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BANKING ON BOTS – MITIGATING ALGORITHMIC BIAS IN THE FINANCIAL SERVICES

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by Clare Joy, Strategy & Expansion Lead at Onfido

 

When developing new technologies, we must ensure that they operate fairly. At a time when identity is increasingly being used as the key to digital access, any technology based on identity must function fairly and equally for everyone, regardless of race, age, gender, or other characteristics leading to human physical diversity. While digital services have proliferated across many industries, this issue is particularly relevant in the financial sector, as Covid-19 accelerates a shift towards automated platforms delivered remotely by banks and other providers – with biases in AI having stark implications for unfairly rewarding certain groups over others.

 

How does AI bias creep into machine learning models?

Algorithmic decision making relies on machine learning techniques that recognise patterns from historical data. While often successful, it can pose a significant threat when these patterns are based on biases found in the data – these can emerge in two scenarios. First, a standard machine learning model can incorporate the biases found in the data during training. This can lead to subsequent predictions being made based on these biases.

Clare Joy

The other is that although the data is not necessarily biased, there could simply be less data available from a minority group for training. When there is less data to work with, especially with modern machine learning techniques, this is more likely to lead to modelling inaccuracies.

When this happens, it can have a real world impact. For instance, a criminal justice system in Florida has been found guilty of mislabelling African-American defendants as ‘high risk’ at a much higher rate than white defendants. We also saw Amazon discontinue use of an AI-powered recruitment platform which was shown to prioritise male applicants based on the language they used in their CVs.

In the financial industry, many processes that underpin much of society – from credit assignments to mortgage approvals – are simply not as fair as they should be because decisions are based on historical biases. Every individual or group should have the same set of opportunities, regardless of gender, age and ethnicity. For those of us that work with machine learning models, it is imperative that we try to minimise cases of unjust bias and understand how bias arises in our models.

 

Measuring and mitigating bias

Several tech giants are already attempting to do this by releasing supporting software for various parts of the machine learning lifecycle to mitigate biases. For instance, Google released multiple fairness diagnosis tools and a library enabling the training of fair models. Microsoft and IBM have also released tools for assessing and improving algorithmic fairness. However, it is incumbent on all businesses to optimise their own AI processes to eliminate bias.

This is something that we at Onfido focused on in the Information Commissioner’s Office (ICO) Privacy Sandbox, where we systematically measured and mitigated algorithmic bias in our artificial intelligence technology, with a particular focus on racial and other data related bias effects in biometric facial recognition technology. This closed the difference in performance between ethnicity groups for our facial recognition algorithm, which included achieving a 60x false acceptance improvement for users in the “Africa” category.

Part of the solution means companies using AI must review their machine learning models to ensure that they are not using biased data. Regularisation during training is one way of adding fairness, although this assumes that the model and relevant data are both available for a particular vendor or practitioner. By mathematically denoting a notion of fairness, it is possible to optimise for the chosen fairness constraints by adding them to the objective function.

Alternatively, pre-processing the training data sets means that features and sensitive information are decorrelated before training, while having a minimum impact on the data or decision rules. This is particularly applicable when there is no access to both the data and the training pipeline. Another strategy to obtain fairness is post-processing which is done by adjusting the classifier after training, when the pipeline is either unavailable or re-training is costly. By recalibrating the classifier after training, the threshold is set so that it maximises a certain fairness criterion.

 

Championing fairness

Ultimately, by formalising a mathematical notion of algorithmic fairness, we give ourselves a way to remove biases at the data stage, during the model training stage and through post-processing adjustments. Integrating and monitoring fairness constraints in this way can ensure that algorithms provide the same level of opportunity for every group throughout society.

Deploying machine learning models is a responsibility as well as a tool for all businesses that work with AI. While removing bias is essential for improving customer onboarding and user journeys, we have an ethical imperative to minimise cases of AI bias and understand how it arises in our models. In particular, global financial services with such influence over wealth distribution must ensure they do not exhibit biases that could hinder the opportunity of certain groups, which is critical as we enter a privacy-preserving world.

 

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SET YOUR BUSINESS UP FOR SALES SUCCESS IN A POST-PANDEMIC WORLD

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SET YOUR BUSINESS UP FOR SALES SUCCESS IN A POST-PANDEMIC WORLD

Dean Fiveash, Head of FinTech Sales, IFX

Without doubt the Coronavirus pandemic impacted every aspect of our lives and fundamentally changed the way in which we all conduct business.

From the widespread adoption of working from home, to the amplified focus on employee wellbeing and work life balance, to simply acknowledging that people are more than their job titles and are often juggling childcare, pets and terrible wifi issues all whilst trying to do their job. The last 18 months have altered the way we work forever and in order to set our businesses up for success we have also needed to rethink how we operate.

Dean Fiveash

In a people facing sector like sales,  it’s  clear that the loss of face-to-face interaction is perhaps the biggest loss and an impending challenge as we slowly emerge from the confines of the pandemic. Gone are the days of instant downloads from ‘water cooler’ conversations with the team discussing deals or general matters. Instead, our inboxes and diaries are full of zoom catch ups. This isn’t to say that success has dwindled. Flexibility of working from home has helped many businesses to grow rapidly. In fact at IFX we have enjoyed our ten best months of company sales, but there is no denying the way in which we work within our teams has shifted. So how can you set up your sales teams to maximise its chances of success?

 

Adapting To The Times

For many businesses operating during these unprecedented times the shift towards the work from home culture has seen its benefits. Speed is key in the fintech industry and video calls on top of isolated working has greatly improved our time efficiency allowing us to do more for our clients in the long run. Equally, with the workforce being spread around the country and in some cases even globally, came the need for further rigorous checks and processes to ensure the high standards set in the office environment are still being met.

Despite this I would argue that this made us better sales people, and in turn a more successful and thriving sales team.

Post-pandemic success is grounded in not just the talent of your employees but also how you choose to structure your teams. For me, the old adage ‘People Buy People’ remains the most relevant factor for developing a slick sales team. At the end of the day, the technical stuff can be learnt over time but the proficient people skills needed in client facing roles is more innate.

When evaluating team skills, individuals who demonstrate determination and the ability to keep smiling through adversity are a vital asset, especially in the fast paced fintech industry.

Having worked in numerous team leader roles within the sales industry,  I know the difference that a collegiate and supportive team can make to successfully securing deals. The key is to have people at your disposal who are going to pitch in to help others, in turn making the team more robust. In the post-pandemic world, this will remain the key quality to look for and embed as a core value across the business.

 

Fostering A Successful Culture 

Whilst the team structure and core skills are an important part of the team set up, good management and personal development structure is crucial to success. At IFX, our sales leadership team all have client portfolios and are regularly signing and navigating deals. It’s through giving my team practical experience and regular client interaction that we can gain far better market insight than through managing team activity or KPIs alone.

More discipline is also required when working at home to retain the sales focus whilst navigating domestic distractions. As such, maintaining your employee motivation and focus is something each business should work on. A difficult feat without the physical presence of your team and one balanced on knowing your employees and their individual needs. But little things go a long way, so incentives and perks such as company socials, bonuses or simply a free breakfast can work wonders to motivate others. Another tip is to set  attainable goals and regular check-ins with your team to keep motivation on track to reach peak productivity.

 

Looking Forward

Team dynamics will continue to change to adapt to the ever-changing and rapidly evolving landscape, the secret to success will remain the same.

Something to look forward to in the next couple of years as a movement,  is the greater adoption of smarter contracts and embedded FinTech, which of course as businesses and as a team we will have to adapt to.

Ultimately, my biggest piece of advice to others is to get the basics right.  A leading-edge solution fails to achieve greatness if it isn’t backed with competent sales/relationship managers and attentive operational support. Traditional ingredients for success such as reputation and trustworthiness are built over time, often through word of mouth, but building a competent team who can make your clients happy is essential to that mix

 

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THE EVOLVING TECHNOLOGY NEEDS OF THE FINANCE DEPARTMENT

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THE EVOLVING TECHNOLOGY NEEDS OF THE FINANCE DEPARTMENT

Jennifer Sims, Senior Consultant at Xledger

 

The world of finance software is evolving quickly, but with many new software contenders entering the market it can be a mindfield for organisations. Many finance teams are already using multiple accounting apps and software packages for bookkeeping, payroll and invoicing to service individual needs. Whilst it may work fine for now, this segregated approach isn’t sustainable for long-term growth. The world is swiftly moving to agile, automated ways of working. As a result, there is a growing need to choose suppliers that can fulfil multiple functionalities within the one platform.

Financial software is evolving at such a pace that it can be difficult to keep up. Changing up a finance solution is a big step and ease of migration can be a substantial factor in determining which solution provider to go with. But how do you choose a solution that will grow with your business and still offer something innovative in five or ten years down the line? The fear is always that non-techie organisations will end up falling behind, but in such a highly concentrated industry, how do you decide which solution would work best for you?

 

Cloud-first: the term that makes all the difference 

You could find a ‘cloud-based’ service with an application that comes with automated audit trails to make it easier to meet compliance and record-keeping obligations, for example. But for a solution to offer all of the many future benefits promised by the cloud, it needs to have been built specifically for a cloud environemt from the outset – ie. not an on-premise built system that has been later adapted. Cloud-first services (true cloud) were always intended to leverage economies of scale, cope with live updates, be accessible from anywhere with an internet connection, and to scale rapidly, to name just a few of the many benefits.

When we talk about innovation in financial technology, we’re not just talking about software that makes it easier for the financial controller to create reports. If eliminating reliance on Excel spreadsheets is the only tangible benefit you have to really shout about, you are missing out on the real deal. With ‘true’ cloud finance software the sky is the limit.

Finance and accounting technology needs to directly meet the needs of the finance function and support the wider business needs.  When looking at accounting software platforms you’d be hard pressed to find one that doesn’t now promise ‘cloud-based’ enterprise resource planning (ERP) capabilities. The cloud is nothing new, but it’s the way that a solution harnesses this environment that makes a real difference. And here is where there is a need to read between the lines.

 

Automate more with true cloud 

Historically, repetitive and manual tasks are typical of the finance role – from invoice postings to expense claims handling – these can overwhelm the finance team. Research by Xledger[1] has found that an enormous 91% of CFOs and finance decision makers are carrying out at least one of these repetitive tasks as part of their job. What’s more, senior finance leads are averaging a whopping 25 hours per week carrying out repetitive and manual tasks, compared with 15 hours for other finance decision makers.

A modern, true cloud finance system can enable your business to automate repetitive tasks and provide one source of truth so that teams can make informed business decisions that will help to scale a business. Bank reconciliation, dashboard creation and reporting are just some of the tasks that can be handled automatically.These capabilities are aiding overtasked finance teams and saving hundreds or thousands of hours a year.

Whilst different companies are at different stages in their digital transformation what is clear is keeping up with the latest technology is fundamental to the future success of an organisation.

Xledger is a true cloud finance solution. The basics include invoicing, robust general ledger accounting, detailed slice and dice reporting, purchase orders, billing, VAT reporting, and cash and bank payments. It also adds process and structure to the enterprise with procurement and inventory, budgeting and forecasting, and project accounting. Users are always on the latest version of the software and with regulation more stringent than ever today, Xledger is ISO 27001 accredited.

Choosing the right provider for your financial ERP solution comes down to whether it has the fundamentals right. When hosting all of your vital data in the providers’ own servers, it should evidence a highly tested security process that comes with backup services as standard.

As our demand for technology capabilities grows and as ERP models progress, innovation will become the structure for growth – and there is no end to the possibilities.

 

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