The most obvious is that search interest in cloud computing at its peak surpassed all of the other terms over the past decade and a half. The second is that search interest in the phrase “artificial intelligence” plunged from the data’s start in January 2004 through mid-2008 and began climbing again in 2014 as the current AI renaissance began. Searches for AI begin to really accelerate in 2017 just as searches for “deep learning” level off. This is worrisome in that it suggests that to the general public these neural advances are increasingly pulling away from their mathematical underpinnings of “deep learning” and back towards the science fiction catch-all of AI. As this transition strengthens it raises concerns that the public sees these creations as more than mere statistical equations codified in software and once again as silicon incarnations of a new form of artificial life. This raises the danger of another AI winter as the public’s soaring imagination begins to collide with the primitive reality of current advances.
To be prepared for the future of banking requires an ability to embrace the change that is upon us, a willingness to take intelligent risks and the internal commitment to disrupt yourself. The marketplace is no longer moving in incremental steps, but this means that opportunities for growth are everywhere. Unfortunately, since most bankers have a bias against risk, we overestimate the threats related to change and underestimate the rewards that change can bring — we talk ourselves out of moving forward. Most people would look at ‘the next step’ as being permanent, irreversible and not a ‘perfect’ fit. The reality is that uncertainty does not always equate to being risky. Especially if you have made a plan and done the learning process in a manner that minimizes negative consequences. In the end, you will never have complete certainty about the next great opportunity. But that is part of the fun … really. You have an amazing opportunity to invest the time and effort to be prepared for the future … and disrupt yourself.
We almost universally know now we must adapt to the digital future, to change and grow. But how best to do it remains the top question. We’ve also learned along the way there are numerous submerged obstacles to digital transformation that won’t be denied and must be overcome before we can really even get started. Sometimes, as they say, we must first go slow to go fast later. Stubborn and long-standing issues related to technology like technical debt or poor master data posture, to name just two, threaten to derail efforts before they even start. Issues related to the nature of people take up the rest, and can sometimes seem intractable. ... Consequently, in my work advising and/or leading digital transformation efforts, I’ve developed and refined four key frameworks built out of years of repeated use and validation in organizations around the world. These reflect many of the central issues that I believe we’ve learned that we must address and then codified them into a plan that most organizations can execute against.
Legacy technology is one of the major setbacks for big financial institutions. In April 2018, TSB found out just how damaging outdated infrastructures can be when they tried to migrate to a new system. The reported cost to the bank was £105.4m and 80,000 customers. However, for Panzarino, legacy technology is only part of the issue. “People talk a lot about legacy tech, but we are still dealing with legacy culture. I worked for a time in the world’s third largest bank, so it was very much steeped in legacy culture. Things moved quite slowly, it was very political, people had their own agendas… It was very difficult to navigate.” An upshot of the legacy mentality is that banks often struggle to forge meaningful partnerships with startups. “There is an opportunity for banks to learn from genuine early stage startups, and for startups to be able to run a pilot or similar, but very rarely does anything come to market. They just don’t have the financial or human resources, and the growth experience to be able to sustain themselves through the process,” says Panzarino.
Before the advent of AI in cyberattacks, the security landscape was already challenging. But the use of AI in targeted criminal attacks has made cybersecurity more treacherous. Not only are attacks more likely to be successful and personalised, but detecting the malicious piece of intelligent code and getting it out of your network is likely to be much more difficult, even with AI security in your corner. Adoption of AI by cybercriminals has led to a new era of threats that IT leaders must consider, such as hackers using AI to learn and adapt to cyberdefence tools, and the development of ways to bypass security algorithms. It won’t be long before a continuous stream of AI-powered malware is in the wild. In the short term, cybercriminals are likely to harness AI to avoid detection and maximise their success rates,” says Fraser Kyne, Europe, Middle East and Africa (EMEA) chief technology officer at Bromium. “For example, hackers are using AI to speed up polymorphic malware, causing it to constantly change its code so it can’t be identified.
The shift to a strategic rather than supporting role for IT isn't a new notion. Indeed, it has been a well-recognized trend for years and has been happening ever since the technology team's main job moved beyond keeping the mainframe and computers up and running. What's new is the pace at which this evolution is now happening and the criticality of being able to adapt the IT department and technology leadership to a higher level of strategic involvement. "You have to lead more with the technology than ever before," Le Clair said. Forrester outlined this vision in its recent report, "The Future of IT," stating: "A company's fate and fortune will be determined by its ability to exploit technology to its highest potential." Other research has reached similar conclusions. For example, in its 2018 report "Using Strategic IT for Competitive Advantage," CompTIA said: "The critical difference between today's IT and the IT of 10 or 20 years ago is the degree to which technology is being used to drive the strategic goals of a business."
Building a blockchain solution is about finding the most efficient way to solve a real-world problem, while also building a profitable and legal business. Who cares what Satoshi Nakamoto would think? For most enterprise solutions, private permissioned blockchains are the way to go. They are faster, cheaper and allow for a certain degree of centralized control. Because public blockchains are slow, it only makes sense if transparency and anonymity are at the core of the solution. It’s all about the use-case. Financial service firms, for example, rarely ever work with public blockchains, as they don’t want to share any financial data on a public blockchain. That’s why Ripple is a private network. Another consideration is the consensus mechanism. Proof-of-Work and Proof-of-Stake are the most common, but there are a lot more. Each solution comes with its benefits and disadvantages; thus, choosing the right mechanism will be paramount on the way to success.
While this new and budding industry presents opportunities and innovations, ill-supervised and underregulated industries can present sizable risks for consumers and the financial marketplace as a whole. As new players begin offering alternative banking models, they may prioritize disruption over proper risk management protocols and regulatory know-how, as several high ranking officials at the Federal Reserve have warned. Even St. Louis Fed President James Bullard noted his concern that fintech will be the “source of the next crisis.” Today, some digital financial services serve around 80 million members, while consumer data aggregators can serve more than 21 million customers, according to a report from the Treasury Department. That is a significant number of consumers served and a hefty amount of financial and personal information at risk. These services, in many cases, have proven beneficial, but Washington policymakers must act to protect consumers from devious marketplace actors by ensuring fintechs are subject to the same data security standards
One of the first actions toward evolving as a public sector CIO is stepping back and taking a critical, objective look at the current technology infrastructure and software underlying core business processes. More often than not, this means accepting that certain systems and processes that have served well for years — even decades — may yet run for decades more, just at a higher or lower volume of transactions. Most public sector CIOs don’t have the luxury of scrapping everything and starting fresh in the cloud. However there is likely significant and increasing cost and risk of maintenance on these legacy platforms. It doesn’t necessarily imply a total rip and replace of all operations, but this fresh look is likely to reveal several pieces and processes within the department that could be shored up and made more efficient for the long haul. Fortunately, if the CIO can get past their comfort zone in these systems and processes, as well as the overwhelming pressure to maintain the status quo, there is opportunity. The most immediate savings that are under the control of the public sector CIO can often be found within the existing mainframe environment.
The success of Bank+Fintech collaboration rests with those organizations who can understand each other’s strength and weaknesses to improve the customer experience while also reducing operational costs. Potentially more important will be whether these collaborations can deliver the level of personalization, speed, contextuality, and seamless delivery to defend positions against the threat of the more pronounced competition that could come from the likes of Google, Amazon, Facebook and Apple (GAFA) or challenges from Alibaba and Tencent. The good news is that infrastructure-based technology, enabled through the potential of open Application Programming Interfaces (APIs), is transforming the financial services industry. Combined with the ability to process and analyze increasing amounts of consumer data with machine learning, and the automation benefits of robotic process automation (RPA), chatbots, and Distributed Ledger Technology (DLT), there is greater potential for agility, efficiency, and accuracy.
Quote for the day:
"Every great leader can take you back to a defining moment when they decided to lead" - John Paul Warren