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Alix Melchy, VP of AI, Jumio


The European Commission recently proposed a new regulation framework around the use of AI. The draft legislation seeks to regulate the use of AI proportionate to the level of risk each AI system presents in order to promote ethical and trustworthy use and development of AI in Europe. Moreover, the legislation will ban AI systems that present unacceptable risk and impose strict requirements on those which are considered to be high risk.

Although the UK has left the EU, there are some sectors that are still closely linked to the European banking sector — for example, lending. Therefore, we can expect financial services organisations, even those based in the UK, needing to keep abreast of these regulatory requirements, or else risk severe consequences.

Let’s look at how organisations in the financial services sector can better prepare themselves.


AI is no magic wand

AI is regularly positioned as an essential investment to stay ahead of the competition, provide greater customer service to customers, deliver more relevant services and offerings, as well as helping to transform many back-end processes. Its potential use cases have only increased further as we see more bank branches than ever having to close due to the impact of the coronavirus pandemic and consequently more consumers becoming dependent on digital banking services.

However, this raises the serious question as to what would happen if the algorithm used in financial decisions was tinged with bias. Such biases could negatively impact the way millions of consumers and businesses borrow, save and manage their money.

We need to be aware of the limitations of AI and learn to set reasonable expectations with it. To do this, we must take a step back and separate the actual technological capabilities of AI from magic and remind ourselves that AI is a tool, not a solution for everything and must be used responsibly. If businesses are going to start implementing AI, they must specify the exact problem they are trying to solve in order to select the best suited options and come back to this initial goal time and time again throughout the project to ensure it still aligns.


Eliminating bias

Another important factor to consider is the data that is going to underpin an AI model. AI systems are built on sets of algorithms that “learn” by reviewing large datasets to identify patterns, on which they are able to make decisions. In essence, they are only as good as the data they feed on.

Financial organisations must ask themselves whether they have enough of that data and, if so, whether it is representative of the population. Algorithms are data hungry and this data needs to be well stratified. It’s absolutely vital that the data represents society fairly so that it doesn’t reproduce historical biases. While it’s possible to buy datasets to speed up the process of building AI models, it is important to ensure that this data meets the required criteria rather than simply being a large data set. This allows employees in the financial services sector to treat customers fairly and, when combined with appropriate modelling and processes, allows them to maintain transparency and accountability in their decision-making processes to avoid legal claims or fines from regulators which can cause deep reputational damage.


Getting it right

Another process that businesses must implement in their AI practices is a pilot testing phase to ensure that the algorithm is working as expected and to better understand why an algorithm is making a certain decision. This allows companies to assess feasibility, duration, costs and adverse events to better understand why an algorithm is making a certain decision before it’s put into a real-world scenario.

It’s also important to note that the EU’s guidelines highlight that AI software and hardware systems need to be human-centric and a machine cannot be in full control. Therefore, there should always be human oversight and humans should always have the possibility to override a decision made by a system. Every algorithm has a set of limitations, and so, when designing an AI product or service, financial organisations should consider the type of technical measures needed to ensure human oversight. This is vital when it comes to understanding how AI is working, finding ways to train it and ensuring that it’s working as expected.


Establishing ethical AI

As mentioned earlier, a key driver for the EU Commission’s plans to take a risk-based approach to AI is to promote trustworthy AI systems that are lawful (complying with all applicable laws and regulations) and ethical (ensuring adherence to ethical principles and values) in order to avoid causing unintentional harm.

If financial organisations are to reap the benefits of AI, we must first minimise the potential harms of algorithms by thinking about how machine learning can be meaningfully applied. This means we need to have a discussion about AI ethics and the distrust that many people have toward machine learning.


There are some key areas to consider when it comes to ensuring AI is ethical:

  • Usage consent: Make sure that all the data you are using has been acquired with the proper consent.
  • Diversity and representativity: AI practitioners should consider how diverse their programming teams are and whether or not they undertake relevant anti-bias and discrimination training. This will draw upon perspectives of individuals from different genders, backgrounds and faiths, which will increase the likelihood that decisions made on purchasing and operating AI solutions are inclusive and not biased.
  • Transparency and trust building: Accurate and robust record keeping is important to assure that those impacted by it know how the model works.

In the financial services industry, there are many ways that AI can be leveraged. For example, in the document-centric identity proofing space whereby documents (such as a passport) are matched with a corresponding selfie to connect real-world and digital identities, proving AI is being used in an ethical way is becoming crucial. Gartner predicts that by 2022, more than 95% of RFPs for document-centric identity proofing will contain clear requirements regarding minimising demographic bias, an increase from fewer than 15% today. This shows that there’s a real opportunity to leverage AI solutions to provide the best service, but financial institutions must ensure that they are doing so in an ethical and accurate way by focusing on these key areas discussed. By following this guidance, businesses can ensure that their AI projects start off on the right foot and pave the way for regulatory compliance.



Five predictions set impact the finance teams in 2022




By Rob Israch, GM Europe at Tipalti


The CFO now has a very different set of responsibilities in comparison to a few years ago; 2021 saw sustainability move up the C-suite agenda, Brexit was officially pushed through meaning new rules and regulations for industries, and pandemic uncertainty caused further disruption for businesses. Understandably then, 97% of UK CFOs believe their role has become more complex over the last two years, according to latest research by Tipalti. Finance leaders, who were already rushed off their feet, are now having to wear even more hats.

Operating in a new climate, with new challenges and circumstances, finance teams must be ready to innovate to find new solutions to changing business needs. From becoming more attuned to ESG ratings to fighting against the burden of manual processes and tasks, below we explore what finance teams can expect to experience in 2022.


  1. A tightening of CEO-CFO relationship

As opposed to solely managing financial operations and ensuring compliance, the CFOs relationship with the CEO will intensify in 2022. This shift will see the CFO become increasingly involved in looking at the strategic ways the business can grow and diversify.

Nearly two-fifths (39%) of CFOs have noted a larger demand to collaborate with the c-suite now than two years ago. However, organisations are still slowed down by old ways of working, as nearly a third (29%) of CFOs state they are having to deal with more manual finance operations. As a result, CFOs aren’t afforded time to support the business leader in the way that their job requires.

Rob Israch

By innovating financial processes through automation, finance teams can free up time for the strategic tasks that matter most to the business. In fact, UK CEOs believe that the ability to prioritise innovation (25%) and the ability to improve financial and business reporting accuracy and timeliness are the most important qualities for a successful CFO today.


  1. Invoice payments fraud will be harder to fight

Every year, defending against fraud gets increasingly challenging. As accounts payable complexities rise, finance teams will experience payments fraud at an alarming rate.

Finance teams today are tasked with managing more diverse payment methods, increasing cross-border transactions and dynamic tax compliance and financial reporting. Yet, teams struggle to cope when operations are processed manually. The most common perpetrator of payment fraud is manual processes. They are neither efficient nor airtight enough to ensure optimum financial control. Busy finance teams, escalating complexities in AP and error prone manual processing sets the perfect scene for fraudsters to take advantage.

To mitigate such risk, companies need to leverage people, processes and technology. This means investing in robust technologies such as automation to standardise procedures. Data entry will be minimised, end-to-end payments processing visibility will be optimised and policy compliance becomes automated. Not only does AP automation relieve workflows by minimising manual intervention, but the technology acts as a hub for enforcing strong financial controls as the number of people and systems involved in payment processing is reduced substantially.

In addition, 2022 will see more multi-entity businesses emerge as organisations recognise the value of the ‘work from anywhere’ model. It can be challenging to manage finance functions across these multiple entities, and that is often why different business units in geographical locations run their finances in isolation, with varying processes and approvals being managed in different ways. However, with no central control or oversight, you run the risk of internal fraud.


  1. Finance leaders will need to focus on ESG initiatives

Following COP26, business leaders are under pressure to set and meet green targets, and many are turning to their CFOs for solutions. In fact, CFOs ranked incorporating environmental, social and governance (ESG) and sustainability into the business and its operations as the greatest driver of complexity in their role (27%), above even the global pandemic (22%).

A key reason for this is that ESG ratings have become an important tool for asset managers and investors to evaluate and compare future investment prospects. Currently more than a quarter (28%) of UK business leaders rank international growth as a top priority for the year ahead, so a less than favourable ESG rating is not an option. So far, the challenge for CFOs has been finding the time to work on sustainable initiatives.


  1. Uncertainty will continue to loom over the UK post-Brexit

It has been over five years since the UK voted for Brexit – but it will most certainly be on the agenda in 2022 as new regulations emerge. There are a number of challenges that Brexit brings, and much uncertainty still remains in place.

In navigating the uncharted waters of Brexit, businesses will encounter new hurdles when looking to fill roles, as the Global Talent Visa makes competition for skilled employees more formidable than ever before. With the visa application deadline passed, some employees may have chosen to move back home contributing to headcount issues for finance teams.

Moreover, the UK is still yet to agree many key trade agreements. Businesses will need to stay vigilant – watching out for any changes at relatively short notice and be ready to adapt.


  1. Employee wellbeing will need to be prioritised

Along with many other departments, the Great Resignation period has meant finance is experiencing Churn. Whilst the wellbeing of all employees will be a key focus for the c-suite this year, CFOs will need to ensure the work of the finance team is engaging and talent is not wasted on tedious and time-consuming operations. Introducing automation to take care of those manual tasks will free up time to upskill employees, while making them feel valued in their role.


The future office of finance

2022 will see finance teams adapting the way they operate to combat new challenges. With agreements signed following COP26, implementing sustainable initiatives is no longer a choice, and in the wake of Brexit uncertainty, businesses will have to face new rules and regulations head on. On top of this, the CFO will need to pivot away from solely financial operations in order to drive strategy, fight against fraud threats while prioritising the wellbeing of their team.

It’s a complex set of responsibilities and will only be achieved if finance teams are able to move away from manual administrative work and towards new technologies and automation capability. A CFOs time is precious and needs to be reserved for the tasks that matter.

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Three ways to reduce uncertainty in financial services marketing




By Patrick Costello, Senior Product Strategy Director, Optimizely 


According to Bain & Company, uncertainty is one of the key factors affecting marketing teams within financial services companies. For example, there’s a lot of uncertainty around when consumers will become less concerned about personal safety in favour of other product features. Similarly, it’s unclear whether marketers will be able to develop the agility needed to remain relevant in times of ever-changing consumer preferences.

Digital-first financial services are here to stay post-Covid, so financial services companies must manage change and reduce this current uncertainty by involving the whole business in improving the customer experience.

Our work with customers and partners has shown that there are three key approaches that enable marketers to move faster while reducing uncertainty and keeping pace with consumer expectations. These include the creation of content based on evidence, rolling out new features in a tightly controlled environment and continuous optimisation and personalisation of customer experiences.


Create content based on evidence

The research by Bain & Company noted above has found that successful marketing leaders tend to conduct tests more frequently, which helps the whole marketing team to learn how consumers behave and create content that drives engagement.

Leading digital companies like Amazon and Google have a well-established culture of experimentation, where they continuously test to determine how customers are behaving. In fact, a highly developed culture of testing adds certainty to the process of improving customer experiences.

Rapid testing and experimentation can reveal how customer habits are changing, and what features they highly value and interact with, in order to inform business decision-making.

By continuously experimenting with its quadrillions of landing pages, online travel company successfully transformed from a small Dutch start-up to the world’s largest online accommodation platform in less than two decades.


Roll out new features with control and safety

Traditional software development cycles normally rely on fragmented data from a variety of sources instead of a single unified view of the customer. This means that new releases do not necessarily resonate with customers and require painful ‘rollbacks’, for example to previous versions of a website.

By putting customer data at the centre of product development and carrying out early exploratory testing, companies can establish if a feature is likely to resonate with customers before actually building out the full feature code. They can deliver frequent and small releases and rollback easily if needed, thereby reducing uncertainty and risk.

One of our customers, a major US airline, used an early exploratory test to validate customer demand for instalment payment options on its website. Rather than implementing this feature straight away — and risk losing revenue — the test included creating a minimal user experience, in this specific case measuring clicks on an icon.

The data collected allowed the team to develop a business case for the new feature and enabled informed decision making around resources and future potential. Ultimately, the new instalment payment option resulted in a significant uplift for the business.


Personalise and optimise experiences

What constitutes as a great experience differs significantly from person to person and also depends on the wider context. Over the past 18 months customer behaviour and expectations have changed profoundly, adding new complexities to the customer experience equation. A strong personalisation and optimisation strategy helps financial services marketers engage with users at an individual level by connecting the right message to the right user at the right time.

Such an approach requires continuous improvement and iteration, and the adoption of ever more sophisticated personalisation capabilities.

Any personalisation strategy is underpinned by core segmentation which includes identifying psychographic characteristics of audiences, their affinities and intents. One-to-one personalisation can be achieved with the help of machine learning (ML) capabilities, where businesses can map out who their audiences are and their needs at any given point in time.

Continuous personalisation and optimisation not only reduces risk and increases certainty, but also has a demonstrable impact on revenue. In fact, three in four companies have increased their digital revenue by over 5% through always-on optimisation and personalisation, according to Digital Experience Economy Research.

By releasing features in a controlled manner, creating new content based on evidence and introducing continuous personalisation, organisations can move fast while ensuring their relevance to each and every individual in the long term.


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