By John Watkins, Industry Consultant: Fraud Strategy and Intelligence Division at SAS
Customer identity is a precious asset and a highly prized commodity. As the financial services industry has become more digitised down the years, customers’ digital identity decides what they can do and what online services they have access to.
This has been revolutionary for the customer experience. So long as the service provider is happy that customers are who they claim to be, they can access their account, make a transaction or take out a loan anywhere and at any time they want. Indeed, the speed and ease of this process has become a point of competition, with organisations vying to provide the most seamless and frictionless online experience possible.
However, in the rush for greater convenience, the industry can’t afford to forget about proper identity verification. The threats from identity theft and cyberfraud are growing more sophisticated and pernicious. Organisations must guard against this, but in a way that doesn’t penalise the innocent customer.
A global identity crisis
Digital identity is a convenient means of authentication, but the lines are starting to blur. The industry has woken up to the fact that online identities are malleable and spoofable. Impersonating customers online to commit fraud and gain access to their financial assets has become big business for cybercriminals. In 2017, identity fraud peaked in the UK with 174,523 cases reported by Cifas, with eight out of 10 fraudulent applications made online.
What’s more, these hackers are constantly innovating and adopting new technologies to stay ahead of security measures. Even popular, tried and tested measures like two-factor authentication have been compromised.
Cybercrime is fundamentally adversarial. A lone wolf or criminal outfit will probe every weakness in your verification system and will stop at nothing to breach your defences. You need to cover all your bases and ensure they have no place to hide.
At the same time, however, you mustn’t go too far in the other direction. You have a duty to protect your customers, but also to provide them with the best experiences – something an endless loop of authentication methods can never deliver.
The 5 senses of security
When you meet someone for the first time, you make use of all your senses to get a first impression of them. The same principle should be applied to online interactions with customers. Why wouldn’t you use all the information channels available to you to find out if the user is being honest?
The problem is that credit, fraud and risk managers and their staff too often make the call based on incomplete insight. This is because they only collect and analyse some of the data that’s on offer.
If you don’t consider every possibility, you’re only leaving blind spots to be exploited. For example, an authentication system may approve a request from cybercriminals simply based on the device they’re using. Yet if the system had checked the device’s location and the customer’s behaviour, the hacker would likely have been exposed.
The main data points to consider are:
- Experiential information: The organisation’s previous interactions with and knowledge of the user based on an existing profile. Has this user been denied access before, and why?
- Channel information: The channel or device the entity is using. Has the user accessed your services with a certain device before?
- User behaviour: The behaviour of users while they’re interfacing with your services. Are they hesitating too long when asked to make decisions? Does their cursor move robotically?
- Public record: Publicly available information on the customer. Are they accessing their services from their registered address? Does their given age match what’s on their driver’s license?
- Group and risk analysis: Wider data from analysis of the market and threat landscape. Is the email address the entity is using part of a known fraud cluster?
Organisations don’t have to implement every data type into their verification process. Yet every new segment they do adopt vastly increases their chances of detecting and stopping fraud in progress.
Time waits for no one
However, data alone won’t protect you or your customers. Once you have the data and a process in place for discovery, you have to do something with it. While models do an outstanding job predicting fraud, rules stop it. At the same time, you need to act quickly. Customers won’t wait around if you spend more than 10 seconds weighing up their credentials. The process has to be seamless and instantaneous.
Yet the industry’s approach to authentication has sadly become segmented. There are thousands of point solutions that cover only one part of the verification process. They are rarely joined up and only waste customers’ time and patience. It’s critical that the process begins and ends with the customer experience in mind.
Turning insight into authentication
To turn insight into an authentication decision, organisations should consider an end-to-end solution. When a customer tries to sign-in or access a service, an orchestration platform should be set up to collect all the desired data points before sending them to a decision engine. The solution can then analyse the data and evaluate if entities are the customers they claim to be.
When the process for verification is unified and data-driven, passive authentication becomes a reality. The customer enjoys a real-time, seamless experience – no password required – while the decision engine rapidly confirms identity in the background. This is security and customer satisfaction all in one.
There is no silver bullet that will protect your organisation from cyberfraud in every event. However, when you have access to the right data and the capability to interpret and act on it in real time, you achieve the best of both worlds.
REACHING THE NOT-SO DIGITAL NATIVES
By Garry Hamilton, Group Business Development Director, Equator
It’s 2020. There’s no denying that banks and financial institutions have found themselves in a war against the tech giants in recent years. But can they win? Can consumers ever be truly satisfied? Or will institutions in this space stick with what they know regardless of how well it is working? In the digital-first now, FS companies have moved into an uneasy but rewarding landscape. Just as with consumer goods, they find themselves in a space where they no longer innovate ahead of consumer aspiration and demand, instead finding themselves increasingly under pressure to catch up.
The experiences consumers have with global giants such as Google, Amazon, Facebook and Apple (GAFA) define their expectations for all digital experiences. To stay up to speed, FS companies need to understand the shift in consumer demand as well as the multitude of threats to a business model that’s seen as traditional and staid. In short, they need to prepare.
The paltry, taped-together digital offerings from the incumbent financial service brands no longer stand. However, these brands still go to market with products and services defined by internal processes and limitations, giving little consideration to true service design principles or customer experience.
Thankfully this is changing, in part by credible (and incredible) upstart fintech companies, chipping away at the monoliths. At Equator, we work with several brands in this space, including Santander and Virgin Money, all of whom have realised the tides are changing. Major finance brands are no longer looking for the sharks in the water that come after all they do, instead realising that it’s a multitude of piranhas that pose the most significant threat.
Case in point: TransferWise has demonstrated that something as mundane as foreign exchange can be made fresh; Atom Bank has shown that a lean approach to savings and loans can drive solid business without being so reliant on rate, and Starling Bank has demonstrated that a serious focus on making the tech work can yield excellent results. Consumer choice for financial services has never been greater.
The hidden threats that GAFA may pose on traditional finance brands are, as yet, not fully realised. Apple has already demonstrated its ambition in the US with its digital credit card offering. Amazon has Amazon Pay and shown interest in the insurance market. Facebook is out there with its (stumbling) cryptocurrency effort, and Google’s feature-creep into aggregation (and payments) indicates a genuine and poorly understood threat from some of the wealthiest and most capitalised tech companies in the world. It’s hard to imagine a reality where consumers reject financial services from these brands.
But for incumbent brands in this space, the opportunity to maintain success lies in two key areas. Firstly, data. While it’s commonly understood that this is the currency that enriches the GAFA businesses, consumers’ financial behaviours are still broadly out of reach. Banks and financial institutions with historically loyal customers are sitting on a gold mine of data that can be turned into actionable insights. Insights that could deepen loyalty, increase relevance and make historically uninteresting and stuffy institutions appear modern and relevant.
Secondly, these organisations have significant human knowledge capital. These people know how the wheels turn, how to negotiate regulation and compliance, and how to manage risk. When you look to the most successful start-ups, their success is less borne of wealth, but more of knowledge and how financial systems operate. That cannot be underestimated. Banks and financial institutions need to strive to keep their staff loyal – not just the traders with their extreme bonuses. They’re not the ones that tech businesses would come after.
Getting a financial service off the ground isn’t cheap, but that’s not something GAFA worry about. Instead, it’s the complexity of negotiating the regulations and marketplace. What FS brands need to watch out for is that the fintech piranhas do not become sharks – not necessarily through growth but through acquisition and consolidation. Acquiring TransferWise, Monzo or Starling Bank is still pocket change to these organisations. And they DO have the technical wherewithal to bring autonomous platforms together and make a success of it, something high street banks and insurance companies have proven incapable to see through.
To survive and thrive, financial brands should take advantage of the one thing they’re historically good at – assessing and mitigating risk, with the critical difference being that keeping it the same as it’s always been is no longer the safe option. At Equator, we’ve already seen clients, such as AXA and Lloyds, acquire or partner with fintech start-ups. There’s a real effort from the high street banks to deliver a Monzo-esque functionality to their customer base. And we see real innovation in everything from insurance to loans and savings.
But there is still a long way to go. Regulation in the UK has been reasonably balanced between control and competition since 2007. However, technology continues to outpace the law, and we need to keep the pressure on the regulators to allow for new customer engagement models, new ownership models and new ways to deliver financial products and services.
In the last few years at Equator, we’ve assisted many major financial institutions take on tomorrow by helping them innovate and bring new products and services to life. We’ve helped Virgin Money bring their innovative B banking service to life, pioneered original service design in the most mundane of places for Tesco Bank and a lot more besides. We know that there are many enthusiastic brands out there looking to take on tomorrow and bring digitally-enabled services to life. But the sector still has some growing up to do. Crucially, it needs to accept that the disruption that came after the 2007 financial crash has nothing on what is around the corner
We’re still only really getting off the ground with the second payment services directive. Open banking is creeping in. We’ve yet to see the promised liberation of the payments sector (which should be huge), and it’s fair to say we should expect more niche disruptors to emerge, as money continues to pour into the sector. And that’s not even covering off the effect that machine learning and automation will continue to have in the industry over the coming years. If you ever dared to think finance was dull, get ready for a disruptive and exciting time.
RISK VS REWARD: IS AI TAKING OVER?
Xavier Fernandes, Analytics Director at Metapraxis
A study by Oxford University academics into “The Future of Employment” in 2013 prompted apocalyptic headlines which stated that in the future 40% of jobs will be automated thanks to advancing technology.
The researchers subsequently claimed that the truth was in fact a little more prosaic; rather than facing complete automation, the research found that 40% of jobs faced some aspect of automation in their activity. So with new ‘AI processes a likely reality for almost half us, what does that mean for our current roles and should we be worried?
The fourth revolution?
The first industrial revolution saw machines replacing muscle, both human and animal. The second and third saw electrical power, mass production and computerisation revolutionise the job market. Now, with daily headlines of AI as an employment superpower, there is some concern that AI is bringing a fourth revolution, and with it, unknown circumstances.
This ‘fourth industrial revolution’ is defined by replacing brain power with machines. Our thinking capacity is what inherently sets us apart from other species, so it’s not surprising that any encroachment on it triggers some existential angst.
Evolve to reap the rewards
While many businesses still don’t fully understand the capabilities of AI, those who fear its development are, instead of embracing it, missing all the benefits that it can bring to the workplace. Businesses that utilise AI appropriately are seeing vast improvements across their entire value chain; better customer experience, reduced costs, and more insightful analysis to support management decisions.
AI is particularly useful for supporting tasks with repetitive activity, for example, performing financial checks and assessing large sets of data within financial services firms. AI performs particularly well within this context, spotting outliers before a human expert would notice them, allowing impending problems to be flagged and avoiding costly mistakes.
There is also an increasing focus on maximising customer lifetime value through the use of AI. Being able to predict existing customers’ needs as well as track trends in their financial circumstances is supercharging the old cross-selling approach with testable, predictable outcomes.
With potential benefits like these on offer, management teams of innovative financial services are increasingly relying on AI to help them with some of the heavy-lifting of analysis. Using advanced data capabilities and learned behaviours, AI analyses market trends to provide predictions of future performance. This insight is invaluable and allows management teams to change direction and correct any problems accordingly. This offers a huge advantage over those that have not adopted such tools.
Supporting the workplace
Algorithms and AI are typically ‘smart’ at doing one, tightly-constrained task, but they can be less helpful with many of the activities that humans find straightforward. In most white-collar jobs, automation tends to replace certain tasks in the job, rather than the role in its entirety, as the need for human intelligence is still highly necessary. In particular, we still need human input to first challenge, and then synthesise, this information before taking action. Employees should therefore work with the business to proactively identify what areas of their role could be automated, so that they can focus on the areas that add real value to the business’ commercial goals.
Challenging AI is certainly still important. We know that algorithms can be much better than humans on certain, bounded tasks. However, many algorithms rely on existing data sets to build their understanding. As a result, when a business unit has ‘symptoms’ that fall outside of that body of knowledge, the algorithm may suggest the wrong course of action with costly results.
Indeed, even with plenty of data, algorithms will reflect any biases the data set contains. We’re seeing this with some legal sentencing algorithms where there is evidence that they are treating disadvantaged people more harshly. Getting the answers to why and how far we should trust our algorithms should therefore become an everyday part of any job affected by AI.
Rather than depending entirely on AI for all decisions, workers should be taking all these new, AI-generated insights and using them to complement the human decision-making process. No manager of a complex business ever has enough time to sieve through all the analysis available, but with AI driven algorithms able to flag up any issues and indicate where action needs to be taken, we may find that we have some AI ’colleagues’ who will cover our backs and suggest innovative options. Yes, there will be times when the algorithms get it wrong, but as long as we’re watching out for those, the future is bright.
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