ARE YOU READY TO HARNESS ESG DATA?

ver the last few years, Environmental, Social and Governance (ESG) data collection and reporting has become a much bigger priority for organisations with over 96% of S&P 500 companies publishing sustainability reports in 2021, according to research from the Governance and Accountability Institute.

There are many factors driving this need to examine and publish ESG data, but at the forefront are customer, employee, and governmental influence. Increasingly, consumers and employees are picking companies that they interact with and work for based on their track record with ESG policies and reporting. Additionally, government bodies and regulators are starting to weigh in with regulations.

While just at the start of the journey with regulations, many government bodies and regulators either have, or are considering, mandatory ESG data collection and ESG data reporting requirements for corporations under their jurisdictions. In December, 2022, the European Banking Authority (EBA) published its roadmap for sustainable finance. The roadmap – a collection of standards and rules aimed at better integrating ESG risk considerations into the banking sector – is set to come into effect in stages over the next three years.

In preparation for the regulations that come along with this roadmap, many European banks have already worked on their ESG data platforms to meet the new requirements around green financing. A key part of this is figuring out how to flexibly incorporate all of the new data sources, types and formats that will be ingested and analysed under the EBA’s new roadmap.

Understanding ESG data

ESG data comes in two categories –‘inside-out’ and ‘outside-in’. Data that comes from companies and is used for analysis is ‘inside-out’ and this data normally lags about 6-12 months behind because it is normally from annual ESG-related disclosures. On the other hand, ‘outside-in’ data is more regularly updated, sometimes in real time. It comes from a variety of sources that banks have access to from financial and company data of their customers and can have more just-in-time impact. Additionally, outside-in data can also encapsulate the collation of multiple diverse data sources, for example satellite images of fields with water level information meshed up with commercial transportation data.

While most financial datasets are numerical, ESG data normally includes both structured and unstructured datasets. It can include not only text and images, but if a company wants to analyse satellite data to understand their own climate dataset, they may even need to analyse videos. And this is only a few examples of the variety of data, so it is vital to work with a data model that can support many different types of data.

The velocity of ESG data collection and analyses also increases exponentially as organisations embrace the idea of integrating this data in real-time. For example, loan due diligence used to depend on quarterly ESG data, but as customers demand faster loan approvals, financial institutions are increasingly going to have to rely on real-time data.

With both the variety and velocity of ESG data increasing, so too will the volume of data requiring storage and analysis. Additionally, at the moment, there are no universally applicable ESG standards which means organisations are dealing with many different standards, with different data requirements depending on where they operate.

ESG data in real time

With ESG data, it is vital that it accurately reflects what is happening at the time of use which means the use of real-time data in analysis, reporting and scoring is becoming more common. This, however, requires harnessing technologies such as cloud computing, AI, and machine learning to instantly track breaking news stories for ESG-related data on investments or incorporating up-tothe-minute satellite data into reports on a firm's environmental impact for example.

Using real-time data platforms, asset and fund managers can calculate accurate ESG scores to aid in investment decisions or risk calculations. Commercial operators can also ensure that their diligence covers their supply chain and in-direct production facilities at third parties.

Leveraging ESG data

A key part of the EBA roadmap is the use of ESG data around loans with environmental sustainability features, or green lending. These loans, sometimes called energy efficient or green mortgages, are typically given to retail or SME clients to make energy efficient improvements to their properties, think solar panels or funding renewable energy work.

This will have an impact on scoring criteria for green loans and banks will now have to take into account these changes in relation to performance indicators, including the acceptance performance of loans and mortgages. This will also impact loans that have already been originated.

The loan origination process and data systems supporting the process will also be impacted. Banks need to think now about how they are going to tackle evolving or unforeseen changes, capture different data attributes for the same product or loan, incorporate new data types and formats, find insights from data explosion and meet the demands of customers and the competition.

While monitoring the upcoming roadmap and shifting world of regulations around ESG, banks and financial institutions should start to evaluate whether their infrastructure can handle the varied types and volume of data that will be required. Also, breaking down siloed data sets to enable easy search and analysis of data will be key to finding value in ESG data.

Continued inflation, rising interest rates and the ensuing cost-of-living crisis will inevitably lead to increasing numbers of customers falling into arrears. Lenders will face the dual pressures of increasing provisions on the balance sheet and the rising operational expense of supporting their customers through this period.

This tough macro-economic environment, unfortunately, coincides with a challenging period in terms of compliance and regulation. The introduction of Consumer Duty in July 2023 means that lenders of all types and sizes need to examine the way they operate and may need to adapt. This new and enhanced set of standards aims to increase the quality of outcomes for customers. Organisations must act now to protect and support all customers, particularly those who are vulnerable – identifying those at risk of falling into arrears and supporting those already in arrears.

Understanding customers’ attitudes to debt

Our recent research shows that a fifth of UK consumers (19%) aren’t confident they are able to pay all their bills and 30% fear they won’t be able to pay an unexpected bill. These figures show a real lack of confidence in the personal finances of a significant proportion of the UK’s population.

When asked what they would do if they found themselves unable to pay a bill, nearly 60% say they would seek support from friends or family members, but 15% would do nothing, which is extremely worrying.

The lack of reliable and robust data throughout the consumer lifecycle prevents many organisations from effectively communicating with customers or making informed decisions. They cannot form a 360-degree view of their customers, which hampers their ability to adopt a consumer-centric approach to identify and support ‘at risk’ people. Over a third of consumers (36%) say they never get a personalised service from their main bank.

Leveraging data to offer more personalised services

With the impact of the cost-of-living crisis reaching far and wide, there’s a real need to fully understand customers and their attitudes to debt. In particular, it’s critical to identify consumers who can’t repay the money they owe lenders to effectively manage and protect them.

Lenders must take the time to fully understand the different data sources available. Making good use of behavioural data, showing how a customer interacts with the organisation at different touchpoints, will give lenders insight and knowledge into how they are likely to behave in future and will allow them to offer more personalised services.

Such insight can help lenders provide valuable financial advice and ensure their services suit each customer. It’s their role to guide customers through difficult times – educating and signposting them to third parties if needed to ensure they receive the appropriate amount of support.

Data analytics is key to making informed decisions

Investing in data analytics is key to ensuring the information is properly used to build different customer personas and segments, allowing for insight-driven decision-making and creating personalised services. For example, predictive modelling of customer behaviour enables a forward-thinking and proactive view of the customer, helping to identify who is likely to default on a debt and when. Using such insight will enable lenders to create successful contact strategies.

Likewise, with a Systems Integrator, companies can use sophisticated technologies such as Big Data, AI and Internet of Things (IoT) to provide real-time insights into consumer behaviour and preferences. Financial services providers can use that insight to prevent bad debt, reduce operational costs, and ensure customers are cared for throughout their financial lifecycles.

Adapting communication strategies to customer preferences

Debt can be a highly sensitive and embarrassing topic for many, and debtors may not admit they are in difficulty and need support. The type of communication people prefer reflects those needs. Our findings show that 18% of consumers prefer the lender to get in touch by post if they are falling behind in their payments for a loan or mortgage, 15% by telephone, 16% via a text message, 12% by email, and 9% prefer to be contacted via a mobile app.

Banks and other lenders must adapt their communication strategies to reflect the spread of preferences across various channels. There is a real need for omni-channel services within the sector to make successful debt collections a reality. Customer journeys need to be designed with integrated touchpoints, offering customers the opportunity to pay on their terms via the channels they use and are comfortable with.

Closing thoughts

Many people choose to ignore their financial worries rather than reach out for available support. Lenders must be proactive in their approach. By revisiting their existing vulnerability and customer engagement methods, financial services providers can better handle customers’ increasing demand for support.

As the cost-of-living crisis continues and Consumer Duty increases scrutiny and pressure on the sector, creating a solid foundation of customer-centric data and analytics will help teams deliver compliant, personalised and supportive services through the crisis and beyond.