With over 10 years experience working solely in the Data & Analytics sector our consultants are able to offer detailed insights into the industry.
Visit our Blogs & News portal or check out our recent posts below.
Rosalind made the move to recruitment after several years in E-commerce and Real Estate. She is now a Senior Recruitment Consultant looking after Fraud Analytics for Harnham, within our Credit Risk space. She is excited to enter this new space and continue to learn about the fascinating world of Fraud and Financial Crime.
£42000 - £56000 per annum + competitive bonus + benefits
An exciting fraud and FinCrime department are looking for experienced consultants with excellent transaction monitoring knowledge and strong skills in SQL.
£50000 - £80000 per annum + bonus + benefits
By using core analytical skills and advanced modelling, your role will be pivotal in client application of the solution and minimising fraud losses.
With over 10 years experience working solely in the Data & Analytics sector our consultants are able to offer detailed insights into the industry.
Visit our Blogs & News portal or check out our recent posts below.
Ben Owen and Danni Brooke are the Co-Directors for the EMEA Practice at Fortalice Solutions, a leading global cyber security and intelligence operations company. They travel globally to assist clients with their cyber security requirements, bespoke training needs, intelligence and investigations both online and physical and counter fraud training/consultation. They deliver and manage a portfolio of pro-active intelligence solutions to keep people, nations and businesses safe from threats and head up the EMEA operations. Ben and Danni also feature on the hit Channel 4 show, Hunted and Celebrity Hunted which has been airing for over four years with another series set to be filmed this summer. I caught up with them recently to discuss the latest Fraud, tools and challenges for the Cybersecurity industry. Cybersecurity is an ever-changing landscape. What trends do you anticipate for the next 12 months and beyond? It is always difficult to pin down what the next real trend is going to be in the Cybersecurity space as adversaries are becoming ever more sophisticated. What was once a very difficult process for skilled individuals is becoming more readily available to novices with advances in software, particularly those shared on the Dark Web. What is an inevitable threat trend in the next 12-months and beyond is the exponential rise in the Internet of Things (IoT). With a world where everything is hooked up to the web, it is apparent that tech companies selling these devices are under immense pressure to get products to market. The need for speed could mean that some security principles and best practices may be overlooked. As the UK encountered during the Mirai Botnet attack of 2016, a network of electronic devices acting in concert can cripple the internet or, worst case, become a weapon that could cause actual physical damage as well as cyber damage, power stations, hospital networks to name but a few. How have Data & Analytics impacted the detection, and prevention, of cyber-crime? A company will have to protect themselves against an enormous amount of cyber threats every second. A cyber-criminal will only need one successful attempt. Data & Analytics are proving successful in the fight against cyber-crime and their proactive and holistic approach is at keeping people and businesses safe. Of course, it is Data that is being stolen, but very often Data can come to the rescue. It helps in a number of ways, e.g. identifying anomalies in employee and contractor computer usage and patterns, detecting irregularities in networks, identifies irregularities in device behaviour (a huge advantage with the rise of the IoT). What one must remember, however, is the people behind the Data. You can’t simply collect Data and assume you will be able to detect and respond with the right actions. You need the people with the right analytical skills to sift through the Data, find the right signals and then react to the threat with an appropriate and timely response. What tools and technologies do you think will become increasingly important in the fraud and cyber-crime landscape? Here at Fortalice we are investing a lot of time into coverage of the Dark Web. We live in a rapidly changing digital landscape. Criminals, fraudsters, and others are now operating with more sophistication and anonymity. Where do they go to exchange fraudulent details and ideas about current victims? What medium do they use to discuss organisational targets or new ways of defrauding companies? The answer is the Dark Web. Traditionally, companies fight fraud from the inside out. We want to change this landscape by accessing the entirety of the Dark Web, its pages, shady storefronts, and treasure troves of Data, and drawing on monitoring toolsets to give our clients a 360-degree resource for identifying adversarial communications and movements. It’s all about Internet coverage. Wherever it is difficult to find – that’s where your threat will be. A final point to this question is one of sharing tools and techniques. A collaborative approach is always a good way of making sure the wider audience benefits. We always work with our clients and offer other services and support outside of our remit to make sure they’re fully protected from a cyber and physical space. What are the biggest security threats for businesses? Security is fundamentally broken because the design of many security solutions does not design for the human psyche. Security solutions are bolted on, clunky, and hard to use but because security teams prioritise defending against easier cyber threats, they often don’t focus on the hardware side. The biggest risk to companies and individuals is always defined by the Data that is most important to you or to the business. For individuals, this might be privacy or identity. For businesses, this could be customer Data, intellectual property, and the company’s money in the bank. The reality is that business executives can’t outspend the (cybersecurity) issue and they must be prepared. Cybersecurity no longer exists in a vacuum and it must be elevated to the conversations held in the boardroom and with senior leadership as well as entire divisions, departments, and organisations. For someone trying to get into security analytics, what skills do you think are key to being successful in the industry? The detail is in the name of the role. A huge ability to interpret large amounts of technical Data is key to the role, as well as being able to assimilate what it means and how to action it. Risk management is also key to this role. Very often you will identify potential risks and you will have to triage those priorities on your own as co-workers won’t have the technical expertise to assist. You will need to be able to communicate successfully to all levels of a workforce and last but by no means least – a good sense of humour! When you think you have gotten to understand a new threat or vulnerability a new one will replace it within seconds. Time to put the kettle on, smile, and get back to work with your analytical prowess. Within fraud, it's well known that criminals are sharing their approaches, is this mirrored in cyber-security and if so, how is the industry combating this? Criminal collaboration is huge on the web. First of all, there is no talent shortage for fraud rings or cybercriminals. There are no requirements for fancy university degrees or certifications and the crime ring pays for performance. They don’t care what you look like, how you dress, or if you clock in during normal work hours. They care about getting the job done - hacking into and stealing information from others. Together they are sadly stronger and more effective. On Dark Web forums, you will see fraudsters sharing and selling their ‘IP’ knowing that others will also contribute. That way they are all winners. In the private world ideas equal money. That is of course not a bad thing for business, but it is bad for collaboration. Businesses generally don’t like to share ideas with one another because it has taken them lots of time and expense to get to their product or solution. As cliché as this comment sounds - we have to change this landscape for the greater good. There are lots of smart government initiatives for national defences in cyber security and fighting high-end cyber-crime but seldom does this have a positive impact locally with smaller businesses. There is a huge amount of information out there for individuals and advice, but we need to bridge the gap still between criminal collaboration and that of the good guys. If you could change one thing in the industry, what would it be? The mind set of security professionals that humans are the weakest link. We’re not! Humans are at risk because technology is by design, open. I’d also change the mind set of those not in the Cyber Security industry. All too often the severity of what is being reported is not taken seriously, nor are budgets set aside for cyber security issues. That said, it is improving but there is a long way to go. Ben and Danni spoke to Senior Consultant, Rosalind Madge. Get in touch with Rosalind or take a look at our latest job opportunities here.
21. March 2019
This is the second part of our interview with Derek Dempsey, Fraud Analytics Director for FICO. To read the first part click here. How have Data & Analytics impacted the detection, and prevention, of Fraud? Big Data is interesting. We want to use as much Data as possible but you have to be sure the Data you have is reliable and robust. Bad Data could lead to significant issues for anyone in Fraud detection. But really, Data & Analytics are the fuel that drives effective Fraud detection. Our world has used these tools very effectively for many years and we have always been at the forefront in the use of new techniques. Effective Fraud detection relies heavily on using the best Data available and the right analytical techniques to extract useful information. Furthermore, it is constantly evolving as more information becomes available. What impact do you think Machine Learning and AI can have on Fraud prevention and detection? In the last few years we’ve seen an explosion of new interest and new techniques, with lots of new players coming into the market. Companies like FICO have been using Machine Learning for Fraud detection for 25 years so this isn't new. What is new is a whole generation of new technologies, new companies and disruptive approaches that have been driving change. For us, apart from increased competitive pressures, one of the other big changes is that companies have built large Data Science teams and have a bigger appetite to build their own solutions using open source technologies. However, it’s one thing building these great models, it’s another getting them to operate effectively and correctly in the real world. The level of governance that we are subject to is enormous just to make sure that our models perform as they should. This will be a challenge to the newcomers. They are here to stay though, and should drive better performance and better Fraud detection. Where AI techniques are set to make an impact is in AML and compliance monitoring. These have used rule-based techniques due to regulatory pressures, but it is clear that more advanced techniques are required to provide better detection of money laundering and terrorist financing. However, businesses do require us to provide explainability but regulators are saying the will look favourably on AI usage if it can provide this. The more AI techniques are used, the more this issue of explainability is going to be important. Why is the development of analytics, tools and techniques so important within different industries? I think domain knowledge of the Data and business will always be important. 'Specialisms' occur and always will. The analytic techniques, the tools and languages can be standard although some are more appropriate for different specialities. However, the differences in Data and business model always results in specialised applications to address this. For example, something like Card Fraud or AML require techniques that can analyse and process huge volumes of transactions, whereas something like Claims fraud or Application Fraud won't have this requirement and other factors can be more relevant. However, there is little doubt that Financial Services and Telco have actively sought AI technologies while others have been more fearful of so-called 'black box' approaches. How are Big Data and Data Science tools, such as Hadoop, helping combat Fraud effectively? There’s no question from an analytics perspective that Python and R are the two languages that Data Scientists are using. But it helps to have specialists in technical skills, such as DevOps and DataOps, to provide the technical expertise that allows them to build models and utilise Data most effectively. As for Hadoop, it makes sense for Data Scientists to have an understanding, but ultimately I see that skillset as one belonging to the technical specialists. This is why I firmly believe that every Data Science team should have a supporting tech function. What are the latest technologies helping combat Fraud? From an analytical perspective there has been a recent focus on graph analytics and network analytics as these can be applied to external Data. These approaches have been around for a while however and are limited to certain types of Fraud. We’re also seeing more unsupervised techniques being used as these do not rely on prior fraud case data so can be applied to a wider set of cases. Another new area has been adaptivity. This is a model that adapts over time depending on operational feedback from the analysts. Again this is not new but you really need to balance the impact of this new information compared to how the model is currently working and so is very challenging. As long as you can maintain a sufficient degree of explainability you can ensure the process is sufficiently well governed. There has been a significant move to cloud-based technologies where companies can reduce their implementation and maintenance costs. We are just about to release our new Falcon X platform, which is a cloud-based fraud platform, that will allow clients to use all the FICO capabilities but also allow them to develop their own analytics as well. I think potentially the biggest change will be the progressive adoption of biometric authentication. SCA is a requirement for all high-value transactions in PSD2 and this requires two-factor authentication from inherence, ownership and knowledge: so something you are, something you have or something you know. I think biometrics will start to play a big role in authentication and, hopefully, will provide much greater identity security. Another trend I think we may see is the growth of digital IDs. There is already a well-advanced program in the Nordics called BankID and the concept of a digital ID seems inevitable at some point. Derek spoke to Senior Consultant, Rosalind Madge. Get in touch with Rosalind or take a look at our latest job opportunities here.
28. February 2019
Derek is a Director of Fraud Analytics for leading fraud analytics software provider, FICO. With a background in Philosophy, he is passionate about the application of advanced analytics in furthering the Fraud industry. I recently met with him to discuss the latest Fraud trends, tools and challenges for the industry. Fraud is an ever changing landscape. What trends do you anticipate for the next 12 months and beyond? It is ever-changing indeed. As other routes are shut down, we will see new Fraud attacks that reflect the changing payments landscape and the continuing shift to digital, mobile and the introduction of new players due to PSD2. For example, we’ve seen a significant shift in card Fraud following the introduction of chip and pin towards card not present or ID Theft and Application Fraud. We’re also seeing Fraudsters using AI to mount ever-more sophisticated attacks. From a Fraud Detection perspective, this has led to a convergence of Fraud teams towards a more holistic financial crime approach, as well as increasing use of AI techniques alongside requirements for greater explainability. What are the biggest risk areas for businesses to be aware of? I’d say one of the main areas is the risk of data breach through hacking and internal leaking. In terms of cyberattacks, companies may feel that they can address this fairly readily. However, they need to be vigilant as hackers have some very sophisticated techniques at their disposal. FICO have recently introduced some of our AI fraud techniques into the cybersecurity domain to combat this. However, internal leaking probably causes bigger issues. This is more often due to social engineering than a malicious internal leak but these types of breach are difficult to detect. You need an additional level of control to detect unusual, but permitted, activity and this is challenging. Financial Service organisations also need to be aware of risk areas associated with new products and services. The proliferation of mobile payments and new account and payment service providers in the new PSD2 ecosystem marks another shift in payment services and this will bring in many new players. However, new products tend to be targeted by professional fraudsters. So while we all like the greater ease and convenience, anti-Fraud measures need to keep on top of this. For aspiring Fraud-fighters, what skills do you think are key to being successful in the industry? It varies really. I got into this because I’d previously been a mathematics lecturer and then did a Master’s in AI and Cognitive Science. Fraud was, and remains, one of the best areas to apply these skills and techniques. Certainly it helps to have a Mathematical or Statistical background, but ultimately a problem-solving mindset is what really matters. The modern-day Data Scientist needs to be equipped with a range of technical skills to be effective, so it is useful to understand Big Data technologies such as Hadoop and Spark and how to interact with cloud-based services. Python and R have become the key analytical programming languages. Visualisation is also important so its useful to have skills in this as well. Soft skills are probably not the most important when it comes to Fraud Analytics but communication skills are essential - you need to work in teams and be able to ask questions and provide answers to others. Primarily, you need the ability to interrogate your Data, understand what it actually represents, understand its source and its reliability. And you need to do all this whilst keeping in mind your business objectives. It's well known that Fraudsters are sharing their approaches, so why is the industry not? There are examples of sharing in industry. In the UK we have CIFAS which is one of the leading organisations for Data sharing and this provides a great service to its affiliated businesses. We have the IFB and other organisations that are based on sharing Data. The introduction of cloud-based technologies should encourage companies to share more and FICO have invested heavily in a cloud-based strategy. The next generation of FICO's flagship Falcon fraud product, Falcon X, is a flexible cloud-based, platform solution. However, a lot of information is currently held by the commercial sector which limits how much sharing can be done. There are many companies who provide specialised Data on email addresses, devices, IPs but all of this is under a commercial umbrella and companies do need to protect assets that they have built up over many years of research. But most businesses do support sharing activity and see that that it’s to their benefit and there is definitely a willingness there. Personally, I would like to see more sharing in terms of guidance, education and awareness to customers. This is a responsibility for all of us in the sector and we need to be more proactive than just leaving information sitting on a website. Companies should be under greater pressure to provide this awareness training and, if they do, I would predict a significant reduction in certain types of fraud. There were a string of high profile Data breaches in the news last year such as Facebook and Carphone Warehouse. Do you think businesses are doing enough to protect customers? I don't think so. Obviously this something we take this very seriously and, for us, most of the Data we use is anonymised so we already minimise the risk. I know that banks are investing very heavily in this and being proactive as, more than anything, there’s a huge reputational risk to a large-scale Data breach. Other sectors probably need to do more however and all sectors can improve. Hopefully GDPR will make a big difference within the EU. There are large fines for failing to take adequate measures. Other countries are adopting similar legislation and we’re seeing non-EU businesses embrace the same guidelines as well, so hopefully that will help. If you could change one thing in the industry, what would it be? I think increasing awareness and education would make a big difference. One company I worked with mounted a big customer campaign and it made a huge impact in driving down Fraud. I think this is an underrated area. I'm aware that the FCA and other industry bodies such as IFB do produce materials but I think much more could be done. It perhaps goes without saying that I also think that increased usage, and better usage, of Data and Advanced Analytics is crucially important to reducing levels of fraud. Derek spoke to Senior Consultant, Rosalind Madge. Get in touch with Rosalind or take a look at our latest job opportunities here.
26. February 2019
Sharing and collecting data is part of our everyday lives. Whether our information is shared over social media, e-commerce sites, banks, or elsewhere, this can open up risks. 2017 saw the highest number of identity fraud cases ever, an increase in young people ‘money muling’ and higher bank account takeovers for over-60s. Whilst overall fraud incidences fell 6%, these cases highlight just some of the changing trends as fraud issues stem more from misuse than ever before. Dixons Carphone, Facebook and Ticketmaster are just three cases you may recognise from a string of high profile data breaches this year. Technological advances, more accessible and available data, coupled with an increased sophistication of fraud schemes, makes it more likely that data breaches and fraud attacks will become regular news items. But how is the fraud landscape changing and can technological advances be advantageous in detecting and reducing fraud? Identity fraud increasing for under 21s In June 2018, Dixons Carphone found an attack enabled unauthorised access to personal data from 1.2 million customers. It’s now been uncovered that the number is much higher, closer to ten times initial estimates. Whilst no financial information was directly accessed, personal data such as names, addresses and emails enable fraudsters to fake an identity. Younger fake identities are used more for product and asset purchases which typically require less stringent checks, such as mobile phone contracts and short-term loans. In 2017, Cifas, a non-profit organisation working to reduce and prevent fraud and financial crime, reported the highest number of identity fraud cases ever. Under 21s are most at risk seeing a 30% increase as they engage more with online retail accounts. Whereas previously identity theft would manifest itself in fraudulent card and bank account activity, it’s now being used to make false insurance claims and asset conversion calling for stronger detection in these industries. Young People Used as Money Mules This age group aren’t only being targeted for identity theft; there’s a 27% uplift in young people acting as money mules. ‘Money muling’ is a serious offence that carries a 14-year prison sentence in the UK. In most cases, younger people are recruited with the lure of large cash payments to facilitate movement of funds through their account, taking a cut as they go. In a world where young lives are glamourised and luxurious goods are displayed over social media, this cut can be particularly appealing. Whether aware, believing the reward outweighs the risk, or unaware a money laundering crime is being committed, deeper fraud controls are needed across social media as much as bank accounts. This raises the question as to whether banks should be linking social media to customer details to stop money laundering early on? Increased bank account takeover for over 60s Cifas also reported an increase in account takeovers for over 60s for the same period. Seen by fraudsters as a less tech-savvy and therefore more susceptible demographic, over 60s are increasingly being targeted with online and social engineering scams. The same features which can make some over 60s a target for these scams, can also mean that account takeovers are not immediately noticed and reported, posing yet another difficulty for fraud monitoring and prevention. Vigilance and proactiveness is key. Here are three tips to get you started: Never give personal or security information to someone who contacts you out of the blue, either online, on the phone, or face to face. Always phone and check with the company first. If you make the call then you know you can trust the person on the other end. Check with your bank to see if they offer an elder fraud initiative such as a monitoring service that scans for suspicious activity and alerts customers and their families or educates seniors on types of scams and how to avoid them. When in doubt about something, delay and seek a second opinion. Check with your local library, government offices, or non-profit organisation for more top tips to stay safe from scams and social engineering. Industry approach Traditionally, financial services organisations have been at the forefront of developing fraud controls; they are often the ones most impacted by the financial risk (the monetary cost of the attacks on their business) and regulatory risk (ensuring their business is adhering to regulations and controls). However, with modern day trends and the changing nature of fraud, all industries need to be focused on reputational risks and prevention. Single big events like Facebook and Dixon Carphone’s data breaches can have a far-reaching impact. But, there is light at the end of the tunnel. Monzo, an online bank, which bills itself as the future of banking has stepped up the game when it comes to their customer’s security. Upon reports of fraudulent activity on customer cards, they took immediate action to correct the problem. Then they took things a step further, introducing digital analytics to help identify trends and patterns. As patterns emerged, Monzo then notified both the breached business and the authorities. Perhaps a cross-industry collaborative approach is needed as, after all, fraudsters are collaborating. By doing so, businesses will become more proactive, rather than reactive, and can put measures in place to stop potential fraud. If you’ve got a nose for numbers and want to help secure the reputation of businesses the world over, we may have a role for you. To learn more, call our UK team at +44 020 8408 6070 or email us at firstname.lastname@example.org
09. August 2018
The UK is one of the most advanced financial services in the world and business is booming. Across industries, machine learning has boosted productivity and service, yet the financial and credit risk services lag behind. Challenger banks and non-traditional payment sources are disrupting the status quo by ushering in a new era of machine learning.For many industries, machine learning has arrived and it’s leading to big improvements in service and productivity. Though there is a growing trend to revolutionise technology in banking, many financial services organisations struggle to fill posts. It’s time to ask why and brace for financial decision-making with machine learning at its heart.In the Age of Machines – A New Era of Robotic RelationshipsTechnology disruptors within the financial services industry face a two-fold balance of keeping to compliance while at the same time creating user-friendly applications. As business trends change, decision makers are faced with new challenges such as cyber security, fraud prevention, and how best to protect their consumers. Meanwhile, working to ensure greater affordability in an industry ripe with new entrants to the marketplace with technology platforms are the fore.Virtual banking applications challenge traditional banking systems using AI and predictive analytics for a more refined scoring system. Their technology led endeavors help to automated processes and help to speed up their credit decisions for real-time decision making.Though the benefits are obvious, financial services have been hesitant to move forward until they can be sure no issues will arise. So, to combat their concerns, businesses are redefining robotic relationships. Using emergent and collective problem solving to gain insights into their customer and the services they may require now and in the future.In this new era, businesses are integrating humans, machines, and data on a large scale creating ‘social machines’. The intuitive balance and sometimes gut reactions of humans coupled with machines probability and risk to determine impact on a number of issues including, but not limited to, economics, financial, credit risk, and psychology. Machines are learning to learn.How it Works – Machine Learning FundamentalsAt its most basic level, a machine’s function is to analyse data. In machine learning, it combines data analysis and the relationships that exist within it. This allows a business to know the best product or service at the right time to the right audience. Therefore, improving the customer service, satisfaction, and interaction for confident, repeat business.However, by their nature, Risk Analysts and Credit Risk professionals are cautious and may be leery of allowing machines to make decisions on the behalf of humans. Yet, all the power to make it work is there -- to incorporate algorithms to probabilities to correct and real-time decisions in the most sensitive environments, such as banking, the direction of travel is fairly clear. In fact, a recent Accenture study, suggests 38% of banks plan to invest in machine learning extensively within the next three years. Next StepsOvercoming skepticism is a step to machine learning and realising it already exists in our everyday lives. Already, Amazon and Google have created machine learning systems to improve customer experience. And as machines process and figure out relationships via patterns and trends, services and experiences improve with it. Within credit risk, however, it’s important to include confidence and trust in data quality. Maintaining confidence is a significant challenge yet to be overcome due to the vastness of scale when it comes to customer data within the financial services industry. However, methods have evolved to combat this issue such as maintaining web infrastructure.In fact, with the advent of wireless systems, satellites, and mesh networks, web infrastructure offers greater ability to combat these challenges to build social machines which manage risk through transparency and wide engagement. In a market ripe with opportunities, we’re now faced with an ever-increasing skills shortage within Credit Risk. Want to help bridge the gap? Then check out our current vacancies here.You can also contact our UK Team at 0208 408 6070 or email email@example.com to learn more.
17. January 2018
The government has announced that UK lenders will publish the lending data for 10,000 postcodes by the end of the year.Chief secretary to the Treasury Danny Alexander has revealed that seven of the UK’s banks and building societies, which account for 80% of the current stock of lending, have agreed to release the data to identify areas where action is required to boost access to finance.The data will be published by the British Bankers’ Association (BBA) and the Council of Mortgage Lenders on a quarterly basis.It will show the outstanding stock of lending that has been committed to customers in relation to loans and overdrafts to small businesses, mortgages and unsecured personal loans.Alexander said: “From next year businesses will be able to see exactly where the major banks are lending – up to within a few streets of their premises.“It is a major step forward in terms of transparency and should encourage competition by helping smaller lenders to identify gaps in the market and allowing businesses to hold their local bank to account where they aren’t lending.”The first data set will include statistics from the Royal Bank of Scotland, Lloyds Banking Group, HSBC, Barclays, Santander UK, Nationwide, and Yorkshire and Clydesdale Banks.The government claimed that the figures will highlight the more deprived areas where banks are not willing to lend, allowing local and regional banks and lenders, such as Community Development Finance Institutions (CDFIs), to offer finance.It also expects more banks, building societies and credit unions to sign up to publishing their lending data.Ben Hughes, chief executive of the Community Development Finance Association which represents CDFIs, called it a “major breakthrough”.He added: “Having accurate and robust data will enable government, banks, other investors and the community lending sector itself, to address the significant gaps in lending that exist in our disadvantaged communities. It will bring wealth to neighborhoods that aren’t currently served by the mainstream.“Our members exist specifically to help communities that others don’t. Now we can be very clear on where those communities are and what help they need.” Click here for the article on the web.
31. July 2013
Banks are changing dramatically amid an avalanche of regulatory change and widespread debt reduction. They will be safer and, sadly for users of bank services, costlier as a result. Yet all of this may soon seem somewhat irrelevant, because technology could transform the way banking works far more profoundly.Banking is very ‘digitizable’. Cash is the only part of the industry that is inherently physical and that is a tiny part of what a bank does. The rest is really about transferring and modifying property rights and information of various sorts, all of which can be digitized.Banks are next in lineOf course banks have invested huge sums in technology – automating processes and enabling customers to bank online – but we haven’t yet seen the fundamental transformation of business models that have taken place in other sectors, such as music.It will happen, and when it does it will have a huge impact.Some of the consequences are clear from other industries. Intermediaries disappear or get marginalized unless they discover new ways of adding value.Look at what has happened to recorded music companies or bookshops. Banks are the primary intermediaries of the financial world, so their margins will fall unless they reinvent what they offer their customers and how they work.Winners take it allIn the digital analytics world, things work differently. Scale and network effects drive competitive advantage. Winners tend to take all, as Google and eBay demonstrate. Discrete products get turned into bundled services. Customers of Spotify, a music service, do not buy recordings of individual songs – they buy a subscription to a cloud-based archive.Perhaps surprisingly, the transparency of the Internet doesn’t always lead to the disaggregation of bundles and the disappearance of cross-subsidies. Things get pulled apart and put together in different ways. Monetisation, costs and customer value can be even more often disconnected than in the physical world.New business models will emerge, as we have already seen: Lending Club’s peer-to-peer model is changing personal lending. Some will thrive, many will fail.Banking will get cheaperAbove all, customers will benefit enormously. Greater transparency will mean better prices for customers. Digital analytics delivery will mean never having to go to a branch. More information and more flexible service configurations will put the customer in control.Why is it happening so slowly compared to other industries? Part of the answer lies in the banks themselves. Contrary to what many believe, banks are extremely risk-averse. They don’t like failing, and it’s almost impossible to innovate unless you are prepared to fail. In a context where trust is so important, and where there’s increasingly little tolerance for any kind of failure, that’s extremely difficult.But regulation is an even more powerful impediment – and not only because ‘financial innovation‘ is a four-letter word in banking supervision circles. Technology-driven innovation that leads to big winners and big losers, that replaces established products with flexible service bundles, that overturns established business models and blurs the boundaries of banking, and that sometimes fails to deliver quite what was intended, doesn’t fit well with today’s regulatory zeitgeist.Real innovation needs to happenTo be fair to the regulators, it’s not like banks are straining at the leash. Mostly they’re investing in technology to meet ever-increasing regulatory demands, or to reduce costs. There’s relatively little investment in real innovation that offers major changes in customer experience; and the prevailing ‘zero tolerance’ environment is toxic to new ideas.Moreover cyber-security and privacy issues are becoming ever more acute. The more finance becomes digital analytics, the more important it is to prevent intrusion, disruption and digital analytics theft.Yet, despite such challenges, and whether they like it or not, banks and their regulators are going to have to embrace technology-driven innovation. Otherwise it will simply happen by stealth, driven by players outside the industry. We have already seen examples such as M-Pesa, the mobile payments solution pioneered in Kenya, the ubiquitous Paypal, or most recently, Bitcoin – the online currency.We should be making banks betterGiven the scale of customer benefits, and the scope to seize competitive advantage, there are huge prizes for those who can innovate successfully. Too much of the debate about banking is about not repeating the mistakes of the past. We risk missing the opportunity to make banks much better in the future.We’re stepping up the pace of innovation at the bank I run: generating more ideas, implementing them more swiftly, being quicker to discard the ones that don’t work. By making everything digital analytics, exploiting the power of Big Data and the ubiquity of mobile communications, we see huge opportunities to enhance the value to our customers, to increase efficiency and to manage our risks more effectively.The upsides are huge, and the downsides are stark. That’s why accelerating technology-driven innovation is a top priority. Click here for the article on the web.
17. July 2013