Good article from McKinsey on the revolution catalysed by the combination of machine learning and new payment systems as part of big data. The outline some of the opportunities to expand the use of machine learning in payments range from using Web-sourced data to more accurately predict borrower delinquency to using virtual assistants to improve customer service.
Machine learning is one of many tools in the advanced analytics toolbox, one with a long history in the worlds of academia and supercomputing. Recent developments, however, are opening the doors to its broad-scale applicability. Companies, institutions, and governments now capture vast amounts of data as consumer interactions and transactions increasingly go digital. At the same time, high-performance computing is becoming more affordable and widely accessible. Together, these factors are having a powerful impact on workforce automation. McKinsey Global Institute estimates that by 2030 47 percent of the US workforce will be automated.
Payments providers are already familiar with machine learning, primarily as it pertains to credit card transaction monitoring, where learning algorithms play important roles in near real-time authorization of transactions. Given today’s rapid growth of data capture and affordable high-performance computing, McKinsey sees many near- and long-term opportunities to expand the use of machine learning in payments. These include everything from using Web-sourced data to more accurately predict borrower delinquency to using virtual assistants to improve customer service performance.
Machine learning: Major opportunities in payments
Rapid growth in the availability of big data and advanced analytics, including machine learning, will have a significant impact on virtually every part of the economy, including financial services (exhibit). Machine learning can be especially effective in cases involving large dynamic data sets, such as those that track consumer behavior. When behaviors change, it can detect subtle shifts in the underlying data, and then revise algorithms accordingly. Machine learning can even identify data anomalies and treat them as directed, thereby significantly improving predictability. These unique capabilities make it relevant for a broad range of payments applications.
What is machine learning?
Machine learning is the area of computer science that uses large-scale data analytics to create dynamic, predictive computer models. Powerful computers are programmed to analyze massive data sets in an attempt to identify certain patterns, and then use those patterns to create predictive algorithms (exhibit). Machine learning programs can also be designed to dynamically update predictive models whenever changes occur in the underlying data sources. Because machine learning can extract information from exceptionally large data sets, recognize both anomalies and patterns within them, and adjust to changes in the source data, its predictive power is superior to that of classical methods.
An excellent article from McKinsey (I may be biased, but analytics used right are very very powerful!).
More than 90 percent of the top 50 banks around the world are using advanced analytics. Most are having one-off successes but can’t scale up. Nonetheless, some leaders are emerging. Such banks invest in talent through graduate programs. They partner with firms that specialize in analytics and have committed themselves to making strategic investments to bolster their analytics capabilities. Within a couple of years, these leaders may be able develop a critical advantage. Where they go, others must follow—and the sooner the better because success will come, more than anything else, from real-world experience.
By establishing analytics as a true business discipline, banks can grasp the enormous potential. Consider three recent examples of the power of analytics in banking:
To counter a shrinking customer base, a European bank tried a number of retention techniques focusing on inactive customers, but without significant results. Then it turned to machine-learning algorithms that predict which currently active customers are likely to reduce their business with the bank. This new understanding gave rise to a targeted campaign that reduced churn by 15 percent.
A US bank used machine learning to study the discounts its private bankers were offering to customers. Bankers claimed that they offered them only to valuable ones and more than made up for them with other, high-margin business. The analytics showed something different: patterns of unnecessary discounts that could easily be corrected. After the unit adopted the changes, revenues rose by 8 percent within a few months.
A top consumer bank in Asia enjoyed a large market share but lagged behind its competitors in products per customer. It used advanced analytics to explore several sets of big data: customer demographics and key characteristics, products held, credit-card statements, transaction and point-of-sale data, online and mobile transfers and payments, and credit-bureau data. The bank discovered unsuspected similarities that allowed it to define 15,000 microsegments in its customer base. It then built a next-product-to-buy model that increased the likelihood to buy three times over.
Results like these are the good news about analytics. But they are also the bad news. While many such projects generate eye-popping returns on investment, banks find it difficult to scale them up; the financial impact from even several great analytics efforts is often insignificant for the enterprise P&L. Some executives are even concluding that while analytics may be a welcome addition to certain activities, the difficulties in scaling it up mean that, at best, it will be only a sideline to the traditional businesses of financing, investments, and transactions and payments.
In our view, that’s shortsighted. Analytics can involve much more than just a set of discrete projects. If banks put their considerable strategic and organizational muscle into analytics, it can and should become a true business discipline. Business leaders today may only faintly remember what banking was like before marketing and sales, for example, became a business discipline, sometime in the 1970s. They can more easily recall the days when information technology was just six guys in the basement with an IBM mainframe. A look around banks today—at all the businesses and processes powered by extraordinary IT—is a strong reminder of the way a new discipline can radically reshape the old patterns of work. Analytics has that potential.
Tactically, we see banks making unforced errors such as these:
not quantifying the potential of analytics at a detailed level
not engaging business leaders early and to develop models that really solve their problems and that they trust and will use—not a “black box”
falling into the “pilot trap”: continually trying new experiments but not following through by fully industrializing and adopting them
investing too much up front in data infrastructure and data quality, without a clear view of the planned use or the expected returns
not seeking cooperation from businesses that protect rather than share their data
undershooting the potential—some banks just put a technical infrastructure in place and hire some data scientists, and then execute analytics on a project-by-project basis
not asking the right questions, so algorithms don’t deliver actionable insights
Research from McKinsey shows that despite all the hype surrounding digital transformation, there is a long way to go for many organisations, and the rate of transformation varies across industries. Yet already there is already profound economic fallout.
This finding confirms what many executives may already suspect: by reducing economic friction, digitization enables competition that pressures revenue and profit growth. Current levels of digitization have already taken out, on average, up to six points of annual revenue and 4.5 points of growth in earnings before interest and taxes (EBIT). And there’s more pressure ahead, our research suggests, as digital penetration deepens
At the current level of digitization, median companies, which secure three additional points of revenue and EBIT growth, do better than average ones, presumably because the long tail of companies hit hard by digitization pulls down the mean. But our survey results suggest that as digital increases economic pressure, all companies, no matter what their position on the performance curve may be, will be affected.
Mckinsey says that Consumer adoption of digital banking channels is growing steadily across Asia–Pacific, making digital increasingly important for driving new sales and reducing costs. The branch-centric model is gradually but unmistakably giving way to the mobile-centric one.
Deferring the development and refinement of a digital offering leaves a bank exposed to the risk of weakened relationships and lower profitability. Now is a critical moment to draw retail-banking customers toward Internet and mobile-banking channels, regardless of the general level of network connectivity in a given market.
Our annual study, the Asia–Pacific Digital and Multichannel Banking Benchmark 2016, was led by Finalta, a McKinsey Solution, and examined digital consumer-banking data collected between July 2015 and July 2016 from 41 banks. This article focuses on our findings from Australia and New Zealand, Hong Kong, Malaysia, Singapore, and Taiwan, examining consumer digital engagement, user adoption, and traffic and sales via Internet secure sites, public sites, and mobile applications.1 We detail three counterintuitive findings, and make suggestions for how banks should move forward.
Three counterintuitive findings
Consumer use of digital banking is growing steadily across all five markets (Exhibit 1). In the more developed markets of Australia and New Zealand, Hong Kong, and Singapore, growth in recent years has been concentrated in the mobile channel. Indeed, among some banks use of the secure-site channel has begun to shrink, as some customers enthusiastically shift most of their interactions to mobile banking. In emerging markets, growth is strong in both secure-site and mobile channels.
Three counterintuitive findings point to the need for banks to act aggressively to improve their use of digital channels to strengthen customer relationships.
First, banks can excel in their digital offering despite limitations in the digital maturity of the markets they serve. One measure of digital maturity is the Networked Readiness Index (NRI), published annually by the World Economic Forum. This scorecard rates how well economies are using information and communication technology. It examines 139 countries using 53 indicators, including the robustness of mobile networks, international Internet bandwidth, household and business use of digital technology, and the adequacy of legal frameworks to support and regulate digital commerce. Comparison of digital-banking adoption with the level of networked readiness reveals that a country’s level of digital maturity does not necessarily promote or inhibit the growth of a bank’s digital channels.
Singapore, for example, has the most highly developed infrastructure for digital commerce in the world. However, when it comes to digital banking, Singaporean banks trail their peers from the less-networked markets of Australia and New Zealand, where banks have been able to draw consumers to digital channels despite gaps or weaknesses in digital connectivity.
Some banks have also been successful in pushing mobile banking regardless of network limitations (Exhibit 2). While Australia and New Zealand have moderately high levels of third-generation (3G) and smartphone penetration (trailing both Hong Kong and Singapore), the banks surveyed have achieved much stronger consumer adoption of mobile channels than their peers in other markets.
The second key finding is that having a relatively small base of active users does not necessarily mean low traffic (Exhibit 3). Among all participating banks in our survey, banks in Malaysia report among the smallest share of customers using the secure-site channel; however, these customers tend to log on many times a month, and the typical secure-site customer interacts with the bank more than twice as often as the secure-site banking customers of participating banks in Hong Kong and Singapore.
Third, the survey data reveal wide variations in performance across key metrics by country. In Australia and New Zealand, for example, there is wide variation in digital-channel traffic, with customers logging on with 32 percent more frequency at participating banks in the upper quartile than those in the lower quartile. In Hong Kong, digital adoption among upper quartile peers exceeds that of the lower quartile peers by ten percentage points. Participants in Singapore observe a sixteen-percentage-point gap between the upper and lower quartile peers in the proportion of sales through digital channels.2 The wide gap between best and worst in class in multiple markets points to a significant opportunity for banks to beat the competition with compelling digital offers.
What banks should do
Banks in emerging markets have an opportunity to leapfrog to digital banking. Despite gaps in technology and smartphone penetration, a number of banks have tapped into consumer segments eager to adopt digital channels. Banks in emerging markets should prepare for rapid consumer adoption of digital channels. The digital evolution in emerging markets will differ considerably from the trajectory of banks in more developed markets.
Banks in highly developed markets have room to grow their active user base and digital sales. Indeed, the cost and revenue position of banks that do not act to improve their digital offering may weaken relative to peers that shift more business to digital channels. Banks in all markets should plan for this transition, especially through the integration of diverse technology platforms, the consolidation of customer data across multiple channels, and the continuous analysis of customer behavior to identify real-time needs. It is important to build services rapidly and to go live with minimally viable prototypes in order to attract early adopters—these digital enthusiasts eagerly experiment with new features and provide valuable feedback to help developers.
The significant variation of performance among countries shows great potential for banks to boost digital engagement with a dual emphasis on enrollment and cross-selling. Banks should carefully consider four best practices that often bring immediate gains by streamlining the customer’s digital experience:
Deliver credentials instantaneously upon in-app enrollment. The global best practice shows that banks that issue credentials instantaneously through in-app enrollment see their mobile activity rise on average 1.5 times faster. Of the banks that provided data on functionality, more than 50 percent do not have in-app enrollment. This presents a significant value-creation opportunity.
Simplify authentication processes to make them both secure and user friendly. Approximately three in five banks surveyed lack the ability to authenticate a user’s mobile device. In our experience, banks that store device information and allow users to log on simply by entering a personal identification number or fingerprint see three times more digital interaction than banks that require users to enter data via alphanumeric digits each time they log on.
Implement ‘click to call’ routing to improve response times. Instead of using a voice-response system, where customers must listen to a long list of options before selecting the relevant service choice, an increasing number of mobile apps are adopting click-to-call options for each segment, enabling customers to bypass the voice-response menus. Of the banks that provided data on capability, only 30 percent in our Asia–Pacific survey offer authenticated click-to-call options. The improvement in customer service is significant, with global banks able to improve the speed of answering customer calls by up to 40 percent.
Make digital sales processes intuitive and simple. Take credit cards as an example: best-practice global banks achieve average conversion rates (the ratio of page visits to applications) some 1.6 times those of Asia–Pacific banks. They do this by presenting products and features for which a customer has been prequalified through an intuitive, easy-to-read dashboard display or via tailored messages. Application forms are prefilled automatically with customer data. With intuitive and simple applications, banks in the Asia–Pacific region could increase the rate of completed applications by 22 percent, to come up to par with global best-practice banks.
Across the five markets we focused on, the branch-centric model is gradually but unmistakably giving way to the mobile-centric one. Looking at how digital-channel adoption and usage is evolving, along with the diversity of scenarios, banks have ample room to win in their target markets with a carefully tailored digital offering. Digital-savvy consumers warm quickly to well-designed and easy-to-use digital-banking channels, often shifting to the new channel in a matter of days. Banks need to act quickly to improve their customers’ digital experience or risk being left behind.
Mckinsey in a new report “A brave new world for global banking“, says three formidable forces—a weak global economy, digitization, and regulation—threaten to significantly lower profits for the global banking industry over the next three years. Developed-market banks are most affected, with $90 billion, or 25 percent, of profits at risk, but emerging-market banks are also vulnerable, especially to the credit cycle. Countering these forces will require most banks to undertake a fundamental transformation centered on resilience, reorientation, and renewal.
Our report, A brave new world for global banking: McKinsey global banking annual review 2016, finds that of the major developed markets, the United States banking industry seems to be best positioned to face these headwinds, and the outcome of the recent presidential election has raised industry hopes of a more benign regulatory environment. Japanese and US banks have between $1 billion and $45 billion in profits at risk by 2020, depending on the extent of digital disruption. Yet after mitigation, their profitability would drop by only one percentage point to 8 percent for US banks and 5 percent in Japan. Banks in Europe and the United Kingdom have $35 billion, or 31 percent, of profits at risk; more severe digital disruption could further cut their profits from $110 billion today to $50 billion in 2020, and slice returns on equity (ROEs) in half to 1 to 2 percent by 2020, even after some mitigation efforts (see exhibit for how digitization may reduce fees and margins across different businesses).
Emerging-market banks face a different challenge. They are structurally more profitable than their developed-market counterparts, with ROEs well above the 10 percent cost of capital in most cases but vulnerable to the credit cycle. Brazil, China, and Russia could have $50 billion in profits at risk, with China comprising $47 billion. A slower growth scenario could result in additional credit losses of up to $250 billion, of which $220 billion would be in China, our report finds, but with their current high profitability of $320 billion, Chinese banks should be able to withstand these losses.
Three formidable challenges
Banks must adapt to the reality of a macroeconomic environment that offers a number of risks and limited upside potential. Along with stagnating growth, banks face enormous challenges to digest the wave of postfinancial-crisis regulation, despite industry hopes of a more benign regulatory environment in the United States. Control costs in risk, finance, legal, and compliance have shot up in recent years. And additional proposals, termed “Basel IV,” are likely to include stricter capital requirements, more stress testing, and new guidelines for conduct and compliance risk.
Meanwhile the pressures of digitization, which boosts competition and compresses margins, are growing. Some emerging-market banks are managing well, offering innovative mobile services to customers. But our report finds that in the largest emerging markets, China and India, banks are losing ground to digital-commerce firms that have moved rapidly into banking.
In developed economies, digitization is impacting banks in three major ways. First, regulators, who were initially more conservative about the entry of nonbanks into financial services, are now gradually opening up. Over time, huge tech companies may be able to insert themselves between banks and their customers, capturing the vital customer relationship and presenting an existential threat. On the positive front, a number of banks are teaming up with fintech and digital firms, using big data and analytics to sharpen risk assessment and drive revenue growth. Lastly, many banks have been able to digitize processes and dramatically lower costs in their middle and back offices (although digitization can sometimes add costs).
A fundamental transformation
Countering the headwinds now gathering force means most banks will need to embark on a fundamental transformation that exceeds their previous efforts. Tinkering around the edges, as many banks have done for years, is not adequate to the scale of the task and will only exacerbate the sense of fatigue that comes from years of one-off restructurings.
This transformation is centered on three themes:
Resilience. Banks must ensure the short-term viability of their business through tactical measures to restore revenues, cut costs, and improve the health of the balance sheet. They need to protect revenues through repricing and greater intermediation, reduce short-term costs, manage capital and risk, and protect core business assets. Our report found that digitization is only the start of the answer on costs, with radical reductions in functional costs needed to fundamentally rebase the cost structure.
Reorientation. While the resilience agenda is defensive in nature, in reorientation, banks go on offense. They must reorient their business models to the customer and the new digital environment by establishing the bank as a platform for data and digital analytics and processes, and aggressively linking up with fintechs, platform providers, and other banks to share costs through industry utilities. They also need to streamline their operating models and IT structure and move toward a proactive regulatory strategy.
Renewal. The industry must move beyond traditional restructuring and renew the bank via new technological capabilities, as well as new organizational structures. Any new business model that banks design will likely require new technology and data skills, a different form of organization to support the frenetic pace of innovation, and shared vision and values across the organization to motivate, support, and enable this profound transformation.
Although consumers have quickly adopted digital channels for both service and sales, they aren’t abandoning traditional retail stores and call centers in their interactions with companies. Increasingly, customers expect “omnichannel” convenience that allows them to start a journey in one channel (say, a mobile app) and end it in another (by picking up the purchase in a store).
For companies, the challenge is to provide high-quality service from end to end, regardless of where the ends might be. That was the case for a regional bank that sensed that too many customers were falling into gaps between channels.
Mapping its customers’ journeys confirmed the suspicions (exhibit). Four out of five potential loan customers visited the bank’s website, but from there, their paths diverged as they sought different ways to have their questions answered. About 20 percent stayed online, another 20 percent phoned a call center, and 15 percent visited a branch, with the remainder leaving the process.
The channels’ differing performance pointed to specific problems. Ultimately, more than one-fifth of customers who visited a branch ended up getting loans. But in the online channel, less than 1 percent got a loan after almost 80 percent dropped out rather than fill in a registration form. Finally, in call centers, a mere one-tenth of 1 percent of customers received a loan—perhaps not surprising, since only 2 percent even requested an offer.
To integrate digital and traditional channels more effectively, the bank had to become more agile, with the understanding that its one-size-fits-most processes would no longer work. Complex registration forms were simplified and tailored to different types of customers. Revised policies clarified which channel took the lead when customers moved between channels. And new links between the website and the call centers enabled agents to follow up when online customers left a form incomplete. Together, these types of changes helped increase sales of current-account and personal-loan products by more than 25 percent across all channels.
Good piece in the McKinsey Quarterly where the bank’s Head of Operations and Technology, Don Callahan, describes the bank’s efforts to accelerate its digital transition. Watch the video.
We know we have to be mobile first, and we are doing a lot there. In order to be all-in on mobile, we have set up a “lean team” in our Long Island City office, with about 100 people who are operating in a very agile way.
Callahan surveys the 21st-century banking terrain: digital competitors are massing on every front—from fintech start-ups to new divisions of global institutions—while the speed of every banking process and customer interaction accelerates daily. All this change requires a focus on agility, Callahan says, which in turn demands a cultural rewiring.
At the helm of Citi’s digital transformation, Callahan is helping drive new thinking across the bank. He points to Citi’s digital lab for start-up innovations, powerful new apps for customer smartphones, and, internally, a push to expand capabilities across cloud computing and big data and analytics that enable automation and machine learning. In an interview with McKinsey’s James Kaplan and Asheet Mehta, Callahan describes what it takes to mobilize digital change at one of the world’s leading financial institutions.
The General Insurance sector is one of the laggards across financial services when it comes to digital transformation. However, according to a new report from McKinsey, whilst the sector lags in digital sophistication and so examples of the full benefits of digital are scarce; they suggest that the top 20 or 30 processes can account for up to 40 percent of costs and 80 to 90 percent of customer activity. Digitizing these processes can take out 30 to 50 percent of the human service costs while delivering a much better customer experience. And the benefits do not end there.
The nature of competition in property and casualty (P&C) insurance is shifting as new entrants, changing consumer behaviors, and technological innovations threaten to disrupt established business models. Though the traditional insurance business model has proved remarkably resilient, digital has the power to reshape this industry as it has many others. Innovations from mobile banking to video and audio streaming to e-books have upended value chains and redistributed value pools in industries as diverse as financial services, travel, film, music, and publishing. As new opportunities emerge, those insurers that evolve fast enough to keep up with them will gain enormous value; the laggards will fall further behind. To succeed in this new landscape, insurers need to take a structured approach to digital strategy, capabilities, culture, talent, organization, and their transformation road map.
Though the P&C insurance business has long been insulated against disruption thanks to regulation, product complexity, in-force books, intermediated distribution networks, and large capital requirements, this is changing. Sources of disruption are emerging across the value chain to reshape:
Products. Semiautonomous and autonomous vehicles from Google, Tesla, Volvo, and other companies are altering the nature of auto insurance; connected homes could transform home insurance; new risks such as cybersecurity and drones will create demand for new forms of coverage; and Uber, Airbnb, and other leaders in the sharing economy are changing the underlying need for insurance.
Marketing. Evolving consumer behavior is threatening traditional growth levers such as TV advertising and necessitating a shift to personalized mobile and online channels.
Pricing. The combination of rich customer data, telematics, and enhanced computing power is opening the door to usage- and behavior-based pricing that could reduce barriers to entry for attackers that lack the loss experience formerly needed for accurate pricing.
Distribution. New consumer behaviors and entrants are threatening traditional distribution channels. Policyholders increasingly demand digital-first distribution models in personal and small commercial lines, while aggregators continue to pilot direct-to-consumer insurance sales. Armed with venture capital, start-ups like Lemonade—which raised $13 million in seed funding from well-known investors including Sequoia Capital—are exploring peer-to-peer insurance models.
Service. Consumers expect personalized, self-directed interactions with companies via any device at any hour, much as they do with online retail leaders like Amazon.
Claims. Automation, analytics, and consumer preferences are transforming claims processes, enabling insurers to improve fraud detection, cut loss-adjustment costs, and eliminate many human interactions. Connected technologies could allow policyholders and even smart cars and networked homes to diagnose their own problems and report incidents. Self-service claims reporting such as “estimate by photo” can create fast, seamless customer experiences. Drones can be used to assess damage quickly, safely, and cheaply after catastrophes. All these disruptions are being driven and enabled by digital advances, as illustrated with examples from auto insurance. No single competitor or innovation poses a threat across the entire value chain, but taken together, they could lead to the proverbial death by a thousand cuts: many small disruptions combining to fell a giant.
Whilst most Fintechs are attacking the retail banking value chain, where the share of global revenue is highest, all segments are under attack according to a report published by McKinsey “The value in digitally transforming” credit risk management“. This chart which shows the footprint of Fintechs relative estimated share of bank revenue and client segments.
Whilst it may not be fully representative for any one segment or product, the chart is based on McKinsey’s financial-technology database which includes >350 of the best-known start-ups. “Commercial” includes small and medium-size enterprises, “large corporates” includes large corporations, public entities, and nonbanking financial institutions. The “financial assests and capital markets” includes investment banking, sales and trading, securities services, retail investment, noncurrent-account deposits, and asset-management factory.
The new competitors are beginning to threaten incumbents’ revenues and their cost models. Without the traditional burden
of banking operations, branch networks, and legacy IT systems, fintech companies can operate at much lower cost-to-income ratios—below 40 percent.
Interesting piece from McKinsey showing that the structure of the fintech industry is changing and that a new spirit of cooperation between fintechs and incumbents is developing. For example, in corporate and investment banking, less than 12 percent are truly trying to disrupt existing business models.
Fintechs, the name given to start-ups and more-established companies using technology to make financial services more effective and efficient, have lit up the global banking landscape over the past three to four years. But whereas much market and media commentary has emphasized the threat to established banking models, the opportunities for incumbent organizations to develop new partnerships aimed at better cost control, capital allocation, and customer acquisition are growing.
We estimate that a substantial majority—almost three-fourths—of fintechs focus on retail banking, lending, wealth management, and payment systems for small and medium-size enterprises (SMEs). In many of these areas, start-ups have sought to target the end customer directly, bypassing traditional banks and deepening an impression that they are disrupting a sector ripe for innovation.
However, our most recent analysis suggests that the structure of the fintech industry is changing and that a new spirit of cooperation between fintechs and incumbents is developing. We examined more than 3,000 companies in the McKinsey Panorama FinTech database and found that the share of fintechs with B2B offerings has increased, from 34 percent of those launched in 2011 to 47 percent of last year’s start-ups. (These companies may maintain B2C products as well.) B2B fintechs partner with, and provide services to, established banks that continue to own the relationship with the end customer.
Corporate and investment banking is different. The trend toward B2B is most pronounced in corporate and investment banking (CIB), which accounts for 15 percent of all fintech activity across markets. According to our data, as many as two-thirds of CIB fintechs are providing B2B products and services. Only 21 percent are seeking to disintermediate the client relationship, for example, by offering treasury services to corporate-banking clients. And less than 12 percent are truly trying to disrupt existing business models, with sophisticated systems based on blockchain (encrypted) transactions technology, for instance.
Assets and relationships matter. It’s not surprising that in CIB the nature of the interactions between banks and fintechs should be more cooperative than competitive. This segment of the banking industry, after all, is heavily regulated.1 Clients typically are sophisticated and demanding, while the businesses are either relationship and trust based (as is the case in M&A, debt, or equity investment banking), capital intensive (for example, in fixed-income trading), or require highly specialized knowledge (demanded in areas such as structured finance or complex derivatives). Lacking these high-level skills and assets, it’s little wonder that most fintechs focus on the retail and SME segments, while those that choose corporate and investment banking enter into partnerships that provide specific solutions with long-standing giants in the sector that own the technology infrastructure and client relationships.
These CIB enablers, as we call them, dedicated to improving one or more elements of the banking value chain, have also been capturing most of the funding. In fact, they accounted for 69 percent of all capital raised by CIB-focused fintechs over the past decade.
Staying ahead. None of this means that CIB players can let their guard down. New areas of fintech innovation are emerging, such as multidealer platforms that target sell-side businesses with lower fees. Fintechs also are making incursions into custody and settlement services and transaction banking. Acting as aggregators, these types of start-ups focus on providing simplicity and transparency to end customers, similar to the way price-comparison sites work in online retail. Incumbent banks could partner with these players, but the nature of the offerings of such start-ups would likely lead to lower margins and revenues.
In general, wholesale banks that are willing to adapt can capture a range of new benefits. Fintech innovations can help them in many aspects of their operations, from improved costs and better capital allocation to greater revenue generation. And while the threat to their business models remains real, the core strategic challenge is to choose the right fintech partners. There is a bewildering number of players, and cooperating can be complex (and costly) as CIB players test new concepts and match their in-house technical capabilities with the solutions offered by external providers. Successful incumbents will need to consider many options, including acquisitions, simple partnerships, and more-formal joint ventures.