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Opening Opportunity Through Automating Statement Reconciliation

Source: Finance Derivative

by Nick Peplow, Financial Services Director at Liberata

For companies dealing with high volumes of suppliers, goods, and services on a regular basis, busy accounts payable departments have a never-ending treadmill of tasks to complete. This is particularly the case when key details of purchase orders and ledgers are spread across multiple sites and disparate systems. As individual orders build up into vast numbers of transactions every month, supplier statement reconciliations rarely make it to the top of a company’s to-do list, which can cause a cascading set of problems to the business ultimately hitting the bottom line. Teams freed up from mundane manual tasks can also begin to contribute to the organisation’s goals in a much more valuable manner. The most interesting part is that automation doesn’t have to be a complex process, and there are solutions to fit every scale and budget.

Reconsidering reconciliation

Reconciliation is a fundamental accounting process that ensures the actual money spent or earned accurately matches the money leaving or entering an account at the end of a fiscal period. When applied to suppliers, it means that the services or goods provided for are billed and paid for accurately and on time. Reconciliation is a critical part of the finance department’s operations as it is an opportunity to check for fraudulent activity and to prevent financial statement errors. Usually, reconciliation is performed at set intervals, monthly or quarterly, and is key to managing cash flow, and ensuring regulatory compliance. However, due to its somewhat mundane nature, it’s one of the first scheduled tasks to fall down the list of priorities during busy times.

Manual reconciliation is a process prone to error

Reconciliation of supplier statements is not a complicated process, but when undertaken manually it tends to be time-consuming and prone to error. This is particularly true for companies that have a high-volume of suppliers, goods and services, which often leaves finance teams in a never-ending reconciliation loop. Complications are amplified when details of purchase orders are split across sites or even on different systems and platforms. In the worst case, invoices are physically printed out from one system and typed into an adjacent system before they can be matched. As well as being slow, resource intensive and wasteful, tasks undertaken in this manner are generally unpleasant to undertake. For these reasons, manual reconciliations are often put off until complaints arise that make their swift completion critical. Teams that are compelled to rush through mountains of paperwork tend to accumulate errors which then require even more time to reconcile. As reconciliation is pushed back to a perpetual last-minute panic, suppliers are often late to receive payments which can impact on delivery schedules.

It can also be the case that during the reconciliation process issues arise that require input from external suppliers. Invoices can be missing, justifications for payments may need to be sought from within the business, in fact there are any number of problems that can arise from a late reconciliation report.  Discrepancies unearthed in this way impact and disrupt not only internal workflows, but also impact on supplier workflows, especially if undertaken in an ad hoc and unstructured manner. In the long term this can affect pricing strategies or preferential terms, potentially leading to an increase in coverall costs to the business.

Reconciliation is also a lengthy and tedious task. Often it may even be performed by temporary staff unfamiliar with the systems, services and suppliers who they are administering – yet another reason why mistakes creep in. Staff are only human, after all, and even 99% efficiency means that 1 in 100 reconciliations will have an error of some order of magnitude.

The solution is automation, and it’s not rocket science

While reconciliation is not a complex process, failure to undertake it in a systematic process often leads to complex and undesirable outcomes. The key to solving these issues is through automation.,. When automation is applied to the problem, not only are results achieved on time, more economically and with greater accuracy, automation provides an array of analytics that can deliver real insights into how suppliers are performing. These insights can be shared across the organisation and assist with cohesive planning initiatives and help to deliver better business outcomes.  As cloud technology evolves and more streamlined processes become available in the workplace, increasing numbers of businesses are taking advantage of automated reconciliation services, and discovering the benefits of adopting this approach.

Reimagining finance within the organisation

Part of the problem of automating tasks is envisioning what staff will do with the time saved from undertaking them. Automation implies that repetitive and mundane tasks are removed from the job stack, meaning that managers are forced to reinterpret the roles of their team members. Most finance departments are overworked and there are plenty of other tasks that require attention. However, for progressive managers and organisations, automating away the mundane can lead to a step change in how their department integrates with the wider organisation. With more time to focus on the bigger picture, finance heads can use the technology and data analysis they possess to provide better information and insights to other departments. As well as contributing to strategic and operational decisions, finance can play a greater role in ensuring that all departments are working together towards the organisation’s shared goals. For example, team members, free from mundane data entry, could be spending more time benchmarking suppliers and providing next-best alternative solutions, assisting the commercial teams to enforce best practice in purchasing and proactively helping to navigate the pitfalls of the supplier selection process.

Accounts payable team members possess valuable skills. Removing them from a time-consuming process, where errors are almost guaranteed, and allowing them to work on more complex tasks, such as improving supplier relationships, analysing delivery schedules, working cross functionally with other departments is ultimately a much more valuable undertaking to the business, and automation plays a key role in achieving this.

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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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