By Paolo Sironi1
1Author and IBM Industry Academy member
Financial services have been in turmoil since 2008 after the default of Lehman Brothers. The most recent years were marked by corporate restructuring, consolidation of regional banks, toughening of capital requirements, more demanding compliance rules, broad simplification of financial products and, last but not least, the emergence of a vibrant FinTech ecosystem fostering digital innovation. Essentially, the cost/income ratios of established firms appear unsustainable when compared to existing and future dynamics of economic, regulatory and digital frameworks. First, interest rate margins (lending operations) don’t seem to add a significant contribution to shareholders’ value in the Western world after the price for risk. Second, global payments are under continuous digital attack by Chinese technology champions and, most recently, by the Apple partnership with Goldman Sachs and Facebook’s Libra. Third, intermediation margins based on wealth management relationships (investment and insurance products) are also suffering from a progressive compression of commissions and embedded fees, affecting capital markets in return.
Many blame digital disintermediation, others artificial intelligence or exponential technologies to be the source for disruption. I honestly believe that this is not accurate. Current commoditization of financial products is primarily a consequence of excessive asymmetries of information that underlined most of the banking trading models with clients. As a matter of fact, the 2008 banking showdown was only the tip of an iceberg whose roots sink deeply into the 1980s deregulation of financial markets and the internationalization of finance, which ignited a process called “financial innovation” (structured products, hedge funds, securitizations) and enabled market players to optimize product-driven economic models to maximize “aggressively” their profit-making with end clients: families, municipalities and small and medium-sized enterprises. The depth of the asymmetry of information was particularly evident in investment management, since end clients tend to be unaware price-takers while banks and financial advisors are undisputed price-makers.
The progressive reduction of interest rate and intermediation margins jeopardized the economic sustainability of many medium-sized transactional banks. Financial services are therefore asked to fight their game by considering two main strategies: one is about volume, the other is about value. Clearly, on one side a strategy based on accelerating volumes requires significant M&A and digitization efforts, knowing that the ultimate competition will not oppose banks to banks or banks to FinTech, but open banking platforms against big technology operators like Ant Financial, WeBank, Amazon or Facebook. Further, on the other side the strategic transformation of business models from product centricity to client centricity is a true Copernican revolution of incentives and workflows. Therefore, competing on value requires much deeper understanding of FinTech capabilities and clients’ biology to craft a suitable hybrid model that can generate differentiating value for clients and higher-than-average margins for financial firms.
Therefore, the digital difference between volume strategies and value ambitions in wealth management is not restricted to front office transformation, that is, frictionless experiences or modern marketing language. Instead, it pertains to the essence of the revenue generating mechanism. Transaction-based investment management is a product-centric model: banks collect embedded commissions and product fees when investors allocate their wealth or roll their investments. In contrast, service-driven wealth management operations require the industrialization of higher-margin relationships by packaging investment solutions into a service called “financial advice”. Advice remuneration is progressively disjointed from offered products and clients will be asked to pay for this transparently and happily. Indeed, clients themselves seem to be unprepared to identify real value and assume higher responsibility for investment decision-making.
As such, FinTech and, in particular, artificial intelligence are not the real cause of business margins disruption but an amazing opportunity for financial services to navigate these uncharted waters and reach successfully the new shore of value-based digital banking.
Given the challenges ahead, financial intermediaries seem to be largely unprepared to compete on value-generation for clients. This has resulted in a bigger emphasis on testing FinTech solutions aimed at digitizing distribution channels for investment products, thus competing on cost convenience and frictionless access to digital offers. History has already shown that only hybrid models can help the wealth management industry to move forward, also on volume-driven strategies but most importantly on value-based offers. It is therefore paramount to understand how artificial intelligence can add value into this transformation involving both humans and digital technology.
At a time when the value chain between manufacturers and distributors gets shorter every day, artificial intelligence can primarily add value (or subtract costs) according to the following major use cases:
The growth of passive investing has accompanied a progressive reduction of embedded commissions, also due to more demanding regulations like European MiFID II enforcing ex-post reporting of costs and charges. As a consequence, investment managers require higher automation of portfolio management techniques to remain sustainable by managing operational costs. In this respect, FinTech startups are making numerous attempts to infuse AI algorithms inside commoditized portfolio rebalancing techniques. The quest for products differentiation is instead favouring the emergence of AI-based signalling algorithms, aimed at stock picking, promising to trade market news and insights that could not be revealed without parsing on unformatted data with ML and deep learning. We are still at the early stages of a new wave of AI-driven quantitative methods. Yet investment officers will always remain wary of the limitations of the new approaches. AI can certainly provide an enriching perspective on market prices dynamics compared to quantitative finance (based on stochastic models and econometric techniques). However, big data is always a representation of the past, just like traditional risk management time series; since the future is open to uncertainty, even AI can fail to grasp market changes. Therefore, it is relevant to implement AI investment strategies only as part of a larger data architecture, which enables building of the required level of professional interaction and decision-making transparency that divides insightful signals from the insufficient reality of big data and small data.
Regarding process automation, back-office and middle-office activities are where the biggest cost-saving contributions can be harnessed, especially for the activities related to classification and reconciliation of market and contractual data. AI analytics are being deployed progressively to reduce manual tasks, increase time to market and provide point-in-time perspectives of business relationships and event management campaigns.
Robo-advisors have made great efforts to disintermediate human-based investment relationships, focusing on cost convenience and frictionless digital experiences. However, the main obstacle to their success has not been about their investment solutions or imperfect applications. We shall remind ourselves that investing is largely an offer-oriented industry (push economy) while mobile is largely a demand-driven technology (pull mechanism). This is due to the difficulties experienced by the majority of banking clients to become fully self-directed or digitally assisted when it comes to investing money or buying insurance contracts. Wealth management is still a human-driven business, notwithstanding the current level of technology, because of the human emotional aspects behind investment decision-making. The reasons for this gap (which explains the asymmetry of information) are grounded in the biological traits of Homo sapiens, needing relationship-based conversations when making important financial decisions in the presence of fundamental uncertainty. These biological aspects, which need to be carefully understood in the design thinking processes for digital wealth management solutions, are well described in my latest economics and philosophical essay “Financial Market Transparency” (2019).
Therefore, the push/pull gap between digital offers and human demand explains why most robo-advisors had to integrate human assistants and relationship managers to support operations, lower client attrition and make best usage of marketing money to improve onboarding success rates. Yet artificial intelligence can play a key role in making wealth management sustainable by supporting the work of financial advisors with consistent and personalized content to ease the transformation out of product-centric offers towards client-centric solutions. AI-generated content will be a critical factor to allow the reskilling efforts of banking networks, from branches hosting product brokers to centres of financial advice and planning. Whenever possible, AI will also empower end clients to become more pull-oriented (capable of understanding finance beyond push-marketing).
Finally, conversational banking is where AI will make the biggest revolutionary impact. These are the years of the chatbots, which are very basic virtual assistants to engage clients with a captivating level of conversational engagement. The real turning point is expected when AI will become truly and deeply conversational, starting to aggregate trust in e-commerce operations and rolling up into investment management relationships. Voice-powered AI conversations are the main condition which allows us to flip mobile technology from being a pull mechanism to become a push-oriented ecosystem. The time is not yet, but the foundations for this change have being already laid by the AI-driven FinTech revolution.