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Three ways data can help financial organisations thrive in today’s economy

Source: Finance Derivative

By Rinesh Patel, Global Head of Financial Services, Snowflake

Financial organisations are caught in the middle of an ever-evolving landscape caused, in part, by emergent fintechs, shifting consumer expectations and increased regulatory change. Businesses are therefore turning to their data, re-imagining how they collect, process and analyse it, to drive growth and opportunity.

Despite this intention though, firms can often find themselves overwhelmed with the amount of data at their fingertips. Data tends to reside in individual departments that have no secure, efficient way of sharing it with other teams, creating silos of information. When teams need to collaborate, organisations are faced with additional costs and complexities in the movement of that data. The current infrastructure used by many financial institutions is not able to support the changing requirements of the industry, where data is the lifeblood.

Firms looking to harness their data should leave behind their outdated legacy architecture and implement an enterprise data strategy with a cloud-native platform. They can reposition themselves to accelerate time to market and value, with differentiated products and improved client offerings to gain a critical competitive advantage. Here are three ways that financial services are using better technology and enhanced data management to add business value.

Adhering to regulatory requirements

The volume of global regulations and reporting obligations has risen exponentially in the past decade, creating greater complexity and security challenges for firms capturing and processing data. Many of these regulations were taken by supervisors to ensure financial stability after the financial crisis of 2008. Regulators have greater expectations of firms with the aim of risk mitigation and transparency. With advanced technologies facilitating data capture, storage and analysis now available, supervisory bodies are also keen in part, to ask for additional disclosures because it’s now possible to demand more documentation and seek greater transparency.

The landscape of differing interpretations, overlapping regulatory requirements across asset classes and geographies and strict, even unrealistic deadlines for implementation have forced customers to take tactical quick-fix solutions, elevating operational risk and the chance of regulatory fines. Compliance departments have therefore been spending years building reporting processes, managing inconsistent data sets, maintaining ageing data stores and importantly overseeing differing levels of governance, adding more cost and complexity to the task at hand. For a large multi-segment global bank or asset manager this fragmented and manual approach to data management and analysis is not sustainable given the scale of processes and multi-geographic considerations that they have to comply with.

As regulators continue to push the long-term structural change agenda, financial services must now ready themselves to meet more robust reporting requirements to comply with the ever-changing regulatory landscape. The objective is to simplify and better manage data across teams with the governance and security provided by technological capabilities now offered through modern cloud capabilities to drive needed reporting. This will allow firms to replace old and inconsistent data with a centralised data architecture, providing a single source of truth. The time and cost reduction from data sourcing, ingestion, and the normalisation of data for analysis, can shrink to significantly streamline reporting processes.

Customer 360 experience

Consumers provide financial institutions with a vast amount of information, ranging from their banking habits to their behavioural preferences. Financial organisations have traditionally been slow to tap into the totality of this information to provide a better experience for customers.

The quest to provide greater visibility and a 360-degree view of customer behaviour is at the core of financial services organisations’ priorities. Customers want smooth, easy digital experiences that can speak to their desire for ease of use and convenience. This is seen in the ways virtual banking consumers have opted for technologies that are simple to interact with, self-directed and frictionless when it comes to carrying out digital transactions. New regulations, such as PSD2 and rules around open banking have also primed customers to expect more.

The challenge for legacy institutions is to bring the ease and usability of digital-first platforms with the sophistication of a major, global provider. Tapping into the full spectrum of data created by consumers is central to a successful transition.

Wealth advisory, investment management professionals are increasingly looking at data capabilities to support ongoing relationship management with their clients. Using data to understand customers in this way helps banks to successfully move customers up the wealth value chain. Wealth management organisations can digitise the investment process – from finding customers to managing accounts, and offering bespoke plans. Effective use of data in this sector can free up time for advisors, helping to retain key customers and charge higher commission levels thanks to a new level of personalised service.

Developing an effective ESG strategy

Environmental, social and corporate governance (ESG) considerations have grown in significance with increasing stakeholder pressures, driving a response by firms to prioritise their sustainability agenda. To understand, evaluate the problem and take action, firms need access to technology providing holistic ESG data capabilities and solutions, with performance and scale.

Financial firms are amassing large data sets from the public sector, including government reports, scientific bodies and private sector reports, to understand and address the climate challenge. Businesses are moving with urgency to acquire robust data sets, to meet ESG criteria and sustainability metrics needed to evaluate impact and make progress against their own commitments. There are several pervasive business use cases for teams experiencing ESG data challenges, including portfolio construction, financial planning and regulatory reporting that will require an effective ESG data management strategy.

Ever present challenges in the ingestion, standardisation, and sharing of ESG data will be at the forefront of every organisation – as they process the magnitude of the challenge and transform their operations to address the issue. With cloud-native solutions, firms can use ready-to-use query data across established marketplace data sets. They can then share that data across teams in a secure, governed way – with greater speed to market. Organisations can meet the need for scalable analytics, and access a data ecosystem to build their own proprietary ESG applications for different user and workflow requirements.

A business fit for the future

With data cloud solutions, businesses can effectively analyse the vast amounts of data available to them, equipping them to meet the ever-changing financial landscape. Leaving behind legacy systems will open up a multitude of opportunities and benefits that will drive business growth. This includes developing a 360 view of the customer, improved data governance and the opportunity to use data to support an effective ESG strategy. Without the ability to harness data through the cloud, companies will get left behind the competition and struggle to meet the standards that modern consumers expect.

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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.

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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.

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.

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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.

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