Licensed neobanks are disrupting the global financial services industry on the back of strong trends, but their long-term success hinges on their ability to create an artificial-intelligence-powered banking model that is customer centric, operationally efficient, and profitable at scale.
The last decade has seen around 400 launches of licensed neobanks,
an overarching category that includes digital-only banks, virtual banks, and challenger banks (see sidebar, “What is a neobank?”). Incumbent banks, nonbank challengers (such as fintech players), stand-alone digital attackers, and large consumer and payments platforms have all launched neobanks in recent years, making the competitive pool increasingly vast and diverse.
Neobanks have reset the paradigm for the traditional banking industry in terms of customer experience, product innovation, and pricing, enabled by burgeoning consumer demand and supportive regulation of licensing frameworks. Investors have been drawn to the disruptive power of these banks, directing approximately $32 billion in estimated global venture capital funding to neobanks between 2017 and 2021.
When it comes to financial performance, however, only a handful of neobanks have demonstrated success. The rest have struggled to create value. Some of the world’s biggest neobanks have, in fact, seen their valuations plummet in recent months, responding to ongoing macroeconomic volatility and shifting investor perceptions on the neobanks’ valuation drivers.
In such a scenario, how can neobank start-ups set themselves up for success today? While we believe long-term trends will keep the digital-banking industry relevant and appealing, even amid an economic downturn, they will not guarantee profitability. In this article, we will describe a framework of winning characteristics and capabilities that any new entrant should incorporate in its strategy and execution to become a future-ready neobank.
Differentiating characteristics of winning neobanks
The banking ecosystem has undergone seismic shifts in recent years. In the case of retail banking, profit pools are large and attractive, but banks have historically had to cope with high barriers to entry (for example, high fixed costs and a need for trusted consumer brands) and long paths to reach breakeven points. Neobanks have challenged these barriers by operating with lower cost structures—typically a fraction of traditional banks’ costs—and disruptive pricing practices; compared with traditional banks, they offer lower prices and more transparent pricing terms with fewer hidden fees.
That said, the real challenge for neobanks today is how to strengthen that competitive advantage by capturing a higher share of consumers’ wallet and generate material profits. The answer, to a large degree, lies in embedding data and AI capabilities extensively across all aspects of neobanks’ operations.
AI can help neobanks deepen customer relationships (for example, designing intelligent value propositions that solve unmet needs, harnessing big data to deliver hyper-personalized services and enhanced cross-selling), and improve financial performance (for example, maximizing customer lifetime value, dramatically lowering the cost to serve through automation, and adopting superior data-driven risk management practices). In addition to having an AI-first mindset, neobanks also must ensure that these AI capabilities work to their advantage and deliver actual business value.
Many of the world’s leading neobanks that have achieved scale or profitability have leveraged AI effectively. According to our analyses of these successful players, they all demonstrate the following seven characteristics.
1. Launching products at high velocity
Most banks aim to be customer focused, but winning neobanks go a step further in ensuring customer delight by rapidly launching and reworking new products and propositions. They adapt to evolving customer needs, as well as attract, engage, and monetize various customer segments.
While a typical bank could take years to launch a new product, neobanks with an AI-first mindset do it in months, sometimes even weeks. To do this, they make substantial investments in gathering and analyzing extensive customer data. They organize full-stack teams that include people from each of the relevant functions—product owners, designers, and data scientists, as well as members of the legal, risk, operations, and marketing teams. They also maintain flexible and configurable tech platforms that allow relatively easy creation of new products.
A case in point is Revolut, which had only two products in 2015: its flagship multi-currency travel card and a mobile app. By 2020, it had branched into 20 products and services,
or on average, a new product launched every quarter, driven by Revolut’s strategy of experimenting with launching new products across the stack to address evolving market trends and then doubling down on the products getting the most traction. In 2018, for example, it launched cryptocurrency features within its app, becoming one of the few licensed neobanks to do so at the time.
2. Focusing on customer engagement
Successful neobanks prioritize and excel at customer engagement. Like their peers in the consumer tech sector, they think of digital engagement as a precursor to monetization—operating on the idea that share of customers’ time precedes their share of wallet.
As a result, their strategy is not limited to providing the best transaction experience or pricing, or bundling their core products. They also tend to go beyond pure-play financial services to deliver complementary services that provide not just new forms of utility (for example, “save and earn more,” “get better deals”), but also inform and entertain (“play games,” “see what’s trending,” “learn about new topics”).
Offerings boosting engagement range from digital and user-generated content, commerce, social features, gamification, and personal financial management. Many neobanks provide deals and discounts, helping customers discover new products, gamifying financial services (for example, managing a “demo” investment portfolio that tracks the market in real time), offering insightful and entertaining content in their area of interest (vacation planning, entertainment booking), and offering social features (for example, “send a gift to your friends and family”).
These neobanks are effectively curating and designing products and services based on several factors, including the ability to engage target users based on their interest patterns and alignment with the bank’s overarching brand and purpose. For example, besides allowing customers to access seamless payments and credit products at point of sale, Sweden’s Klarna uses a machine learning-based recommendation system to determine consumer purchase patterns, and offer appropriate shopping recommendations and financing offers.
Additionally, leading AI-powered neobanks rigorously track their engagement performance. They have dedicated teams to analyze several engagement metrics, such as the average time spent by a customer on the app, the number of daily active users (DAU), number of monthly active users (MAU), and the ratio of daily to monthly active users. Some of the best performers have achieved a DAU-to-MAU ratio of 25 to 35 percent for their overall user base.
3. Hyper-personalizing experiences and propositions
Neobanks have demonstrated ubiquitous use of machine-learning-enabled personalization across customer touchpoints. The more successful ones are also personalizing user experience by matching customers’ present context and historical behavior, direction of movement (for example, predicting their current state of satisfaction and next-best action), and aspirations. The industry standard for digital banks is rapidly shifting toward the n = 1 level of personalization, in which each customer can receive unique propositions and experiences generated by a combination of multiple machine-learning models.
Some leading neobanks are also creating propositions that can automate mundane high-friction tasks, based on customers’ preferences. The UK-based virtual bank Monzo, for example, introduced automatic saving features in its app. The features can round up transactions every time a customer spends and deposit that money in a separate account and can automatically transfer a preset percentage of their monthly paycheck to a savings account on payday. While these features are arguably common in the online-banking industry, their effectiveness depends on how well these nudges are dynamically personalized. A successful bank can build models on a user’s transaction data and demographic information, among other data, to identify actionable budget and savings goals automatically.
4. Adopt conversational design
Chatbots play an essential role in customer communication, with most banks using them to address customer queries and service requests. AI-focused neobanks are going a step further by replacing forms and questionnaires with conversational design in the form of chatbots, voice assistants, and live video consultations.
By doing this, they can combine the customer’s context and intent, using natural-language processing techniques to deliver superior engagement. China’s WeBank extensively deploys AI in user interactions and claims that almost 98 percent of customer queries are addressed by AI chatbots.
This allows the bank to scale with higher operating leverage while serving millions of customers every day.
This characteristic also drives cost-efficiency. One report estimated that operational cost savings from effectively using chatbots in banking could reach $7.3 billion globally by 2023, equivalent to roughly 862 million hours of time saved.
5. Integrating open-banking features seamlessly
While several banks are adopting open-banking enablers, successful neobanks gain competitive advantage by building an open platform from the outset. With a default open-first approach, they can invest extensively in building the right API
-first architecture and experiences at the time of setting up the bank. This allows them to seamlessly tap into the broader open-banking ecosystem and offer customers superior products and services.
Chime, a San Francisco–headquartered fintech, uses open-banking features (powered by its partner Plaid) to let customers link all their bank accounts within the Chime app. Customers can see all their accounts, balances, and transactions in one place, which increases their engagement and gives them a full picture of their finances. In fact, Chime reports that users who link a non-Chime account typically spend five times more money through various purchases with their Chime debit card in their first 12 months on the platform.
6. Leveraging partner ecosystems to scale
Most banks have established partnerships with social-media platforms and digital commerce, healthcare, and lifestyle brands. Winning neobanks seamlessly embed their services within these ecosystems, using technology to provide customers easy, low-friction access to their banking services every time they engage with the ecosystem partner. By doing this, neobanks increase their discoverability, accelerate their scale in a cost-effective manner by going beyond traditional customer acquisition routes, and efficiently leverage partner data to provide contextually relevant offers to customers in real time, without sacrificing their privacy. As a result, leading players can increase new-product sales within the partner ecosystem by around 50 to 70 percent, according to McKinsey estimates.
Given the strategic importance of partnerships, many neobanks are now set up as joint ventures or equity partnerships between traditional banking players and large consumer platforms. Others are creating their own mega ecosystems. One example is South Korea’s KakaoBank, which was formed by the parent company of the country’s dominant messaging app, KakaoTalk, in 2016. KakaoBank has successfully leveraged the scale and popularity of Kakao’s various ecosystems. As of year-end 2021, the bank had 18 million customers,
and its Kakao partner ecosystems now include a range of services, including Kakao Commerce, Kakao Mobility, and Kakao Games.
7. Using customer-lifetime-value (LTV) to guide actions
Successful neobanks have a different mindset when it comes to measuring performance and creating value. In addition to tracking traditional balance sheet metrics like the return on average assets and net interest margins, which summarize the aggregate performance of the business, they also analyze customer-centric metrics like customer lifetime value (LTV), customer acquisition costs (CAC), and return on investment (LTV/CAC).
Doing so enables neobanks’ planning and operating teams to take more granular and tailored actions quickly, which eventually boosts customer value. These actions could be strategic, such as a new-product launch that aims for a material increase in LTV or discounts intended to reduce churn for specific customer cohorts. Other actions are operational—for example, determining how much CAC can be incurred to acquire a certain customer (Exhibit 1).
For example, San Francisco–headquartered Block Finance’s mobile payment service Cash App tracks and reports customer LTV curves with the returns (as a multiple of the CAC) from its customer cohorts, plotted against the time since the customers started using Cash App. According to Block,
Cash App reaches CAC breakeven within six months of acquiring a customer and earns six times CAC within 18 months. Block also has reported that Cash App has also consistently improved its monetization performance for each customer cohort from 2017 to 2020.
Tracking such metrics is not easy. Several front-end and back-end systems must be adapted to measure and report the metrics to a range of teams. The decision-making processes (machine driven or human) need to be aligned on optimizing these metrics. Decision making also must be embedded in the organization’s performance rubric and operating rhythms.
Building the capabilities
Players interested in launching a neobank today should adopt an AI-first mindset and build a holistic set of capabilities in four interconnected layers (Exhibit 2). This approach will equip banks to acquire many of the differentiating characteristics.
To succeed in the current competitive environment, neobanks need to create experiences and build accompanying capabilities that fulfill a range of customer expectations. First, the engagement layer needs to deliver experiences that are not only easy, intuitive, fast, and responsive but also delightful. To do this, they could create low-friction, low-latency, and highly customizable mobile-first journeys.
Second, these experiences need to be available where customers need them the most. One way for banks to do this would be to build their products and interfaces in a way that they can be seamlessly integrated with various ecosystem partners, allowing customers to discover and consume propositions beyond the bank’s core platforms.
Third, the experiences need to be tailored to each customer’s needs and context and be hyper-personalized. For this, neobanks need to set up a product innovation engine that allows for the discovery of unarticulated customer needs and the flexibility to introduce innovative new products. Equally important is to ensure the flexibility of the entire interface (for example, enabling it to change the products and journeys in varied ways for each customer). The engagement layer should also be suitably linked to the models in the decisioning layer, to achieve the third goal, sending customers real-time personalized offers, nudges, and recommendations.
AI-and-analytics-led decisioning layer
Hyper-personalization requires granular AI-driven decisions across the entire customer life cycle, spanning acquisition, onboarding, servicing, retention, and cross-sell. This requires many machine-learning (ML) models across domains including credit analytics, product and channel propensity, fatigue (how often neobanks are reaching out to customers), and risk (including attrition, collections) to identify the next-best action for customers.
Some essential capabilities are required to build and effectively deploy these models. The first relates to data gathering. Since ML models are built on data, neobanks need to create data ingestion pipelines. Data collated from these pipelines should include traditional banking customer information, transaction data, and less-traditional data streams such as clickstream data from a bank’s platforms (app or website) or data from third-party partnerships with various platforms and aggregators. All the data should then be stored in a unified data platform that aggregates, enriches, and maintains a 360-degree view of existing and potential users, providing complete visibility on all customer interactions with the bank across products, transactions, and service requests. This will help neobanks develop new decisioning models.
Once the data have been collated and the model has been created, neobanks also need to be able to rapidly deploy, test, and iterate on their models in live production environments. This requires technical capabilities such as machine-learning operations (MLOps) and close coordination among data science, product, technology, and business teams to ensure the models are built for live and efficient production deployment.
Core technology and data layer
Developing experiences in the engagement layer and personalizing the decisioning layer require a modern technology and data core. When incumbent banks invest in digital capabilities, their money is mostly spent in the maintenance of their existing core technology. Far less investment goes to building new cloud, data, or API capabilities.
Neobanks unencumbered by legacy decisions can leapfrog incumbents by building their core tech. Their goal should be to develop a cloud-based, flexible, and highly configurable banking core that will help engineering teams launch new product variants rapidly through easy configurations. To do this, neobanks would need to make judicious decisions on which components to build on their own and which to outsource. For example, most neobanks use externally available cloud-based infrastructure and storage services.
As a starting point, neobanks should design a modular, micro-services-led, API-first architecture. This should be able to scale efficiently, allow high reusability to reduce the time to market of new features and the overall cost to maintain code, and ensure ease of integration with various partners and the open-banking ecosystem. Additionally, neobanks should ensure that data management enables data liquidity, including the ability to access, ingest, and manipulate data across systems and models. This crucial element serves as the foundation for all decisions in the decisioning layer.
The final layer—and a crucial determinant of a neobank’s speed and agility—pertains to how it attracts and organizes talent across its teams in a way that breaks traditional organizational silos. Three capabilities are key here. First, a successful neobank should be able to set and embody an aspirational vision and culture for the organization. This will help it attract leading talent in banking and critical nonbanking domains, including designers, architects, engineers, and data scientists. Second, these neobanks should have full-stack platform-based teams with all the skill sets required for their end objective—for example, driving revenue, enhancing engagement, or ensuring service quality and increasing operational efficiencies. These teams should also have autonomy to define and execute actions aligned to their goals while being loosely coupled with the rest of the organization. Finally, the neobanks should calibrate the organizational rhythms and processes that promote innovation and rapid experimentation while adhering to regulation and compliance norms.
Despite current headwinds, the long-term growth trajectory of neobanks is expected to remain strong. Those starting their digital-banking journey today should be focused on preparing for disruptions of all kinds. The key to success lies in making AI the centerpiece of the neobank’s design, architecture, operations, and the whole gamut of the customer experience strategy.