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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.

 

Business

OUTSOURCING YOUR IT SOLUTIONS CAN SAVE YOU FROM COSTLY DOWNTIME

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Amir Hashmi, CEO and Founder of leading IT and Cloud services provider Zsah, discusses why you need full-time professionals if you want to avoid the money pits of IT downtime

 

A lot of wealthy business owners will uphold the following infamous statement – time is money. Many CEOs believe that it should be at the heart of your business strategy. They aren’t wrong, and it is no different when it comes to IT. Therefore, it is high-time that businesses consider the real risks and costs associated with IT downtime, and do all they can to avoid it

In the midst of a post-pandemic technological revolution, it’s now more important than ever to carefully consider who manages your technology. It is essentially the motor that drives productivity, efficiency and growth, and if therefore, if there isn’t a thorough and dedicated system in place, businesses risk system failure, which can risk everything.

Something so essential to a company deserves to be taken more seriously than just to deploy the services of an IT help desk when there’s a significant issue. The answer isn’t necessarily to consider ways in which you can fix a problem once it arises, but instead to ponder upon ways of preventing an issue from occurring in the first place. This is what leads us to managed IT support services: your personal, dedicated team of IT experts that not only fix issues when they occur, but that also constantly improve the software and hardware so there is less chance they ever take place.

 

The real cost of downtime

Whenever your IT isn’t functioning at its full capability, you are losing money. Even the shortest of gaps in service can severely impact the customers’ experience, your reputation, and the output and efficiency of your entire staff.

In 2017, ITIC sent out an independent survey to measure downtime costs. It found that 98% of organisations say that a single hour of downtime costs over USD $100,000, with 81% putting the figure at over $300,000. For 33% of businesses, 60 minutes of downtime would cost their firms between $1 million and £5 million.

Figures from Statista.com reveal 24% of organisations worldwide reporting average hourly downtime costs amounting to between USD 301,000 and USD 400,000, with 14% reporting greater than USD 5 million in costs.

Elsewhere, IHS Markit surveyed 400 companies and found downtime was costing them a collective USD 700 billion per year – 78% of which was from lost employee productivity during outages.

 

Managed IT solutions are the key

Though we may never know the full cost of downtime, it is evident that it costs individuals and businesses a large amount of money. Don’t wait until your next emergency to remedy a problem; get the professionals in now to prepare for the future, rather than just fix problems in the present.

When you work with a managed technology services provider, your network and infrastructure are supervised 24 hours a day, all year round. As with any IT service, this means that issues will be fixed – however the real advantage is more long-term. As technology service providers perform regular proactive upkeep, there will be a reduced chance of suffering from issues in the first instance, and when (or if) they do occur, it will be far simpler to recover data thanks to full cloud integration.

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HOW TRADITIONAL INSURERS CAN USE TECHNOLOGY TO IMPROVE THEIR RELATIONSHIP WITH CUSTOMERS

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The customer experience with insurance is anomalous, in that one is only required to engage with their insurer if things are going wrong for them. To add value to the relationship, new technology and methods should be adopted, in turn driving loyalty and business growth, writes Oliver Werneyer, CEO and Co-founder of Imburse

Oliver Werneyer

Insurance is one of the oldest industries in the world and it is still, to this day, considered a grudge purchase. Looking back, insurance has a history of having a challenging relationship with its customers. According to an IBM study, in 2008, only 39% of consumers trusted the insurance industry. This percentage has stayed largely similar over the years, having reached only 42% in 2020. For any business with growth ambitions, good customer relationships are crucial.

I believe that now more than ever, the insurance industry not only needs to continue investing in improving relationships with customers, but to really think about new ways of doing so. At a basic level, the moment of truth for an insurance customer is when either they need to pay or are getting paid. Insurers can have the best policy wording, quick claims processes, apps and advisors, but if the experience to pay premiums or to receive a claim is bad, the customer immediately loses trust.

The pandemic has exposed this tenuous relationship between insurers and its customers. The need to move everything online and provide personalised services has exposed significant shortcomings in the service insurers provide. The industry has been too slow to adopt newer technologies and move engagements closer to the customer (self-service and empowered). This is largely due to the legacy systems and processes that insurers failed to modernise over previous years.

This means that the better-positioned incumbents have stronger customer relationships and benefit disproportionately from the pandemic, as they are able to win more new customers and convert customers from other insurers. They also benefit from significantly lower customer acquisition costs and much better growth, as illustrated in this McKinsey report. Even new entrants or InsurTechs are benefitting massively by focusing on improved customer experience and customer relationships.

However, it is never too late for insurers to build better relationships with customers. The main way to build a good relationship with a client is to make life easier, live up to promises and add value through the relationship with them. By working on these key elements, insurers can start building strong relationships with their customers, and, through the right partners, deliver this in a timely and non-disruptive manner.

 

Embedded Services

Insurance products often get a bad reputation because they cost money, but the benefits might only come much later, or never. Customers don’t get to experience a positive relationship with insurance products, either because they never claim and feel like they lost out, or they claim and they’re in a bad situation. By either embedding other services into the insurance experience to deliver a more transactional engagement, or embedding insurance products into general customer experiences such as online shopping or rewards, insurers can enrich customer relationships to generate value.

This way, insurers become a value-adding part of the customers’ everyday activities and not just a product that they have to pay for and may never get anything back from. One example is to embed micro-savings capabilities, often found in banking, into pension savings and insurance products. This can allow customers to save more for pension, attract younger customers and build a portfolio of fiscally disciplined customers.

 

Tailored journeys and personalisation

Customers have come to expect personalised journeys and engagements from product providers. Streaming services, social media, e-commerce or mobility services have shaped the customer expectations. Now, customers are also expecting personalisation for insurers.

Insurers need to invest very heavily in delivering personalisation and customisation to customers as they engage with their products. Failure to deliver this puts renewed strain on the value perceived by the customer and their relationship with the insurer. This applies not only to customer interfaces, but to aspects such as payments. Insurers should make it easy and pleasant for customers to pay and get paid. As the main moment of truth, payment experiences need to work optimally.

 

Perceived customer value metrics and delivery

The value customers derive from insurance products is, generally, monetary. Therefore, insurers must invest in product enhancement to increase its perceived value. Perceived value is not tied to a monetary value. By being able to choose between multiple payment options, such as a $300 pay-out to a bank account or a $320 Amazon voucher, the customer has a higher perceived value of the payment. This can be achieved by leveraging non-insurance products that can be purchased at a discounted price, exclusive access that the customer would otherwise not have or conversion into a form that is more useful to the customer.

Payments, for collection and pay-out, are at the core of delivering this value. An excellent payment experience immediately influences the customer to be positively inclined toward a product (PwC report). In order to offer this, insurers need to leverage multiple technologies and providers, offer any speed of transaction in any market, and deliver faster automation and better risk control. The key is to transform insurance products into transactional value-adds to customers’ lives and use this opportunity to continuously build on relationships with customers.

The main roadblock for insurers is still the operational implications of these activities and the costs that arise. In looking to build a better customer relationship, insurers need to look at partners that are operational enablers to deliver this. Partners that can solve the integration and speed-to-market problem so that insurers are enabled to deliver new capabilities, not bombard them with new ideas and no path to delivery.

Imburse, for instance, enables insurers to access all the global payment providers and technologies available in any market. Through a single connection, insurers can deploy any payment capability into any channel, for collection and pay-outs, without ever again needing to build a direct operational integration to the providers. This gives them full freedom to leverage payments as a key value driver and customer experience enhancer.

Building a better relationship with insurance customers is key for the insurance industry to close the protection gap. Incumbents are in the prime position to look at Insurtech and Fintech partners to rapidly and significantly modernise, digitalise and transform their own capabilities to deliver major enhanced value to their customers.

Imburse is an advanced universal payment connector that enables businesses to gain cost-effective access to complete global payments technology, regardless of the service provider. To learn more, please visit www.imbursepayments.com.

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