AI tool to measure Singapore real estate sustainability performance

AI tool to measure Singapore real estate sustainability performance

Banks to use NovA! in loan assessment.

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The Monetary Authority of Singapore (MAS) has announced the first use case of its artificial intelligence (AI) based NovA! tool.

The use case will help financial institutions (FIs) assess the sustainability performance of Singapore’s real estate sector. 

NovA!, part of the National Artificial Intelligence (AI) Programme in Finance, is aimed at helping FIs harness AI to generate insights on financial risk.

In the initial phase, the programme will focus on enhancing FIs’ ability to assess companies’ environmental impact and identify emerging environmental risks.

According to MAS, the real estate sector has been identified as one of the priority industries with a large share of economic activity in Asean region and it accounts for a significant amount of greenhouse gas emissions.

According to the 2020 Global Status Report for Buildings and Construction, building construction and operations account for the largest share of global energy consumption (35 percent) and energy-related CO2 emissions (38 percent).  

MAS said FIs would be able to tap on NovA! for their environmental, social, and governance (ESG) risk assessment for originating, underwriting and servicing of sustainability-linked loans.

Sustainability-linked loans feature key performance indicators (KPIs) that are tied to the issuance of proceeds, interest rates and other important terms.

This requires lenders and investors to create new capabilities to help catalyse the secular shift towards green financing and better quantification of financed emissions.

Sustainability metrics

At loan origination, FIs would be able to use NovA! to identify real estate corporate borrowers whose sustainability metrics can be improved, by comparing their historical environmental performance to that of their peers. 

During underwriting, NovA! will assist FIs to set appropriate sustainability performance targets (SPTs) for the borrowers, by comparing borrowers’ SPTs with the SPT Industry Benchmark generated by NovA!.

For servicing of loans, NovA! will compare the actual sustainability performance indicators (through direct use of meters or sensors) against the borrower’s self-declaration to detect greenwashing.

The NovA! ESG use case will also reduce the amount of time it takes for FIs to collect, process, and analyse data through the use of natural language processing (NLP) techniques to automatically extract relevant information from documents.

This will reduce the overall cost of operations for FIs, MAS said.

NovA!’s ESG use case will be integrated with MAS’ Project Greenprint which is a collection of digital data utilities and initiatives aimed at harnessing technology and data to aggregate “high quality, consistent and granular sustainability data”, to enable a transparent, trusted and efficient ESG ecosystem, MAS said.


Datasets collected via Greenprint will be used to supplement NovA!’s insights-generation capabilities, which will be accessible to Greenprint partners via APIs.

Since the announcement of NovA! in November 2021, 16 members have joined the NovA! consortium. The members include Singapore’s major banks, DBS Bank, OCBC Bank, UOB, Citi Singapore, Standard Chartered Bank and HSBC, among others.

Notable non-banking consortium members include Temasek and the Asian Institute of Digital Finance of the National University of Singapore (NUS).

A core team within the NovA! consortium is leading the development of NovA!’s ESG use case.

A demo version of NovA!’s ESG use case would be released during the Singapore FinTech
festival in November 2022.

MAS’s Chief FinTech Officer, Sopnendu Mohanty, said NovA! will provide a “valuable tool to help FIs generate prompt and actionable insights using structured and unstructured datasets and advanced computing”.

This, he added, will better enable financial institutions to direct financing towards sustainable projects and companies that meet sustainability performance targets.

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