESG Manager supports mass-customisation with a data strategy that linked clients’ ESG preferences, their mandates, investment strategy and the core data, and then mobilised that data to analyse, re-balance or monitor the client book.

This was a highly complex data mission.  It required the combining of data related to ESG factors, client portfolios, client preferences, risk management, investment strategy, and suitability assessment.  This in turn called for data ingestion, normalisation and management, for example capturing client preferences at multiple levels in multiple data zones without losing data integrity.  Digiterre and BITA Risk worked together to implement advanced software and data engineering capabilities across architecture, user-interface development, server-side development, and quality assurance.

The project resulted in a unique ESG management application for the private wealth sector.  BITA Wealth ESG Manager incorporates client preferences, portfolio modelling and screening and what-if scenarios, as well as portfolio and client reporting, and ESG and product preference monitoring and alerts.  It features interactive visualisations that enable the assimilation of high-density complex data and a questionnaire framework that enables clients to express a wide variety of ESG preferences.  It is supported by a data model that will act as a foundation to a firm’s ESG proposition and enable that to evolve with data and market developments.

Data management is a key enabler of the widespread adoption of ESG and sustainable investing.  By applying advanced data engineering approaches, wealth managers can effect a step change in ESG adoption.  This empowers them to accelerate their product and business development efforts in the ESG space and helps them unlock the vast potential of private wealth to power a more sustainable future.

You can watch the full webinar here.

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