This week’s must-know stories in the Agentic AI, FinTech and Digital Asset space. The latest edition of the Agentic AI & The Future of Finance Newsletter is here:
Image Credits: shutterstock.com
This week’s must-know stories in the Agentic AI, FinTech and Digital Asset space. The latest edition of the Agentic AI & The Future of Finance Newsletter is here:
Image Credits: shutterstock.com
This week’s must-know stories in the Agentic AI, FinTech and Digital Asset space. The latest edition of the Agentic AI & The Future of Finance Newsletter is here:
https://mailchi.mp/bussmannadvisory.com/openai-oracle-sign-300b-cloud-deal-microsoft-taps-anthropic-ai-openai-wins-ms-backing-blackrock-eyes-tokenized-etfsImage Credits: shutterstock.com
This week’s must-know stories in the Agentic AI, FinTech and Digital Asset space. The latest edition of the Agentic AI & The Future of Finance Newsletter is here:
Image Credits: shutterstock.com
As we navigate the rapidly evolving landscape of financial technology, one development stands out as both a powerful defensive tool and a potential source of new vulnerabilities: Agentic AI. Unlike traditional AI systems that simply respond to commands, agentic AI can autonomously make decisions, learn from experiences, and take independent actions to protect financial systems.
The financial sector has always been at the forefront of cybersecurity innovation out of necessity. With financial institutions managing trillions in assets and processing billions of transactions daily, they remain prime targets for increasingly sophisticated cyber threats. A successful attack can result not only in financial losses but also in devastating reputational damage and regulatory consequences.
Traditional cybersecurity approaches in finance have relied on rule-based systems, manual monitoring, and reactive measures. While these have served as a foundation, they increasingly struggle to keep pace with the volume, velocity, and sophistication of modern cyber threats. This is where agentic AI is creating a paradigm shift in how we approach cybersecurity.
According to recent research, 75% of financial firms surveyed by the Bank of England in 2024 reported already using AI in some capacity, with cybersecurity being one of the primary applications. This adoption is accelerating, with 76% of financial organizations planning to implement agentic AI systems within the next 12 months.
Agentic AI represents a fundamental evolution from earlier AI implementations. While traditional AI agents might perform specific tasks like monitoring network traffic or flagging suspicious emails, agentic AI systems can autonomously detect threats, make decisions about how to respond, and take protective actions with minimal human intervention.
One of the most powerful applications of agentic AI in financial cybersecurity is its ability to provide continuous, real-time monitoring of systems for suspicious activities. These systems process vast amounts of data, identifying patterns and anomalies that would be impossible for human teams to detect.
For example, JPMorgan Chase, has implemented agentic AI systems that can detect unusual transaction patterns across millions of accounts simultaneously. These systems don’t just flag potential issues — they can take immediate action to prevent fraud, such as temporarily freezing suspicious transactions until they can be verified.
Agentic AI is transforming how financial institutions evaluate potential vulnerabilities across their networks and applications. These systems can continuously scan for weaknesses, prioritize responses based on severity and potential impact, and dynamically adjust security protocols in response to emerging threats.
Citigroup has established an AI governance board that actively reviews AI-driven decisions for fairness and bias mitigation, ensuring that automated risk assessments remain accurate and unbiased. This approach allows for more comprehensive security coverage while reducing the burden on human security teams.
Perhaps the most significant advantage of agentic AI in cybersecurity is its ability to move from reactive to proactive defense. By analyzing patterns and historical data, these systems can anticipate potential attack vectors and strengthen defenses before attacks occur.
Barclays has adopted a human-in-the-loop model for its AI-driven security systems, where AI predictions about potential threats are reviewed by security experts before major defensive actions are taken. This hybrid approach combines the speed and pattern recognition capabilities of AI with human judgment and contextual understanding.
Advanced financial institutions are now deploying entire ecosystems of specialized AI agents that work together to protect their systems. One AI agent might focus on threat detection, another on incident response, while a third engages in predictive analysis of potential future threats.
This collaborative approach mirrors how human security teams operate but at a scale and speed that would be impossible for human analysts alone. For instance, a leading financial institution implemented a multi-agent system with specialized components:
The result was a significant reduction in fraud losses and enhanced protection for millions of daily transactions.
While agentic AI offers powerful new defensive capabilities, it also introduces new challenges and potential vulnerabilities that financial institutions must address.
A phenomenon called “shadow AI”—the unsanctioned use of AI tools by employees within organizations—is emerging as a significant security concern. Much like shadow IT, this refers to employees using public AI models for data analysis or AI-powered coding assistants without proper vetting.
Financial institutions must be particularly vigilant about this risk, as employees might inadvertently input sensitive financial data into public AI models, potentially exposing confidential information. According to IBM addressing these risks requires “a mix of clear governance policies, comprehensive workforce training, and diligent detection and response.”
The interconnected nature of agentic AI systems introduces new vulnerabilities that cybercriminals are already attempting to exploit. As Nicole Carignan, VP of strategic cyber AI at Darktrace, points out, “multi-agent AI systems, while offering unparalleled efficiency for complex tasks, will introduce vulnerabilities such as data breaches, prompt injections, and data privacy risks.”
Financial institutions must recognize that their AI systems themselves can become targets of attacks, requiring new approaches to securing these critical components of their cybersecurity infrastructure.
As AI agents become more autonomous in their decision-making, questions about accountability and control become increasingly important. The “black box” nature of some AI systems makes it difficult to explain their decisions to regulators, customers, or internal auditors.
Paul Davis, CEO of Bank Slate, emphasizes that “human oversight is still needed to oversee inputs and review the decisioning process. You have to monitor for AI’s blind spots in areas such as risk assessment and crisis management.”
Despite these challenges, financial institutions can take specific steps to harness the power of agentic AI while building robust cyber resilience.
Financial institutions leading in this space have established comprehensive governance frameworks for their AI systems. JPMorgan Chase and HSBC have appointed Chief AI Risk Officers to oversee responsible AI usage, while Citigroup’s AI governance board actively reviews AI-driven decisions.
These governance structures ensure that while AI systems can operate autonomously, proper oversight mechanisms, accountability frameworks, and transparency requirements are in place. This approach aligns with the EU AI Act, which categorizes AI systems into different risk levels and establishes governance requirements accordingly.
The most effective implementations of agentic AI in financial cybersecurity maintain a balance between automation and human oversight. Barclays’ approach of keeping humans involved in reviewing AI-generated security recommendations before major actions are taken represents a thoughtful middle ground.
The most resilient cybersecurity systems combine the strengths of both AI and human intelligence in a continuous learning loop. AI systems detect patterns and anomalies at scale, while human experts provide context, judgment, and strategic direction.
This hybrid approach allows financial institutions to respond to emerging threats more effectively than either AI or human teams could accomplish alone. As threats evolve, both the AI systems and human teams learn and adapt together, creating a continuously improving security posture.
Looking ahead to 2026 and beyond, several trends will shape how agentic AI continues to transform cybersecurity in financial services.
We’re seeing a clear trend away from simple chatbot interfaces towards more sophisticated, autonomous AI agents in security operations. These agents will be capable of not just detecting threats but also responding to them in real-time, often without human intervention.
This shift will raise important questions about accountability and control that financial institutions must address proactively. As these AI agents become more autonomous, ensuring their decision-making processes are transparent, auditable, and aligned with organizational policies will be essential.
By 2027, at least 80% of developers in financial organizations will be using AI-powered coding tools in some capacity. While these tools can significantly speed up development and help identify bugs, they also introduce new security considerations.
Software developers will need to be vigilant about potential biases or errors introduced by AI coding assistants, as well as the possibility of cyber attacks targeting these AI systems themselves. Implementing a “trust and verify” approach to AI-generated code will be critical for maintaining security.
As agentic AI becomes more prevalent in financial cybersecurity, regulatory frameworks will continue to evolve. The EU AI Act represents just the beginning of what will likely be a comprehensive regulatory approach to AI in financial services.
Financial institutions should prepare for increased scrutiny of their AI systems, particularly those used for cybersecurity. Demonstrating responsible AI usage, maintaining appropriate human oversight, and ensuring transparency in AI decision-making will be key to regulatory compliance.
Agentic AI represents both the next frontier in cybersecurity defense and a new domain of potential vulnerability for financial institutions. Its ability to autonomously detect threats, make decisions, and take protective actions offers unprecedented capabilities for defending against increasingly sophisticated cyber attacks.
However, realizing these benefits requires thoughtful implementation, robust governance, and a balanced approach that combines the strengths of AI and human expertise. Financial institutions that get this balance right will not only enhance their security posture but also build greater trust with customers and regulators.
As we prepare for the Point Zero Forum rum 2025, cybersecurity and the role of agentic AI will undoubtedly be central to our discussions about the future of financial services. The forum’s focus on establishing resilient policies, infrastructure, and innovation aligns perfectly with the cybersecurity challenges and opportunities presented by agentic AI.
Remember that building cyber resilience is not a destination but a journey—one that requires continuous adaptation, learning, and collaboration across the financial ecosystem. As I often say, “We are at the beginning of a marathon. It’s not a sprint.” The most successful institutions will be those that approach agentic AI in cybersecurity with both enthusiasm for its potential and thoughtfulness about its implementation.
I look forward to continuing this conversation at the Point Zero Forum in Zurich and exploring how we can collectively harness the power of agentic AI to build a more secure and resilient financial system.
Oliver Bussmann is a global technology thought leader and ambassador to the Point Zero Forum. With extensive experience as a former Group CIO at UBS and SAP, he advises financial institutions on digital transformation strategies and emerging technologies.
Please note that this newsletter reflects Bussmann Advisory’s and Oliver Bussmann’s personal views and not those of any organization we are involved with. This newsletter is for educational purposes only and none of its content should be construed as investment or financial advice of any kind. More information on www.bussmannadvisory.com.
Image Credits: OpenAI
As we approach the Point Zero Forum 2025 in Zurich, I find myself reflecting on how agentic AI is fundamentally reshaping the financial services landscape. This isn’t just another incremental technological advancement – it represents a paradigm shift that’s transforming how financial institutions operate, serve customers, and manage risk.
The financial sector has entered a new phase in its AI journey: from passive assistance to active agency. While traditional AI systems have operated within predefined constraints—retrieving data, summarizing reports, streamlining workflows—agentic AI moves beyond these functions to plan, execute, and adapt complex tasks with minimal human intervention.
What distinguishes agentic AI is its powerful combination of autonomy, adaptability, and coordination capabilities. These systems can make independent decisions, learn from feedback loops, and interact with other AI agents to execute comprehensive workflows. The global market for agentic AI in financial services is projected to grow at an impressive rate of over 40% annually, potentially reaching $80 billion by 2034.
According to Citi’s latest research, references to agentic AI by BigTech in corporate documents and press articles increased 17x in 2024 and are expected to go parabolic in 2025. This signals a significant shift in the industry’s focus and investment priorities.
As financial institutions face increasing pressure to optimize operations, reduce costs, and deliver personalized services at scale, agentic AI offers a powerful solution for automating processes while maintaining accuracy and compliance.
The practical applications of agentic AI across financial services are diverse and already delivering tangible results:
Investment firms like JPMorgan Chase are deploying AI agents to autonomously monitor markets, detect non-obvious correlations, and optimize portfolio allocations. These systems provide adaptive financial advice, real-time savings goal optimization, and personalized investment portfolios.
HSBC’s “Amy” has moved beyond simple customer service to provide more nuanced financial assistance.
For retail customers, virtual financial assistants and tax planning agents are becoming increasingly sophisticated, with Capital One ranking second in “AI maturity” according to Evident AI’s 2024 index, demonstrating how traditional banks are embracing this technology.
In corporate banking, agentic AI enables custom lending offers, optimized loan structures, and dynamic pricing models. Financial planning agents and adaptive tax planning systems help institutional clients navigate complex financial landscapes.
Royal Bank of Canada, is leveraging agentic AI to provide custom research insights and real-time market alerts to institutional investors, giving them a competitive edge in fast-moving markets.
Perhaps most critically, agentic AI is transforming risk management and compliance. Self-learning systems continuously refine fraud detection strategies, identifying new fraud techniques as they emerge. These systems can autonomously assess loans, using local data to evaluate risk without direct human involvement.
Citigroup has established an AI governance board that actively reviews AI-driven decisions for fairness and bias mitigation, while Barclays has adopted a human-in-the-loop model for AI-driven loan approvals to maintain compliance with regulatory standards.
In compliance, agentic AI refines risk assessments in real-time, dynamically responding to emerging threats and anomalies. This capability is particularly valuable in an era of rapidly evolving regulatory requirements and sophisticated financial crimes.
Behind the scenes, agentic AI systems automate routine tasks with context-aware workflows, streamline complex operations, and handle invoice processing and reconciliations. The technology leverages advanced language models to analyze situations, determine appropriate actions, learn from outcomes, execute complex processes, and adapt strategies based on changing conditions in real-time.
Fintech companies like Covecta have demonstrated how agentic AI can handle lending and credit underwriting autonomously, reducing processing times by 80%. Meanwhile, digital-first banks like Revolut and Nubank are experimenting with fully AI-driven operational models, setting the stage for a new banking framework.
The financial services industry is embracing agentic AI at an unprecedented rate. According to a Bank of England survey in 2024, 75% of financial firms reported already using AI, with an additional 10% planning to adopt it within the next three years.
A recent SS&C Blue Prism survey revealed even more ambitious adoption plans, with 87% of organizations actively deploying new AI technologies, 94% considering AI core to their entire business operations, and 76% planning to implement agentic AI systems within 12 months.
However, this rapid adoption isn’t without challenges. The same survey found that 74% of respondents face difficulties in adopting the latest AI technology, with around one-third citing security and compliance concerns, 36% concerned about employee skills, 34% worried about employee fear of losing jobs, and 33% facing technology integration requirements.
Moreover, some reports suggest that as high as 85% of AI initiatives fail, underscoring the gap between AI’s promise and its practical application in enterprise environments.
As agentic AI adoption accelerates, regulatory frameworks are evolving to address the unique challenges these systems present. The EU AI Act represents a significant step in this direction, categorizing AI systems into different risk levels and establishing governance requirements.
Agentic AI’s autonomy and potential to operate with minimal human intervention raise unique regulatory challenges. The EU approach focuses on ensuring that while these systems can operate autonomously, proper oversight mechanisms, accountability frameworks, and transparency requirements are in place.
Financial institutions are responding by establishing robust AI governance structures. JPMorgan Chase and HSBC have appointed Chief AI Risk Officers to oversee responsible AI usage, while Citigroup’s AI governance board actively reviews AI-driven decisions for fairness and bias mitigation.
The upcoming Point Zero Forum in Zurich (May 5-7, 2025) will serve as a critical platform for discussing these developments and their implications. As an ambassador to this prestigious event, I’m particularly excited about the dialogue that will unfold around agentic AI and other transformative technologies.
The Forum will bring together over 2,000 of the world’s leading policymakers, central bankers, regulators, and industry experts to tackle pressing challenges in the financial ecosystem. One of the key questions guiding the 2025 dialogue will be: “Will Agentic AI-driven intelligent systems redefine industrial productivity and unlock new frontiers of innovation?”
The Forum will address two primary themes:
While the potential of agentic AI is immense, significant challenges remain. Financial institutions must navigate concerns around trust, data privacy, cybersecurity, and regulatory compliance. The technology raises questions about job displacement and algorithmic bias that must be addressed thoughtfully.
From my perspective, having worked at the intersection of technology and finance for decades, I see three critical success factors for organizations looking to harness agentic AI:
We stand at the threshold of a new era in financial services, one in which agentic AI will fundamentally reshape how institutions operate, serve customers, and manage risk. The technology’s ability to autonomously make decisions, learn from experiences, and collaborate across systems represents a quantum leap from traditional automation.
Leading financial institutions are already demonstrating the transformative potential of agentic AI, from JPMorgan Chase’s market monitoring systems to Citigroup’s governance frameworks. The rapid adoption rates—with 76% of financial organizations planning to implement agentic AI within a year—underscore the industry’s recognition of this technology’s strategic importance.
As we prepare for the 2025 Point Zero Forum, I encourage financial leaders to consider how this technology can be harnessed responsibly to drive innovation, efficiency, and inclusion. The forum’s focus on AI-driven intelligent systems and their potential to redefine productivity aligns perfectly with the agentic AI revolution unfolding in financial services.
Remember, as I often say, “We are at the beginning of a marathon. It’s not a sprint.” The journey toward fully realizing the potential of agentic AI will require sustained commitment, thoughtful leadership, and collaborative approaches across the industry.
I look forward to continuing this conversation at the Point Zero Forum in Zurich and engaging with many of you on this fascinating topic.
Oliver Bussmann is a global technology thought leader and ambassador to the Point Zero Forum. With extensive experience as a former Group CIO at UBS and SAP, he advises financial institutions on digital transformation strategies and emerging technologies.
Image Credit: Wanan Wanan | shutterstock.com
Please note that this newsletter reflects Bussmann Advisory’s and Oliver Bussmann’s personal views and not those of any organization we are involved with. This newsletter is for educational purposes only and none of its content should be construed as investment or financial advice of any kind. More information on www.bussmannadvisory.com.
For the past months, few industries have been riding the Artificial Intelligence bullet like financial services. Whether it’s Wall Street or High Street – most of the big names in banking have launched various attempts at harvesting the promises of deep learning, language processing or reasoning algorithms. Some with recognizable success stories in the likes of automating legal work or quantitative trading, others overselling the introduction of merely rule-based systems like robo-advisors or process automation as machine intelligence.
Huge expectations
As of today, there are hundreds of vendors and consultants selling AI into financial services. More and more Fintech players also claim to use some form of Machine Learning, seen as a quality stamp helping to sell their applications into the financial industry. While this trend ups the pressure to rethink the value proposition of many products and services, it adds a whole new level of complexity and lock-in risk for traditional banks. Given the immaturity of many vendor solutions, they will almost exclusively rely on heavy training with banks’ data. What’s also seldom mentioned is that AI solutions are far from finished products, with a long path to readiness for integration and deployment in a large enterprise context. Moreover, there is a noticeable push of vendors that traditionally dealt with banks’ IT departments towards marketing their tools directly into the front office. Selling whatever buzzword gets their attention may make bankers fall in love with AI tools and speed up the their traditionally slow buying cycle. But buying technology for the sake of having technology typically won’t do the trick. Many business functions tend to start searching reasons to implement a certain tool; often without a clear concept of which client problem to solve, nor sufficient judgment of the effort needed to train algorithms or integrate a tool into existing IT architecture.
There is one theme that banks seem to have unofficially declared their favourite AI application: Chatbots. From San Francisco to New York, from London to Oslo and from Singapore to Shanghai – there are already various implementations of text-based chatbots answering client questions to more ambitious virtual assistants executing tasks like transferring money or scheduling advisor meetings. Add to that the first applications for devices like Alexa or Google Home, an even more challenging discipline given restriction to voice control plus unresolved data secrecy and authentication issues from their heavy reliance on cloud technology.
First learning curve
What most conversational agents have in common however is that their current user experience is mediocre at most. The vast majority are nothing more than dumb Q&A bots. Yes, Natural Language Processing is still the most challenging discipline in AI. And yes, users do give you a novelty bonus for the time being – after all we are still in the age of narrow AI. Currently most bots are capable of little more than linear, single-turn conversations. Many struggle with contextual background, let alone switching context during conversations. Navigating between content levels or understanding the status of a request is difficult. So is building shared context, which would make for a true dialog. With the memory of a certain Disney fish, and often helpless at facing sarcasm or fragments of sentences and words, today’s bots are far from enabling natural conversations. Numerous banks find themselves having to ramp up expert resources that spend their days scripting ever new contents into digestible answers. Many are genuinely surprised at the amount of training data needed to feed a bot with domain knowledge, the effort of getting even a single user intent right, and the lower-than-expected rates of correct intent detection. Add to this the challenge of generative replies and inferring new facts from user content, and it’s plain to see why many first generation chatbots have been shut down after only weeks in operation or trial. Humans have a habit of asking complicated questions, and humans tend to be annoyed quickly.
While bots hold the promise of easier, increased and more seamless interactions with clients, it will only be kept if the bank actually solves their most pressing needs. Don’t get me wrong, I’m all for innovation in financial services. But within reason. We are near the peak of inflated expectations and many banks seem unconscious of the deep trough likely to follow. It’s easy to fall victim to a hype, but when your own tech maturity speaks for starting with easier machine learning on structured data, it’s less smart to attempt automated client conversations first. It is essential to think through processes to the end – a conversation ending with a forced branch visit or waiting for physical mail will still be considered broken.
Challenges
Multi-turn, multi-intent, multi-language, natural conversations are currently wishful thinking and still a thought for tomorrow. In the meantime, it’s worth considering whether the time is ripe for facing clients with automated chats today. This cannot be taken lightly. It is essential to gain experience with user behaviour and establish a viable strategy on how to tackle conversational commerce. Determine preferred channels, interfaces and ways to structure your data sources. Select your vendor carefully and get a reference from its existing clients. Don’t outsource this decision or overload yourself with unrealistic ambitions or complexity from the beginning. Give the bot a frame on what it can say and what statements may be problematic due to their legally binding nature. Start trials with internal users and work your way towards clients. Define minimum thresholds for quality KPIs and measure them. Learn to deal with emotional responsiveness and what makes for a convenient conversation. Be transparent about the fact that users talk to a machine, make clear what it can and cannot do. Give your bot a recognizable, likeable, but neutral persona. Think through how to deal with data secrecy. Determine below what probability of generating the right reply the conversation is handed off to humans, and don’t forget to learn from your service centre’s written replies. Run analytics on conversations and monitor how users’ needs and behaviours change.
As plain as it seems, an industry built on trust cannot afford to jeopardize user centricity.
By Antony Jenkins and Oliver Bussmann
This blog post includes the position paper on banking models.
It’s hardly a secret that the winds of change have been howling through the financial services industry. From post-crisis regulation to the Fintech revolution to the emergence of disruptive technologies like blockchain, there is probably no subject more hotly debated in the industry than its future.
It’s good that banks are taking these changes – and their attendant threats – seriously. They are researching, considering, and examining what to do. Yet while we see a focus on innovation, there seems to be a marked reluctance from some bank executives to recognise the degree of transformation required.
To some extent, this is understandable. There is an unfathomable amount of change happening at the moment, especially on the technology front, making it difficult to keep up. The degree of change that is being talked about – not just adjustments but profound rethinkings – can seem daunting too, making it hard to know how to react.
The prospect of the consequences can be intimidating. Banks are complex, often mature institutions that have already made significant investments in expensive technologies and processes. It can be difficult to accept the thought of abandoning them, as well as certain businesses, for the unknown.
We can sympathise. Both Antony, as the former CEO of Barclays, and Oliver, as the former Group CIO of UBS, know very well what it means to be on the inside of a global bank facing the gale force winds of transformation.
Having both now left these institutions for the front lines of this new, emerging world – Antony as CEO of 10x Future Technologies and Oliver as Founder and Managing Partner of Bussmann Advisory – we think we have a good perspective on what is in store.
That is why we are concerned that our old banking colleagues may not have the right sense of urgency.
Let us make no mistake: for banks the time for research and deliberation is over. As the financial services sector grapples with its Uber moment, so banks may soon face their Kodak moment – a rapid diminution in the relevance of banks to their customers as technology provides the means for others to offer a radically superior experience. The time to act is now.
In this short paper, we try to explain why. We summarise the situation facing banks today, examine its causes, and suggest what we think needs to be done – bringing the perspectives we have gained with our experiences on both sides.
New banking models
We are convinced that the banking business model will be greatly disrupted over the next five to ten years as the result of a complete re-architecting of the underlying market infrastructure. We are already seeing the end of the first stage of this process, in which apps and contactless technology have led to enormous changes in how we use bank branches and cash. This is nothing, however, compared to what is coming. We believe we will soon see a new, unprecedented wave of change influenced by a number of factors, including:
Open for new partners
The opening of the financial services industry will present a completely new world for banks.
For one, this will mean getting used to different kinds of partnerships. Banks have traditionally been closed shops, designing, building and maintaining their systems themselves. While this worked in the past, it does not work in an age of highly complex, interconnected and rapidly changing technology.
In place of the standalone approaches of the past, banks will need to function as parts of larger ecosystems, joining networks of partnerships with FinTechs and other providers in various areas of their business. While challenging on the one hand, these partnerships can also help banks assemble best-in-class capabilities to create innovation and transformation at the speed and scale they will need, helping them stay competitive.
These open ecosystems will also create a new world for consumers. We will see this perhaps most dramatically with customer data, which will increasingly come under the control of customers themselves. With more say over how their data is used and which institutions they share it with, customer relationships will be far less sticky than they are today. The new freedoms customers enjoy with their data will enable them to seek more personalised advice and services from a wider set of providers. It could even conceivably be a source of income: in a world where personal data is a valuable commodity, customers may be able to request payment for its use.
Storm clouds of the 21st century
As financial services are disrupted, there will be no shortage of issues to overcome. Consider, for instance, the changes being wrought by PSD2. Here banks will face significant hurdles in areas like cybersecurity, enabling the integration and then onboarding of third parties, testing, and training. We can expect similar challenges in other areas of the banking business as the market transforms.
While this may seem like a lot of storm clouds on the horizon, banks should focus on the many silver linings. To return to the PSD2 example, banks that focus simply on doing what is necessary from a compliance perspective risk missing new opportunities. Those that take a broader view have a real chance to build a better customer experience, and with it new opportunities for revenues.
Banks should also be careful not to let the gathering storm clouds obscure their vision. Looking inward, they must be wary of an excessively risk-averse culture, which can lead them to move too cautiously. Looking outward, banks will want to be sure they don’t overlook where the real competition is coming from, and get blindsided.
To get an idea of the form such competition might take, consider what happens on our smartphones. Based on our behaviour, location and other factors, platforms like Google are already able to predict the next apps or services we may want to use, or information we may want to have. In the future, these platforms will be able to look at our financial preferences, consolidating our account balances, spending patterns and other information to provide us with highly personalised recommendations to help us manage our money and work towards achieving our life goals.
In other words, the financial advisor of the future doesn’t have to be a bank. It can be a machine, and not necessarily one that’s owned by a financial institution.
Facing new realities
So what do today’s banks need to be thinking about in the face of these new realities?
For one, banks will need to innovate beyond banking to reimagine the customer experience. That will mean taking a radical approach to reinvention. The current incremental approach to change and innovation will not be enough to survive in the future, let alone thrive. Nothing short of transformation is required. For this level of transformation to work, banks need to think beyond solving today’s problems. Instead, they must anticipate the needs and problems of tomorrow and actively help to shape a future that meets them.
In the real-time, connected world that will be enabled by such technologies as the Internet of Things and smart contracts, financial services will be increasingly embedded in the value chains of other industries. Banks need to understand what that means for them. They will also need a better understanding of the data in their possession, as data will be the oxygen that will feed the transformation and reinvention of financial services. The good news is that banks already have a wealth of data about their customer’s needs, preferences and behaviours. The bad news is that it resides in fragmented, closed and ageing systems, which prohibits them from aggregating and optimising it to offer better banking experiences. Those banks that can bridge their internal data silos will have a significant competitive advantage.
In the future banking marketplace, trust will become a key differentiator. We believe the definition of trust itself will change due to profound shifts brought about by the disintermediation of financial services and the adoption of distributed ledger technologies. If, as we maintain, customers will in future own and manage their own accounts and data, then the old question of whom I can trust with my money will be replaced by the new question of whom I can trust with my data. Those banks that can win trust will win business – though they should keep in mind that, once trust is given, customers will expect significant value in return.
That means banks will need to lead with the right values, particularly in the sometimes fraught worlds of digital data, privacy and cybersecurity. In these areas, customers will settle for nothing less than the highest standards.
Banking’s big moment
So what should banks be doing?
For one, banks will have to accelerate their innovation efforts while at the same time considering how to create transformation. That means breaking out of a ‘reactionary’ approach and mindset, breaking free from the burden of legacy infrastructures, and pursuing continuous instead of incremental innovation – among other things by learning from the dynamic, rapid culture of today’s new digital companies.
Doing so will most likely mean partnering with startups, FinTechs and other e-commerce players to accelerate change, grow new revenue opportunities and so achieve competitive advantage. This means adopting a Business Development 2.0 approach and embracing the FinTech ecosystem with an end-to-end orchestration – from setting the agenda to ideation to proof-of-concepts to go-live. 10x Future Technologies is a platform designed to enable such transformation, and can serve as an example. In a sector plagued by legacy technology, which prevents incumbents from reacting nimbly to technological threats, we believe the best platforms can only be designed from the bottom up, with the bank’s precise requirements and future-proof adaptability baked in from the outset. In doing so, banks can build significantly improved customer experiences at dramatically lower operating costs and with full transparency for bank management.
Last, but certainly not least, banks should be aware of the new perspectives all this change will bring. We think it is perfectly possible for banks to seize the opportunity presented by the Uber moment they are experiencing today, while avoiding the massive destruction of shareholder value that would result from a series of Kodak moments.
While it will require leadership and courage to provide the requisite focus on transformation, we believe there has never been a more exciting moment in banking, for those prepared to be bold.
Antony Jenkins is CEO of 10x Future Technologies and the former Group CEO of Barclays
Oliver Bussmann is Founder and Managing Partner of Bussmann Advisory and ex-Group CIO of UBS and SAP
About 10x and Bussmann Advisory
10x Future Technologies reflects today’s changes in infrastructure and business models by providing a holistic solution for banks to address their current challenges. 10x’s future-proof core banking platform will empower banks and non-banks to optimise their customer data and interactions in order to offer new innovative and compelling customer experiences in a secure and trusted way. This will put power back into the hands of the consumer and society and generate new revenue opportunities and models for banks.
Bussmann Advisory helps C-suite executives and decision makers in global enterprises stay ahead of the digital disruption curve. With a client base covering top-tier banks, global consultancies and other firms facing disruption, as well as strong connections in the global Fintech community, the Bussmann Advisory team is close to the pulse of the rapid changes facing industry. It provides thought leadership and advisory services above all in digital transformation, innovation orchestration, and business model re-creation.
In a previous post I wrote about my belief that the world was ultimately moving to large-scale, public, decentralized technology models, and these would give rise to global, public, decentralized platforms for enterprises.
The impetus for that post was the announcement of the Enterprise Ethereum project, and my focus was on blockchain and the debate around permissionless or permissioned ledgers.
Blockchain, however, is only part of the picture. Today we are seeing a grand convergence of several technology mega trends that, working together, will make these future platforms extremely smart, fully autonomous, hyper-connected, fully decentralized, and very broad-based.
While it is the technologists who are building these platforms, it will fall to business decision makers to figure out how best to use them commercially. Certain industries are racing ahead in thinking about the radically new business models this future will bring. Others – including financial services – seem to me to be lagging.
I think that’s a mistake, as I intend to discuss in a later post. Here I would like to look at this convergence in some detail, as I think enterprises really need to understand the new environment they will eventually being doing business in.
Today, as people have recognized when for example talking about the fourth industrial revolution, we have at our disposal the various technological ingredients needed for radical automation and radical decentralization.
Most prominent among these, at least in a commercial context, are artificial intelligence (including, but not limited to, machine learning), big data, the Internet of Things (IoT), and edge computing.
Advances in each of these fields represent extremely interesting new technological capabilities in themselves. But to be truly useful for platform building, they need to work in tandem. That’s because they have a number of dependencies.
For example, thanks to artificial intelligence we can teach computers to think for themselves and make autonomous decisions orders of magnitude faster and, at some point, orders of magnitude better than we can.
But thinking machines first need to be educated – either by being fed a steady stream of information so they can learn on their own, or by being given robust enough models of the world to allow them to make intelligent choices without our help. The prerequisite for this is having enough information around in digital form with which to train our machines. This was impossible before big data.
Once our machines can “think”, we will want to “do”. To drive true large-scale automation, our AI decision makers will need to manipulate real-world devices outside of themselves. But this only works on devices that can receive messages, understand what they are being asked to do, and autonomously carry out their instructions. This was not possible before the IoT. And, as we are learning, for IoT-enabled devices to be able to react quickly, and so be useful in a decentralized world, they will not be able to wait for data and instruction from the cloud. Hence the current interest in developing edge computing, in which data and computation takes place on the devices themselves (the “edge” of the network) and not in central nodes. This prediction was described by Peter Levine, a partner at Andreessen Horowitz in his talk “Return to the Edge and The End of Cloud Computing”. In following video, Peter discusses the pressures that our pushing toward edge computing and away from the cloud:
Last but not least, no decentralized platform can be built if the nodes on the network, whether machine or human, can’t easily, securely, autonomously, transparently, traceably and quickly share data. Where can we look for a technology to allow them to do this? To the blockchain or other distributed ledgers, of course. For this reason, I think blockchain will play a key role in the coming convergence, as the communications, trust and auditing hub. But it is only a part of the picture.
There is no doubt that the radical decentralization and automation this will enable will have a radical effect on business models too. The new environment will just be too different for business as usual.
I expect that, thanks to far greater integration of value chains or between businesses and customers, business verticals will blur. The silos between industries will also come down.
Enterprise decision-makers will need to keep this in mind. In subsequent posts I will lay out in more detail how I think these new models will look, and how in my experience some industries seem to be doing a better job than others in preparing for the decentralized future.
There is no doubt that 2016 has been a tumultuous year.
From Brexit to Trump and the Ukraine to Syria, we have seen many upheavals on the geopolitical front. A lot has happened in Fintech, too, although here the upheaval has in my opinion been almost all positive. Today FinTech is firmly established as one of the biggest sectors in all technology.
What will next year bring?
We can look forward to more tumult I think. If there is one overarching FinTech trend, I would say that several things that were only “potentials” in 2016 will become much more concrete. That could make 2017 the “year of getting real” on a number of fronts.
Here are some of my thoughts.
Along with the above “predictions”, here are some of the trends I think worth keeping an eye on in the coming year.
Finally, 2016 was a very big year for me personally.
2016 was also the year of the launch of Bussmann Advisory, with the goal of helping companies stay ahead of the digital disruption curve.
The company has gotten off to excellent start, better than I could have imagined. For that I am grateful, to my new clients and all those who have collaborated with me and supported this move.
With that, I would like to wish everyone the best of the season and a happy and healthy new year. It promises to be an interesting one.