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The ‘risk’ of financial inclusion

Authors: Hugh MacGarvie and Ciaran Furniss

How digital banks can increase their social impact and profit margins

The rise of digital banks, especially within the retail space, is the modern-day gold rush, with an entrant growth of 150% in the last 5 years. Within the developed economies, the market is saturated with new entrants competing to grow their customer base through more enticing sign-up benefits or cash incentives. And the reason for this is simple: through digitising operations and reducing the necessity for physical assets, banks are able to lower their capital and operational expenditure while simultaneously scaling their customers – notable players here are Monzo, Revolut, and JP Morgan’s ‘Chase’ Digital Bank. 

One of the unique selling points (USPs) of these digital banks for new customers is their simplified and efficient onboarding process. Prospective customers are able to open a bank account in minutes by downloading an app to their smartphone and completing a few easy steps (i.e. ID document upload, liveliness face and voice tests) from the comfort of their home.

The benefits of this business case for both banks and customers are well-known, and ‘green-shoots’ are now appearing within developing economies. A good example of this is TymeBank in South Africa. Tyme have been able to grow their customer base exponentially, onboarding roughly 110,000 customers each month, by providing a more accessible platform for their customers to interface with.

TymeBank’s hybrid approach

What differentiates Tyme from other digital banking players is how they have been actively targeting large groups of individuals within the lower socio-economic demographics. Tyme’s customers are predominantly from the unbanked and underserved segments. South Africans earning less than $200 a month make up 48% of their active user base, with 65% of their overall customer-base constituting women within the low-income segment.

Tyme has tailored their offering to be relevant to these specific economic demographics by understanding the financial needs of their users and introducing unique products such as their “GoalSave” savings account and “MoreTyme” credit service. By following a customer-centric approach, they have been able to create inexpensive and practical solutions that assist their targeted clientele in their daily lives.

Tyme have been able to onboard large volumes of individuals from a previously financially excluded demographic through a creative and sophisticated hybrid onboarding process. This onboarding solution consists of building and implementing digital kiosks in the local communities within the grocery stores like Pick ‘n Pay and Boxer, and stationing ambassadors to promote their services and assist prospective clients.

The opportunity

The question that is often raised with initiatives that are targeted towards lower-income demographics is one of profitability. For the digital banking model this is derived through volume: the more customers, the more revenue generated through bank fees and other service charges. Importantly, the cost of bringing on new customers should not be greater than the projected revenue the customer’s fees and charges would accumulate.

Although Tyme’s onboarding solution is sophisticated and has been successful in scaling their target customer base, it still incurs large operational expenses due to the digital kiosks – roughly $281,000 a month. This is detrimental to a business’ profit margin, especially one that derives its revenue through onboarding large volumes of customers.

Currently the bank does have the ability to open an account and onboard via web channels (at 20% the cost of the digital kiosk), but this has a very low take-up rate with only 15% of its customers using that avenue. This could be attributed to a host of factors but primarily a lack of access to the digital infrastructure required.

However, there is an opportunity that would allow Tyme and digital banks alike to continue to scale their business with their targeted customer-base at far cheaper rates: a digital ‘plug-and-play’ risk scoring point solution.

In 2017, there was a change in Know Your Customer/Anti-Money Laundering (KYC/AML) regulations in South Africa. The purpose being more financial inclusivity for individuals who were previously excluded from the formal banking system by introducing a risk-based assessment (RBA) methodology. This created the opportunity for more sophisticated and creative risk scoring solutions to measure the perceived risk presented by an individual and capture the largely underserved market. This regulation change is coupled with a consumer base that has increased access to smart technology – 91% of South African adults own a mobile phone, with 51% owning a smartphone device. This number is only expected to grow in the coming years with large investments on the African continent from the likes of Huawei and the decreasing cost of smartphone ownership. 

Therefore, a purely digital onboarding point solution would complete the identification, verification and risk screening of the targeted demographic through a plug-in mobile application. It could utilise Machine Learning and Artificial Intelligence (ML/AI) data analytics to assess the perceived risk presented by prospective customers through the established Department of Home Affairs database and others like it. 

The ‘risk’ factor

Software like this has already been developed and implemented by digital banks within other geographies through companies such as Jumio, who supply the KYC/AML capabilities for the digital banks to continue to onboard large volumes at rapid rates. These risk solutions are much cheaper than the current physical kiosk costs and aren’t subject to external factors such as opening and closing times of the partner stores, or ambassador availability/location. In order to make this work, the risk algorithm would need to be tailored to the different customer personas and geographical regulation intricacies, but the inherent ‘foundations’ would remain the same. 

Ultimately, through this integration, customers could use their mobile devices to complete their account onboarding, digital banks could reduce their reliance on expensive infrastructure, and financial inclusion initiatives would be able to impact more individuals within these demographics. This would also lead to a movement away from money mobile providers and allow customers to have access to more sophisticated and bespoke financial products and services which could assist in generating more thorough financial records and even credit histories.

However, it’s not perhaps as simple as it seems, as any onboarding solution will always carry an element of risk whether it be physical, brick-and-mortar or completely digital. Through introducing this more digital approach, considerations would need to be made on the level of risk that could be invited while scaling larger volumes of previously unbanked individuals.

And that’s exactly what we’ll explore in part II, as we address the challenge of maintaining the ideal balance between an open and inclusive onboarding process and an accurate yet moderately risk-safe appetite.

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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.

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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.


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.

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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.

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