by Babak Hodjat, VP of Evolutionary AI at Cognizant
Many banks and other financial services institutions (FIs) are beginning to recognise the benefits of AI-driven solutions as a way to get ahead in the market and challenge the competition. Amongst many other benefits, the technology enables organisations to offer hyper-personalised customer experience, dramatically improve internal decision making, and drive operational efficiency. However, many businesses are struggling to move beyond the experimental phase and reach actual AI deployment. It is those organisations that are at risk of being left behind.
The financial world has already been transformed by AI, and this transformation is continuous. A new breed of AI, known as ‘evolutionary AI’ has begun to further accelerate innovation. It is capable of automatically designing itself with little need for explicit programming by humans – innovatively creating complex AI models, and optimising decisions considering multiple scenarios.
This technology is revolutionary for industries across the world, but in particular it is set to transform the financial services sector. Enabling businesses to spot novel strategies that would never have been identified by human data scientists, and, in turn, allowing companies to take full advantage of today’s massive data sets – evolutionary AI will soon be a vital tool in all FIs’ arsenals.
The nuts and bolts of evolutionary AI
Emerging technologies that enable AI algorithms to design themselves are allowing organisations to transcend human limitations. Evolutionary AI operates iteratively. Firstly, it randomly generates a set of potential solutions to form an initial population and assigns a score to each solution based on how well it performs relative to other solutions. In the second round, it retains the solutions that performed best, perhaps only 5% of the total, and recombines their components, sometimes “mutating” them to create a new population. This new population is then tested, and the process begins again. Over multiple generations, the appropriate components of the more successful solutions become increasingly prevalent in the population, and eventually a solution is discovered that yields the best outcomes.
Advantages and use cases
Compared to human design, evolutionary AI can be deployed far more quickly, avoids biases and preconceptions, and typically performs better. Furthermore, the chosen model will evolve and improve over time based on new data.
Evolutionary AI can be applied in a wide variety of areas at FIs. Some examples include designing quantitative trading strategies to maximise returns while minimising risk and loan underwriting. Rather than relying on human analysis, evolutionary AI solutions can quickly analyse all the combinations of relevant variables to create models that more accurately assess the risk of default by a potential borrower.
A recipe for success
In order to reap the benefits of the technology, FIs should focus on the following:
- Responsible AI – Behave in ways that make customers and employees comfortable, i.e. not making decisions that are unethical or exhibit bias. Companies need to monitor them to ensure they continue to act appropriately, as they learn and evolve.
- Viewing AI through a business lens – Having AI projects managed by cross-functional teams with business executives in the lead is a good place to start. Companies also need to look across their organisations to identify opportunities to generate concrete business value from AI — not only in reduced costs but also in boosting revenues by delivering enhanced customer experiences and through improved decision-making.
- Enhance data management – AI applications depend on access to timely and accurate data, which is a challenge for many FIs that have fragmented data architectures with multiple legacy systems. Companies need to identify which types of data are required for each AI project and ensure they can be captured in an appropriate format.
- Approach with speed and caution – AI projects need to be rolled out quickly, while at the same time be rigorously measured, so failures are terminated promptly while successes are moved into production.
The sophistication of AI technology is set to significantly improve over the coming years as it continues to design and test itself. As a result, it will become more critical to the productivity of FIs, and soon businesses will recognise it as a vital tool for consulting on important business decisions. It will not be long before humans and AI are working alongside each other, with robots handling routine tasks, enabling employees to focus on more complex and sensitive activities. Delivering more value together than either could on their own.
WHY DIGITAL TRANSFORMATION IN FINANCIAL SERVICES IS ABOUT CULTURE FIRST, TECH SECOND
Stuart Templeton, Head of UK at Slack
In today’s world, there’s no such thing as a ‘non-tech fin’. Every financial services company needs to consider itself a fintech in order to bring about the innovation, speed, and transparency that customers expect, and that’s why most are pumping significant investment into their digital transformation efforts.
Part of the challenge faced by traditional incumbent banks is that they rely on legacy core systems that stifle the speed of change. These core systems were not built in an API first era. The good news of course is that the obligations of PSD2 and open banking have gone some way to facilitate future innovation.
While legacy banking platforms do continue to present a technical challenge, the human one can be even greater. Traditional institutions are often faced with the prospect of rebuilding their culture from scratch in the pursuit of becoming digital-first. Like many industries, the fundamental challenge is one of coordination: the creation and maintenance of alignment over time.
Couple this with the fact that the expectations of today’s workforce are changing, then companies in the industry have a real job on their hands. A growing percentage are digital natives, and millenials – who greatly value trust and transparency – make up the largest proportion of the workforce today. So how have businesses in the industry historically ingrained culture, and how does this need to change?
Old ways of working – Team A, and Team B
Traditionally, the culture within large financial organisations has been separated by two distinct teams: operations, and tech. They are driven by seemingly opposing forces – one by GANTT charts and lofty business goals, the other by agile software delivery and customer obsession. Often, the two don’t even speak the same language, let alone collaborate and share ideas. Of course there are digital projects, but they aren’t the embodiment of the business, and often tech teams find themselves battling to get buy-in from internal stakeholders who are somewhat removed from those that drive innovation.
Part of the problem is even the notion of having digital transformation projects – there is no such thing in today’s environment – as digital is an overarching movement, and financial services institutions must think of themselves as ‘digital factories’ in order to see a marked change. It is no longer enough to deliver tech updates both internally and externally once every few months, with speed diminished by layers of bureaucracy.
What needs to happen, then, is that these two business segments need to find a way to blend that helps the old incumbents forget their binary ideas of teamship from time gone by and instead let them come together to become one unit. Flattening the established hierarchy so that workers from across all lines of the business can communicate, share ideas and identify problems in real-time is, after all, the key to addressing the transformation gap. They need to think on their feet and iterate as they go: it’s agile thinking, but permeating outside of just the software delivery cycle.
Eating the elephant – one bite at a time
The solution, in theory, is relatively simple: companies need to break open the silos of information created by technologies like email and ensure anyone within a business has access to the knowledge and skills they need to make their projects a success. But of course, in practicality, this can present a seemingly insurmountable task.
Using technology to create an agile and transparent working environment that fosters collaboration is key for many financial services organisations that want to see real tangible results from their investments. Digital natives such as TransferWise and Starling Bank are getting this right by prioritising a decentralised business model, one that empowers collaborative working and knowledge sharing that in turn has a positive impact on employee satisfaction and retention.
They do this through collaboration hubs that provide a rich, permanent, searchable record of knowledge for everyone in the organisation.
Looking ahead: Team ‘us’
Predictions are very difficult, but in five years’ time we can expect to see a greatly altered perception of the financial services industry. We can expect that digital communications tools will continue to play an integral role in the evolution of their workforce culture, helping to bring the right people together internally within the business, as well as strengthening relationships externally with partners and customers alike.
Ultimately, in order to keep learning and improving, banks need to ask questions of themselves as competition and customer demand becomes more fierce: “Why are we doing this?” “What’s the benefit here, and who are we considering in the pursuit of this goal?”
To answer these things, a culture of collaboration and openness is key – underpinned, of course, by the tools that empower it.
DISPELLING BIOMETRIC MYTHS AND MISCONCEPTIONS
By Lina Andolf-Orup, Head of Marketing at Fingerprints
Gangsters cutting off enemies’ fingers to access secret locations and spies lifting fingerprints from martini glasses – the imagination of the entertainment world has been running wild ever since biometrics entered the scene.
Couple that with the limitations of some early biometric solutions from fifteen years ago, still anchored in the minds of many consumers, and you have the perfect recipe for an apprehensive and uncertain public.
Thawing lukewarm attitudes with a biometric touch
The biometrics industry has made great strides in the last few years – something particularly true for smartphones. Fingerprint authentication has replaced PINs and passwords as the most popular way to authenticate on mobile, with 70% of shipped smartphones now featuring biometrics.
And it doesn’t end there. Many adjacent markets are now eager to benefit from the secure and convenient authentication solutions that biometrics offer. Take the payments industry, for example, where biometrics payment cards are currently gathering real momentum.
However, some consumers are still uneasy about accepting biometrics. A recent study found that 56% of US and EU consumers are concerned about the switch to biometrics as it’s not enough understood to be trusted.
Although attitudes are shifting for the better, stats like this demonstrate there is still some work to do to disprove common biometric myths and showcase just how smart today’s solutions really are.
Dispel, adopt, repeat
The evolution in consumer biometrics in the last two decades has been phenomenal. And today’s solutions are far more advanced and safe than many may think.
To help bring an end to the myths, let’s expose some of the most common misconceptions around biometrics.
Myth: Biometric data is stored as images in easy-to-hack databases.
A leading myth about biometrics is that when a fingerprint is registered to a device, it is stored as an image of the actual fingerprint. This image can then be stolen and used across applications. In reality, the biometric data is stored as a template in binary code – put simply, encrypted 0s and 1s. Storing a mathematical representation rather than an image makes hacking considerably more challenging. In most consumer applications, this template is also not stored in a cloud-based location, its securely hosted in hardware on the device itself for example in the smartphone, in the payment card. Thus, it stays privately with its owner.
Myth: Fingerprints can be easily replicated to ‘trick’ devices.
The internet is full of articles and videos that claim it is possible to use materials from cello tape to gummy bears to craft fingerprint spoofs and access biometric systems. Although there may have been a time where gummy bear spoofing was the go-to party trick, todays’ consumer biometric authentication solutions have too many technological defences, such as improved image quality and matching algorithms, to simply ‘trick’ devices. Plus, on top this, the criminal needs to have access to the person’s device where this fingerprint is enrolled e.g. smartphone, payment card, before he/she notices and blocks it. This is not scalable nor common, in comparison to gaining access to someone’s PIN code or skimming a contactless card.
Myth: Physical change will prohibit access to my device.
Although our irises don’t change as we age, our fingerprints can and our faces will. Does that mean we have to update our biometric devices every few months to capture these changes? Not quite! Unless there are drastic, sudden changes, the ‘self-learning’ algorithms in modern-day biometric systems are able to keep up with our developing looks.
Who you gonna call? Mythbusters!
These are just some of the common biometric myths and misunderstandings perpetuating in consumer mindsets. Thankfully, though, while we’re working hard to rid the world of the myths, belief in the value of biometrics is only expected to grow. But as solutions expand and diversify, the myth-busting fight will continue.
Fingerprints has been a leader of innovation in biometrics for the last two decades. We’re proud of the expertise and R&D we’ve been able to pour into our biometrics solutions to deliver stronger security and a better user-experience. To learn more about the most common biometric misconceptions and the modern-day technology that allows us to dispel them, download our eBook here.
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