Connect with us

Business

A bank’s ESG record depends on how its technology is built

Source: Finance Derivative

By Tony Coleman, CTO, Temenos

ESG (environmental, social, and corporate governance) has become mission-critical for banks, from meeting regulatory obligations to aligning with customer values to win market share. 

Many banks have turned to technology to manage their ESG position. But technology is not a panacea. It also presents a risk that banks fall short of their ESG targets. 

Technology that greens

Let’s look at the environmental pillar. Run on-premises or in a private datacentre, technology can be a big consumer of carbon. But deployed with the right infrastructure partners, it can enable banks to reduce their carbon footprint. Cloud is the best example of this. Banks that outsource their computing infrastructure to the public cloud hyperscalers can benefit from their economies of scale and energy efficient build principles. 

The geographical spread and scale of these datacentres allows for carbon-aware computing, which involves shifting compute to times and places where the carbon intensity of the grid results in lower carbon emissions. One study of Microsoft’s cloud infrastructure concluded its datacentres emit 98% less carbon than traditional enterprise IT sites. These hyperscalers have a focussed mindset and the deep pockets to match. The new Graviton3 processors that AWS is now installing in its public datacentres, which claims to use 60% less energy than the standard X86 models that have been in wide circulation, is an example of the progress that only a hyperscaler can achieve.

The green benefits ‘of the cloud’ are enhanced by software purposefully built to run ‘in the cloud’. Software vendors that are committed to decarbonising their solutions in the build phase pass those wins down the supply chain to banks. For example, the latest version of the Temenos Banking Cloud was built with a 12% improvement in carbon efficiency. How the software operates can have an even more profound benefit for banks. For example, banking software that runs ‘scale-to-zero’ protocols will automatically shut down or scale down availability according to demand for its service. This is one factor that has contributed to a 32% carbon efficiency improvement in the run time of the latest Temenos Banking Cloud release.

Collecting this evidence is not simply an internal tracking exercise. Regulations are reaching a point where publishing data against ESG targets will be legally mandated. In Europe the ECB and the Bank of England have launched climate risk stress tests to assess how prepared banks are for dealing with the shocks from climate risk. Meanwhile, initiatives like the UN-convened Net-Zero Banking Alliance (representing over 40% of global banking assets), the Glasgow Financial Alliance for Net Zero and ​​the Principles for Responsible Banking add to the clamour for banks to evidence their progress. Tracking ‘Scope 3 emissions’, which includes all indirect emissions that are not owned or controlled by the bank, is the next phase. Recognising this, Temenos has developed a carbon emissions calculator, which gives our customers deeper insight into carbon emissions data associated with their consumption of Temenos Banking Cloud services.

The same concept can be extended to a bank’s customers, with carbon calculators and automated offsetting schemes that help people build towards their personal environmental goals. Doing so brings a bank’s green credentials into the public sphere, turning environmental initiatives into commercial opportunity.

(Box-out)

Flowe, a cloud-enabled digital bank built on green principles, launched in June 2020. It is the first bank in Italy to be certified as a B-Corp and has been able to maintain its overall carbon footprint close to zero, saving 90.81% – 96.06% in MTCO2e emissions compared to the on-premise alternative. Within six months of launch, 600,000 mainly young Italians had become customers, at one point onboarding 19 new customers per second. This rapid launch and growth was only possible with the agility and scalability of cloud. Read more about this story.

Technology that reaches

Cloud also enables financial inclusion, a key tenet of ESG ambitions. Today, anyone with a mobile phone and internet connection can access banking services. With elastic scalability and software automation, banks have an almost limitless capacity to serve more customers. And they might not be where you think; 4.5% of US households (approximately 5.9 million) were “unbanked” in 2021. In the past, banks would have seen them as unprofitable targets. But as cloud and the associated automations cut go-to-market and operational costs, the commercial case for inclusion becomes stronger. 

Embedded finance gives banks another avenue of reach. Via simple APIs, banks can provide their solutions to non-financial businesses. This ready-made audience might otherwise take years to reach through a bank’s own marketing and sale channels. The embedded finance market is set to be worth $183 billion globally in 2027. That can be seen as a proxy of greater financial inclusion. 

AI offers another opportunity to improve financial inclusion. Armed with AI, banks can deliver highly personalised products and experiences for customers. People can be directed to the most appropriate investments, including funds that promote sustainability and loans made with a better understanding of the applicant’s ability to pay it back. ZestAI (previously Zest Finance), a leading provider of AI-powered credit underwriting, claims that banks using its software see a 20%- 30% increase in credit approval rates and a 30-40% reduction in defaults. 

But mismanaged, AI can have a dark side. If the data used to train them has bias, systems will perpetuate these discriminations. This can lead to unequal access to financial services and unjust or irresponsible credit decisions. In a study conducted by UC Berkeley, Latin and African-American borrowers were found to pay 7.9 and 3.6 basis points more in interest for home-purchase and refinance mortgages respectively, representing $765 million in extra interest per year. What’s more, AI algorithms are often complex and difficult to understand, so it is hard for customers to challenge decisions and for regulators to enforce compliance.

ESG by design

So how do banks reconcile the ESG benefits of technology with the risks? The answer is in how the technology is built; or more specifically, in the principle of ESG by design.  

ESG by design is the concept of incorporating environmental, social, and governance factors into new technology and software features from the outset. The desired outcome is that the solution’s architecture, functions and UX enable ESG optimisation. But it is enabled with a commitment that all decisions taken through the design and build phase are judged through the lens of ESG criteria and targets. 

At Temenos, ESG by design is a core principle to how we build technology. Let’s unpick what that means in practice, with some examples.

  • Shift-left is how we systematically embed ESG into our banking software services. It means estimating the potential carbon footprint of a new project from the start, and then working back to mitigate it at every stage. The same goes for usability, compliance, and other factors that impact ESG. Detecting and addressing issues earlier in the development process is more effective than taking remedial actions after the event, which risks both compromising the efficacy of the solution and increasing the cost and time of the development lifecycle. 
  • If there’s a choice to be made, banks should make it. Though ESG goals align with most bank’s commercial aspirations (i.e less carbon equals less cost, more choice and better experiences equals more customers) it is not binary. Banks will have varying appetites of commitment to ESG. Take scale-to-zero, which I referred to earlier. Limiting service availability and adding latency impacts the customer experience and regulatory SLAs, such as payment processing speeds. 

The optimum balance is not a call for us, as the technology vendor, to make. Instead we give banks the parameters and configurabilities to make the choice themself. This higher degree of control encourages banks to (a) use carbon-aware computing solutions, and (b) engage with the technology with more purpose.

  • Use technology to improve technology. Humans are fallible. AI is only as good as the people that program it. Their biases become the system’s biases. But the black box nature of many AI systems means that these biases go unnoticed. At Temenos we embed an explainable component to our AI tools (XAI). It allows us and our banking clients to understand how AI decisions have been made, and in doing so surfaces flaws that can be fixed. We extend this capability to a bank’s customers, allowing them to interrogate and challenge decisions.
  • The complex supply chains in technology makes ESG a collaborative effort. The work we do at Temenos to support banks with their ESG goals would be undermined if our partners didn’t share our same commitment. That means working with hyperscalers and partners in our ecosystem, and opening ourself up to third party validation. We did just that, using an independent carbon calculation platform (GoCodeGreen) to assess our carbon efficiency. I shared the evidence earlier; a 32% carbon efficiency improvement in the run time of the latest Temenos Cloud release, and a 12% improvement in build time. These are the sort of independently verified data points that banks should be asking their technological providers to submit. 

Collaboration also means being honest about what others can do better, and enabling their innovations. The Temenos Exchange has almost 120 vendors that are continually extending and improving our core solutions. These include Bud, an AI capability that drives highly personalised experiences for lending and money management; and Greenomy, that makes it easier for banks to capture sustainability data and report on it.

Conclusion

ESG by design is an holistic approach to all tenets of ESG: energy efficiency, financial inclusion, transparency and accountable governance. By working with technology partners that elevate ESG to a core design principle, banks can recognise a wide range of commercial opportunities and ensure compliance with evolving regulations. That should make ESG a core selection criteria of software vendors. Banks will want to find the evidence that their technology partners are as serious about ESG as they are; and that they have the design and build practices that bring these to life.

Continue Reading
Click to comment

Leave a Reply

Your email address will not be published. Required fields are marked *

Business

Driving business success in today’s data-driven world through data governance

Source: Finance derivative

Andrew Abraham, Global Managing Director, Data Quality, Experian

It’s a well-known fact that we are living through a period of digital transformation, where new technology is revolutionising how we live, learn, and work. However, what this has also led to is a significant increase in data. This data holds immense value, yet many businesses across all sectors struggle to manage it effectively. They often face challenges such as fragmented data silos or lack the expertise and resources to leverage their datasets to the fullest.

As a result, data governance has become an essential topic for executives and industry leaders. In a data-driven world, its importance cannot be overstated. Combine that with governments and regulatory bodies rightly stepping up oversight of the digital world to protect citizens’ private and personal data. This has resulted in businesses also having to comply e with several statutes more accurately and frequently.

We recently conducted some research to gauge businesses’ attitudes toward data governance in today’s economy. The findings are not surprising: 83% of those surveyed acknowledged that data governance should no longer be an afterthought and could give them a strategic advantage. This is especially true for gaining a competitive edge, improving service delivery, and ensuring robust compliance and security measures.

However, the research also showed that businesses face inherent obstacles, including difficulties in integration and scalability and poor data quality, when it comes to managing data effectively and responsibly throughout its lifecycle.

So, what are the three fundamental steps to ensure effective data governance?

Regularly reviewing Data Governance approaches and policies

Understanding your whole data estate, having clarity about who owns the data, and implementing rules to govern its use means being able to assess whether you can operate efficiently and identify where to drive operational improvements. To do that effectively, you need the right data governance framework. Implementing a robust data governance framework will allow businesses to ensure their data is fit for purpose, improves accuracy, and mitigates the detrimental impact of data silos.

The research also found that data governance approaches are typically reviewed annually (46%), with another 47% reviewing it more frequently. Whilst the specific timeframe differs for each business, they should review policies more frequently than annually. Interestingly, 6% of companies surveyed in our research have it under continual review.

Assembling the right team

A strong team is crucial for effective cross-departmental data governance.  

The research identified that almost three-quarters of organisations, particularly in the healthcare industry, are managing data governance in-house. Nearly half of the businesses surveyed had already established dedicated data governance teams to oversee daily operations and mitigate potential security risks.

This strategic investment highlights the proactive approach to enhancing data practices to achieve a competitive edge and improve their financial performance. The emphasis on organisational focus highlights the pivotal role of dedicated teams in upholding data integrity and compliance standards.

Choose data governance investments wisely

With AI changing how businesses are run and being seen as a critical differentiator, nearly three-quarters of our research said data governance is the cornerstone to better AI. Why? Effective data governance is essential for optimising AI capabilities, improving data quality, automated access control, metadata management, data security, and integration.

In addition, almost every business surveyed said it will invest in its data governance approaches in the next two years. This includes investing in high-quality technologies and tools and improving data literacy and skills internally.  

Regarding automation, the research showed that under half currently use automated tools or technologies for data governance; 48% are exploring options, and 15% said they have no plans.

This shows us a clear appetite for data governance investment, particularly in automated tools and new technologies. These investments also reflect a proactive stance in adapting to technological changes and ensuring robust data management practices that support innovation and sustainable growth.

Looking ahead

Ultimately, the research showed that 86% of businesses recognised the growing importance of data governance over the next five years. This indicates that effective data governance will only increase its importance in navigating digital transformation and regulatory demands.

This means businesses must address challenges like integrating governance into operations, improving data quality, ensuring scalability, and keeping pace with evolving technology to mitigate risks such as compliance failures, security breaches, and data integrity issues.

Embracing automation will also streamline data governance processes, allowing organisations to enhance compliance, strengthen security measures, and boost operational efficiency. By investing strategically in these areas, businesses can gain a competitive advantage, thrive in a data-driven landscape, and effectively manage emerging risks.

Continue Reading

Auto

The Benefits of EV Salary Sacrifice: A Guide for Employers and Employees

As the UK government continues to push for greener initiatives, electric cars have become increasingly popular. The main attraction for both employers and employees is the EV salary sacrifice scheme.

By participating in an EV salary sacrifice scheme, both employers and employees can enjoy cost savings and contribute to environmental sustainability along the way! This article will delve into the specifics of how these schemes operate, the financial advantages they offer, and the broader positive impacts on sustainability.

We will provide a comprehensive overview of the mechanics behind EV salary sacrifice schemes and discuss the various ways in which they benefit both employees and employers, ultimately supporting the transition to a greener future in the UK.

What is an EV Salary Sacrifice Scheme?

An EV salary sacrifice scheme is a flexible financial arrangement that permits employees to lease an EV through their employer. The key feature of this scheme is that the leasing cost is deducted directly from the employee’s gross salary before tax and National Insurance contributions are applied. By reducing the taxable income, employees can benefit from substantial savings on both tax and National Insurance payments. This arrangement not only makes EVs more affordable for employees but also aligns with governmental incentives to reduce carbon emissions.

For employers, implementing an EV salary sacrifice scheme can lead to cost efficiencies as well. The reduction in National Insurance contributions on the employee’s reduced gross salary can offset some of the costs associated with administering the scheme. Additionally, such programmes can enhance the overall benefits package offered by the employer, making the company more attractive to prospective and current employees.

Benefits for Employees

1. Tax and National Insurance Savings

By opting for an EV salary sacrifice scheme, employees can benefit from reduced tax and National Insurance contributions. Since the lease payments are made from the gross salary, the taxable income decreases, resulting in substantial savings.

2. Access to Premium EVs

Leading salary sacrifice car schemes often provide access to high-end electric vehicles that might be otherwise unaffordable. Employees can enjoy the latest EV models with advanced features, contributing to a more enjoyable and environmentally friendly driving experience.

3. Lower Running Costs

Electric vehicles typically have lower running costs compared to traditional petrol or diesel cars. With savings on fuel, reduced maintenance costs, and exemptions from certain charges (such as London’s Congestion Charge), employees can enjoy significant long-term financial benefits.

4. Environmental Impact

Driving an electric vehicle reduces the carbon footprint and supports the UK’s goal of achieving net-zero emissions by 2050. Employees can take pride in contributing to a cleaner environment.

Benefits for Employers

1. Attract and Retain Talent

Offering an EV salary sacrifice scheme can enhance an employer’s benefits package, making it more attractive to potential recruits. It also helps in retaining current employees by providing them with valuable and cost-effective benefits.

2. Cost Neutrality

For employers, EV salary sacrifice schemes are often cost-neutral. The savings on National Insurance contributions can offset the administrative costs of running the scheme, making it an economically viable option.

3. Corporate Social Responsibility (CSR)

Implementing an EV salary sacrifice scheme demonstrates a commitment to sustainability and corporate social responsibility. This can improve the company’s public image and align with broader environmental goals.

4. Employee Well-being

Providing employees with a cost-effective means to drive electric vehicles can contribute to their overall well-being. With lower running costs and the convenience of driving a new EV, employees may experience reduced financial stress and increased job satisfaction.

How to Implement an EV Salary Sacrifice Scheme

1. Assess Feasibility

Evaluate whether an EV salary sacrifice scheme is feasible for your organisation. Consider the number of interested employees, potential cost savings, and administrative requirements.

2. Choose a Provider

Select a reputable provider that offers a range of electric vehicles and comprehensive support services. Ensure they can handle the administrative tasks and provide a seamless experience for both the employer and employees.

3. Communicate the Benefits

Educate your employees about the advantages of the scheme. Highlight the financial savings, environmental impact, and access to premium EV models. Provide clear guidance on how they can participate in the programme.

4. Monitor and Review

Regularly review the scheme’s performance to ensure it continues to meet the needs of your employees and the organisation. Gather feedback and make adjustments as necessary to enhance the programme’s effectiveness.

Conclusion

The EV salary sacrifice scheme offers a win-win situation for both employers and employees in the UK. With significant financial savings, access to premium vehicles, and a positive environmental impact, it’s an attractive option for forward-thinking organisations. By implementing such a scheme, employers can demonstrate their commitment to sustainability and employee well-being, while employees can enjoy the benefits of driving an electric vehicle at a reduced cost.

Adopting an EV salary sacrifice scheme is a step towards a greener, more sustainable future for everyone.

Continue Reading

Business

Machine Learning Interpretability for Enhanced Cyber-Threat Attribution

Source: Finance Derivative

By: Dr. Farshad Badie,  Dean of the Faculty of Computer Science and Informatics, Berlin School of Business and Innovation

This editorial explores the crucial role of machine learning (ML) in cyber-threat attribution (CTA) and emphasises the importance of interpretable models for effective attribution.

The Challenge of Cyber-Threat Attribution

Identifying the source of cyberattacks is a complex task due to the tactics employed by threat actors, including:

  • Routing attacks through proxies: Attackers hide their identities by using intermediary servers.
  • Planting false flags: Misleading information is used to divert investigators towards the wrong culprit.
  • Adapting tactics: Threat actors constantly modify their methods to evade detection.

These challenges necessitate accurate and actionable attribution for:

  • Enhanced cybersecurity defences: Understanding attacker strategies enables proactive defence mechanisms.
  • Effective incident response: Swift attribution facilitates containment, damage minimisation, and speedy recovery.
  • Establishing accountability: Identifying attackers deters malicious activities and upholds international norms.

Machine Learning to the Rescue

Traditional machine learning models have laid the foundation, but the evolving cyber threat landscape demands more sophisticated approaches. Deep learning and artificial neural networks hold promise for uncovering hidden patterns and anomalies. However, a key consideration is interpretability.

The Power of Interpretability

Effective attribution requires models that not only deliver precise results but also make them understandable to cybersecurity experts. Interpretability ensures:

  • Transparency: Attribution decisions are not shrouded in complexity but are clear and actionable.
  • Actionable intelligence: Experts can not only detect threats but also understand the “why” behind them.
  • Improved defences: Insights gained from interpretable models inform future defence strategies.

Finding the Right Balance

The ideal model balances accuracy and interpretability. A highly accurate but opaque model hinders understanding, while a readily interpretable but less accurate model provides limited value. Selecting the appropriate model depends on the specific needs of each attribution case.

Interpretability Techniques

Several techniques enhance the interpretability of ML models for cyber-threat attribution:

  • Feature Importance Analysis: Identifies the input data aspects most influential in the model’s decisions, allowing experts to prioritise investigations.
  • Local Interpretability: Explains the model’s predictions for individual instances, revealing why a specific attribution was made.
  • Rule-based Models: Provide clear guidelines for determining the source of cyber threats, promoting transparency and easy understanding.

Challenges and the Path Forward

The lack of transparency in complex ML models hinders their practical application. Explainable AI, a field dedicated to making models more transparent, holds the key to fostering trust and collaboration between human and machine learning. Researchers are continuously refining interpretability techniques, with the ultimate goal being a balance between model power and decision-making transparency.

Continue Reading

Copyright © 2021 Futures Parity.