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MAINTAINING SECURITY: NOT SOMETHING TO LOSE CUSTOMERS OVER

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By Philipp Pointner, Chief Product Officer of Jumio

 

They say it takes 60 days to make or break a habit. With the UK having spent over 100 days in lockdown, old habits have changed and new ones have formed. While restrictions are starting to ease, these habits will stay with us, including how we choose to manage our finances. While prior to the pandemic, we may have gone to the bank regularly to deposit a cheque, change our bank account or open a new one, this habit has now been broken, putting the role of the branch in question.

Well before the outbreak of COVID-19, bank branches were closing in large numbers. More than a third of the UK’s bank branches have shut for good in less than five years, while hundreds of those that remain have reduced their business hours.

These macro changes in how we interact with our finances impacts financial institutions, which have had to adapt to allow current and prospective customers to access services remotely with the same level of security. Digitalisation in banking has been happening for years, but the global pandemic has significantly accelerated these efforts. While newer challenger banks have a reputation for faster sign-ups and seamless customer experience, security remains a top concern, particularly when the annual value of online banking fraud losses eclipsed £112 million in 2019.

Fraud detection measures have a reputation for making the customer experience worse. How can we preserve the user experience without compromising online security?

 

Philipp Pointner

The best experience vs. the best security

Top security at the account sign-up stage is essential, yet nearly half (48%) of all fraud value stems from accounts that are less than a day old. Experian’s 2020 Global Identity and Fraud Report found that account opening and account takeover are responsible for higher losses than any other type of fraud. The account onboarding process is one that carries many risks — financial, regulatory, and reputational — when identifying the true identity of a customer, especially when not done in person.

In ensuring fraud detection, measures with incremental friction are often put in place to keep identities secure. However, too much friction can be problematic, with nearly 40% of potential new customers quitting onboarding processes which are too time-consuming and onerous. This level of abandonment represents a significant cost for financial institutions. With friction having such an impact on conversion rates, there are lessons traditional banks can learn from their challenger counterparts when it comes to customer experience.

 

How do we solve this?

For many consumers digital banking is not new, but the global pandemic has forced others to try digital banking for the first time because there are no other options. How many of these consumers will return to a physical branch when lockdowns are lifted?

When onboarding, whether online or in branch, banks perform the same set of steps even though the process differs. While banks are required to perform the necessary due diligence as part of their KYC obligations, many of the onboarding steps required in-branch can be automated, streamlined and simplified to deliver a better customer experience.

Face-based biometrics have the power to help banks strike the right balance between customer experience and security when it comes to digital verification. When a customer goes to set up an account, the bank asks them to take a picture of their government-issued ID (e.g., driver’s license, passport) and a corroborating selfie. This process determines if the ID is authentic and if the person in the selfie matches it.

To make this process even more secure, online solutions are now embedding certified liveness detection in the selfie-taking process to make sure that the customer is not attempting to spoof the system with a deepfake video or a picture of a picture. By leveraging biometrics and AI, an accurate verification decision can be made in a matter of seconds, which dramatically lessens the friction and frustration experienced by most online customers.

 

Going beyond onboarding

With over 60% of financial institutions experiencing an increase in fraud volume over the last few years, and cyber fraud as the primary concern, top-end security needs to go beyond the onboarding stage.

Face-based biometrics can also serve as the answer to ongoing authentication. During the initial identity verification process, better online solutions create a 3D face map, containing over 100 times more liveness data than a 2D photo. When a future authentication is required, for example, when a customer tries to reset their password or initiate a wire transfer, the customer is asked to take a new selfie, during which a new 3D face map is created. This face map is compared to the original and authorises the transaction in seconds with a significantly higher level of identity assurance.

This holistic approach is required now more than ever, with fraudsters taking advantage of the surge to digital.

 

So, what next?

Digitalisation is no longer just an important priority — it must be a primary focus for all regulated financial institutions. When lockdowns were announced all around the world, challenger banks were better prepared to support their customers online, but while they may have had an advantage at the start, it doesn’t need to stay that way. With the extraordinary power of face-based biometrics and AI, financial institutions can level the playing field by delivering an online experience that balances account security and customer usability.

 

Technology

HOW TO ACHIEVE THE BEST POSSIBLE CUSTOMER EXPERIENCE THROUGH ARTIFICIAL INTELLIGENCE

By Craig Charlton, CEO of SugarCRM

 

Before high definition televisions were introduced, home entertainment was limited to a grainy picture on a small CRT box in the corner of your living room. It could in no way compete with the picture quality seen in cinemas, for instance, with details hard to pick out. The difference was like chalk and cheese. In the early two thousands this all changed with the introduction of high definition LCD tv’s, which took home entertainment to a new level and made it feel like a truly immersive experience. More recently we’ve seen the introduction of 4K and even 8K devices, which have taken the experience even further.

In the world of CRM and CX, we’re now at a similar watershed moment. Currently, most businesses have a fragmented, dated and distorted picture of their customers, which is affecting the level of service that they can achieve and their ability to grow. Poor data quality is hitting organizations where it hurts, costing them time and money. It’s important that organisations act now to replace their current hazy view of their customer, with a sharply focused picture that’s rich in breadth and depth.

We call this a high definition customer experience, or ‘HD-CX’, and by delivering on it, businesses can reach new levels of performance and predictability, and increase customer lifetime value. There’s a long way to go however, as research indicates that 91% of data in CRM systems is incomplete, and 70% of CRM system data goes bad each year[2].

 

Craig Charlton

 Where can CX take us?

Forrester states that over the next 5-10 years CX will become “crucial for brands to survive, for them to avoid disintermediation, irrelevancy, blandness, and/or cluelessness about customer sentiment.”[3] Those brands that choose to wait 5-10 years before delivering a HD-CX experience however, will have found themselves disrupted and behind the curve.

To leapfrog competitors and fuel growth, companies need to  obtain a high definition view of their market, business and customers as soon as possible. Right the way from formulating ideal customer profiles (ICP) and identifying sectors with a propensity or intent to purchase, through to customer lifetime loyalty. HD-CX is all about drawing on accurate, up-to-date information from multiple sources and from across the organisation to reach new levels of business performance and predictability. The value of performance and predictability applies to businesses of all sizes and in all industries.

 

Why is being ‘time aware’ so crucial?

It’s about time we redefined the 360-degree customer concept and added the key missing component: Time. It was Benjamin Franklin that said, “Lost time is never found again.” Although nobody can stop the flow of time, what we can do is ensure a complete historical record of every change event in the customer journey, and augment this information via a rich repository of relevant information to ensure full situational and directional awareness of a customer.

It’s no good just having a 360-degree view of one moment in time—right now. Recording every change event in the customer journey is essential for predicting future outcomes. Accurate predictions enable companies to make better business decisions, manage risk, respond to problems and take advantage of opportunities. Organisations need to gain insight into the past, present, and future of their customer-facing business processes.

 

Stay ahead of your competitors with artificial intelligence

Making sense of all this data can be a perennial issue for companies, with the average company holding on average 162.9TB of data[4]. To make sense of this data, create a competitive advantage and deliver an unparalleled level of predictability across a whole array of different business use cases, Artificial Intelligence is the key.

Understanding your current state and how you got there is essential, but what if you had the ability to look into the future toward what your business could be? I’ve already stressed the importance of having a complete historical record of every change event in the customer journey to ensure full situational and directional awareness of your customers and your business, but AI considers the other direction of time: the future.

AI has the ability to deliver exceptional predictions, even with limited or incomplete CRM data by leveraging vast external data to consider factors your data doesn’t cover and surface insights that you may not have known existed. These unparalleled predictions allow businesses to make confident decisions and focus on the highest priority activities across marketing, sales, customer service, and more. However, not all AI-powered prediction is created equal and predication accuracy is essential for success. A proven platform with deep learning models combined with both the best quality external data and CRM data, is a combination that most companies can’t provide their customers.

If companies successfully create a time-aware and high-definition picture of their customers, they will benefit from greater customer relationships and unparalleled oversight of their businesses. However, they must utilise AI to provide exceptional customer experiences and business predictions. If businesses don’t adapt to change and instead continue to operate with an old, outdated, standard-definition view of their customers, they will lose out to competitors as they won’t be able to deliver an experience that their customers expect.

 

[2] https://www.dnb.co.uk/content/dam/english/business-trends/b2bm-db-improve-the-quality-of-your-marketing-now-1-0.pdf

[3] https://go.forrester.com/future-of-cx/#:~:text=The%20wheel%20of%20change%20is,relationships%20stable%20amid%20unprecedented%20upheaval.

[4] https://cdn2.hubspot.net/hubfs/1624046/IDGE_Data_Analysis_2016_final.pdf?t=1496694598964

 

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Technology

USING ARTIFICIAL INTELLIGENCE TO ACHIEVE CIRCULAR ECONOMY

By Professor Terence Tse, ESCP Business School

 

It is really only a matter of time before the two main trends, artificial intelligence (AI) and circular economy, would come together. A milestone of this convergence was the white paper “Artificial intelligence and the circular economy”: AI as a tool to accelerate the transition, jointly published by The Ellen MacArthur Foundation and Google earlier this year. It has kick-started the discussion on how AI can be used as a tool to help accelerate and scale our transition to a circular economy. This can be achieved by unlocking new opportunities through improving product and material design, enhancing circularity-based business models, and optimising circular infrastructure. The paper draws on the food and consumer electronics industries to illustrate the circular benefits driven by AI. The forecasted value that can emerge from these is encouraging: up to $127 billion and $90 billion a year in 2030, respectively.

 

The pace will be slow

No doubt these are very good news. It also shows how innovative technologies can take circular economy to the next level. Yet, I believe the path leading there will be full of challenges, not least because, contrary to what general media would like to get us to believe, the development of AI is, in reality, really slow.

 

There are several reasons attributable to this sluggish pace

First, there is a general shortage of AI-proficient graduates. Training up AI researchers takes time. Universities are not churning out data scientists fast enough to meet the job market demand. For those who are graduating, they will most likely be snapped up by the technology giants. Indeed, it has been estimated that some 60% of AI talent are in the employment of technology and financial services companies, leading to a ‘brain drain’ in academia, which in turn, slows down the production of qualified graduates. Small circular economy-based companies (as well as AI start-ups) will struggle to have the same hiring power, as they often lack the ability to match the levels of salaries and prestige offered by large organisations.

Another reason why circular economy-aimed companies, large or small, will struggle to deploy AI is that the technology remains a very expensive investment. AI is, at the moment, far from a plug-and-play technology. Arguably, there are off-the-shelf AI applications available in the market. But what this one size fits-all technology solutions can really do is often very limited and their effectiveness low. Inevitably, for AI to work at an acceptable, value-creating level, it is necessary to integrate it into the existing wider IT system. Customising AI applications to be embedded in the system architecture is very complex and hence very costly.

To make matters worse, the market is seemingly inundated with self-proclaimed AI companies. A recent report has suggested that 40 percent of start-ups in Europe that are classified as AI companies do not actually use artificial intelligence technologies in a way that is “material” to their businesses. As someone who researches and works in the business of AI, I can readily observe this phenomenon has already eroded the trust of many companies, making them increasingly cautious when proceeding with investment and deployment of AI.

 

Gradual developments, not quantum jump

For these reasons above, the adoption of AI, and by extension, in the area of circular economy, will be slow. This, however, does not mean there will be no advancement. Instead of “big bang” new business model creations, AI will most likely produce circular advantages through baby steps in operational enhancement gradually. For instance, one of the important elements in achieving circular economy is better asset management. In a recent research project for the European Defence Agency, my colleagues and I have discovered that there is a wide spectrum of operations for ministries of defence to save money and practise circular economy, from refurbishing and repurposing small military equipment items to reduce waste and minimise the use of virgin materials to extending the service years of capital assets. Unquestionably, the same may be applied to civilian activities. For example, combining the power of AI and drones can extend the longevity of major infrastructure such as reactors and bridges.

Advancements in drone technologies have allowed them to be deployed to take pictures at heights that are dangerous for inspectors to reach. The contributions of AI come from its ability to analyse and identify cracks as well as defects on assets that are not always visible to human eyes from captured images. Consequently, problems are detected before the assets become irreparable, thereby lengthening their lifetime.

A seemingly insignificant but potentially huge possibility of waste reduction would be saving on paper use. In the insurance industry, for instance, there is still a huge reliance on actual paper, with the communications between various stakeholders, including the underwriters, brokers and insured, passing on a large number of physical documents. AI techniques, in particular natural language processing, can help speed up the digitalisation of documents as they can go beyond the point of just reading and processing text to recognising and recording signatures and rubber stamp marks. Little by little, it will be possible to lower paper consumption.

 

The future is now

Both AI and circular economy are by themselves breakthrough ideas that are set to change the world dramatically. Combined, it can be a very powerful force of good. But this can only be achieved if we can synthesise them. For AI and circular economy to work together, it is necessary to educate AI developers to be more familiar with the idea of circular economy as well as making circularity practitioners and researchers more AI-savvy. Holding just half of the equation, we risk missing out on most of the intelligence. After all, no matter how smart machines can be, ultimately, it is the human intelligence – or stupidity – that determines the kind of future that we will be having.

 

Extract of “The AI Republic: Building the Nexus Between Humans and Intelligent Automation”

 

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