Finovate talks with Ronit Ghose, global head of banks research and co-head of the fintech theme group at Citi about the future of challenger banks and why some shouldn’t be calling themselves a “fintech” at all.

Finovate: How would you define the different types of challenger bank that exist today, and what are the key differentiating factors between them?

Ronit Ghose: Challenger banks are designed around the digital revolution and are able to leverage data insights via advanced technology stacks. I’d say there are three types of challenger banks that have emerged:

• The first are standalone challenger banks, which are primarily Fintech companies leveraging technology and data to streamline retail banking by offering better convenience and pricing.
• The second are incumbent-led challenger banks, started within legacy banks through investment in technology and by creating new digital-only banks.
• Finally, we’re seeing BigTech-led challenger banks who can use their vast networks to acquire customers quickly as they branch out into financial services.

Finovate: Interesting! So, we’ve seen many incumbent banks attempt to set up their own challenger banks – how successful has this been, what lessons should others learn, and how can banks make their back end look more like a digitally-native company’s?

Ghose: Over the past five years or so, especially since 2016 through 2017, incumbent banks have moved from ignoring or mocking the new entrants to engaging with them and giving them the best testimonial possible: They have begun copying them by setting up their own new businesses. While the results have been mixed, the success or failure of incumbents in this field could be characterized using three factors: markets, technology and operating model or culture.

So in most cases, incumbent banks launch a challenger bank in a market where they are already active; albeit they use their new proposition to better target a specific segment, such as millennials or digitally-savvy customers. With regards to technology, in the past 12 to 18 months incumbent banks appear to be moving to consider more disruptive technology and business model approaches, and to attempt to actually build new brands or businesses “like a startup”. If you aren’t doing new tech, then stop calling yourself challenger or fintech. ‎

Finally, we have to consider bank employee incentives, training, and formation are the human capital equivalent of a fixed income instrument. By contrast, fintech founders work and their employees are growth equity to the bank employees’ fixed coupon bond. In the language of financial instruments, can banks become convertibles not just bonds? ‎

Finovate: Moving away from challenger banks to other new market entrants, to what extent do incumbent banks fear big technology companies over fintechs?

Ghose: The emergence of BigTech has led to heightened competition in the financial services sector. I think the challenge BigTech poses for incumbent and standalone challenger banks is daunting, given the absence of any cost drag from legacy information technology (IT) systems and underused branch networks (common problems for banks) and their natural advantage in customer acquisition owing to their high user engagement models.

One of the most prominent of these is in Korea, where popular social messaging app Kakao Talk launched a digital-only bank in 2017, acquiring two million customers in a short span of just two weeks from launch date. Similarly in China, challenger banks such as WeBank, backed by Tencent, respectively, have seen strong user growth following their launch in 2015.

The experiences of Korea and China are successful examples of internet companies venturing into banking. There are many lessons to be learned from this. Firstly, incumbent banks should not be overly complacent with their existing customer base – the speed of customer acquisition could be much faster through digital channels than the traditional distribution channels. Secondly, internet giants have a clear edge in certain areas of banking, especially around payments and mobile money. Finally, there are opportunities to cross-sell and scale to other products.

Finovate: So there’s potential for a lot of change and upheaval then. What will the bank of the future be characterized by?

Ghose: Legacy banks often have data that is stuck in multiple silos supported by core banking technology that was literally built in the era of black and white television. Manual intervention is high, which slows down operating speed, reduces flexibility, increases costs, and ultimately degrades efficiency and experience. Creating an incumbent challenger sounds like an oxymoron, but as legacy banks recognize the threat that new entrants into banking are posing to revenue and customers, they need to reinvent themselves and reimagine banking. This involves legacy banks partnering with technology companies to create effective joint ventures as well as moving into more disruptive technology and business models to transform themselves into digital competitors. By creating their own Bank X, we believe some legacy banks can transform themselves from slow moving caterpillars to agile butterflies.

From banking chatbots to speculations on superintelligence, the impact of artificial intelligence (AI) on financial services is one of the hottest topics in fintech. Our Summit Day sessions on AI at FinovateSpring earlier this year were consistently among our best attended sessions.

To continue this conversation, we exchanged emails with Alenka Grealish, Senior Analyst, Corporate Banking, Celent. Grealish’s recent report, AI in the UI: Adoption, Use Cases, and Business Cases, represents Celent’s latest investigation into the issues surrounding the rise and role of AI in financial services.

Finovate: In setting up this conversation, I noted that Celent referred to this as part of an inaugural initiative. Why is now the time to turn the spotlight on this technology and its impact on financial services?

Alenka Grealish: We observed the beginning of a shift from all experimentation to gradual implementation amongst vanguard banks. It was common to have nine proofs of concept to one pilot at the vanguard banks. We’re now seeing more pilots and a few moving into production.

Finovate: What are we talking about when we talk about AI? How broad is this technology?

Grealish: Broad. What defines AI has expanded in the commercial world. The narrow Turing Test no longer applies. The current goal of AI developers is not to replicate humans, but rather complement them and build applications that team with them. A great example is found in anti-money laundering.

The broad definition includes rules-based and learning-based models. A useful way to categorize AI capabilities is: natural language processing and understanding, natural language generation (data-to-text), speech (speech-to-text and vice versa), vision, and data insights (machine learning driven analytics that generate, for example, cash flow forecasts for customers and next best action for bankers).

Finovate: Has AI become a catch-all for a variety of technologies, some of which are AI and some of which are not? And is that an issue for AI adoption going forward?

Grealish: AI has certainly become a buzz word and its definition stretched by tech vendors. Semantics aside…Critical to successful AI adoption is not to seek a problem for AI to solve but rather the reverse:  determine the key problems you’re trying to solve (e.g., high false positives in AML) and/or goals you’re trying to achieve (e.g., personalize customer-banker interactions). Then, (the next step is to) examine the potential means to solve/achieve. The means could be a combination of rules-based and learning-based AI or established tech (e.g., OCR) combined with AI.

Finovate: What were the top two or three high-level takeaways from your research?

Grealish: I was struck by the percentage of banks $10+ billion in size which had implemented front-office AI. I had expected less than 10%.

Finovate: Your report notes a difference in AI adoption between retail and commercial banks, calling the former an “early adopter” and the latter “vanguard.” What distinguishes the two?

Grealish: The vanguard phase is when a small number of entities, less than 5%, moves into production. The technology is not mature but works sufficiently well for low risk use cases. These entities tend to have nimble organizations and little to no legacy baggage. The early adopter phase typically occurs when the vanguard banks are successful and appear to be gaining a competitive edge and inspire the next wave of adopters to take action. The early adopters are innovators but are likely juggling multiple priorities and hence cannot always be in the vanguard.

Finovate: One of the areas you highlight is the use of AI-enabled technologies for employees and workers. What sort of use cases – especially those relevant to financial services – are you seeing here?

Grealish: In terms of employee enablement, I’m excited by what I see.  AI is proving helpful in basic “tell me” support, such as, “do we offer this type of product?” and “where is this feature located in our online portal?” It is also progressing in higher level support, such as data insights on sales trends and next best action suggestions.

Finovate: You note “relative complexity” as a main hurdle to broader adoption of AI. How are financial services companies navigating this challenge (hiring talent, partnerships, etc.)?

Grealish: AI is not a standalone technology but rather is woven into current processes and platforms and/or drives new processes and platforms. Hence, success begins at the top of the house, banks with a transformation, data-driven leadership team view AI as one component of a broader digital strategy.

Next, successful banks have a business model comprising four key elements: a collaborative multidisciplinary organizational dynamic, an enterprise-wide AI initiatives team, strong data and model governance, and regulatory engagement and compliance playbook. At the operating model level, these banks have basic automation expertise and are incorporating AI to solve the hard stuff, such as analysis of unstructured data.

At the foundation, these banks are migrating to a modern data and tech infrastructure that supports a digital-first strategy.

Finovate: You note that the primary business goal for most businesses using AI-enabled technologies is cost savings, but that customer engagement “is increasingly a goal.” What are some of the more interesting use cases for AI-enabled technologies in customer engagement?

Grealish: We’re in the very early days of customer engagement, that is, brief, basic “tell me” conversations. These “tell me” conversations are taking off thanks to Siri, Alexa, Google Assistant, which are driving consumers’ comfort level engaging with machines. The outlook over the next 5 years is promising. “Do it for me” type interactions will become common. For example, a small business will simply ask the online virtual assistant to choose the optimal payment type based on its criteria. Further on the horizon are “Alert and advise me” type interactions. For example, a mid-market company has an FX exposure and is alerted with action options to hedge the exposure.