Neko Solutions

Digital banks have been one of the main #fintech disruption use cases, but as the competitive landscape evolves, they find themselves at a crossroads. This is a Europe 2024 deep-dive. Neobanks, challenger banks or digital banks are terms used interchangeably and although they are not the same, they all share a #technology-first approach that involves a flexible, branchless, digital-native model challenging traditional #banking. The concept emerged in the early 2010s on the back of a few main premises: - Technological #innovation decoupling, for the first time, the front-end from the infrastructure layer and web APIs becoming ubiquitous as the connecting rails underneath - The rise of seamless, end-to-end digital experiences in industries such as #ecommerce and entertainment, acting both as a benchmark and a guide - Big gaps in traditional banking offerings: non-transparent, complex, expensive and inelastic products built on top of outdated systems - The 2008 financial crash acting as a springboard in 2 directions: 1) laid-off talent looking for new opportunities 2) available capital (i.e. VCs) seeking alternative bets as a hedge to traditional financial institutions Two major models rose in Europe: - Greenfield players (i.e. N26, Revolut, Monzo) starting from scratch via a niche market or segment, and focusing on seamless customer experience, attractive design, competitive pricing and a digital or mobile only set-up. - Incumbent banks launching their own neobanks (i.e. Hello Bank! by BNP Paribas, Boursorama Banque by Société Générale and FinecoBank by UniCredit) as a means of competing with the fintech challengers and of retaining their customers. They leverage infrastructure and resources and quite often integrate with existing parent offerings with an eye at cross-selling and up-selling. Despite a rosy start, most players have faced increasing challenges over the past years, especially with the new macro reality. Rising interest rates have boosted the industry as a whole, however challengers have, largely, not managed to reap the full benefits (owed to their business model). According to C-Innovation, only 14 out of 101 digital banks in Europe are profitable. Going forward challengers are facing difficult choices: - Focus has shifted from growth to profitability, but the latter goes through lending, which, in turn, needs a banking license that is not always easy to get with a compliance-light, start-up model (as the Revolut UK saga has shown) - Monetization and scale go through transitioning from a convenience, niche-based set-up to becoming the primary bank (get the payroll and build long-term relationships). Trust is the key here but building it is a long and uncertain path. In entering their 2.0 play, challenger banks are facing the odd situation of having to balance an innovation, growth-first agile culture with more traditional ways of doing banking. Opinions: my own, Graphic source: C-Innovation

🚨 𝗡𝗲𝘄𝘀 𝗙𝗹𝗮𝘀𝗵: Revolut is launching phone plans for travelers in the UK Here is what we know: Revolut is launching phone plans in the U.K., the company has told CNBC exclusively, making it the first financial services firm in the country to offer telecom plans — and among the first globally. The digital banking and payments unicorn said it will start offering eSIMs — SIM cards that can be stored virtually rather than in physical form in the device — this week. The plans will begin rolling out for users in the coming days. Customers on Revolut’s basic app experience without any subscription can get a standard eSIM plan that allows them to access their Revolut app so that they can top up their phone as and when needed. For instance, if a Revolut user arrives at an airport and runs out of data on their current SIM provider, they can still access features on their Revolut app free of charge and top up their data as usual. Tara Massoudi, general manager of premium products at Revolut, said the decision for Revolut to launch eSIMs was to turn the company into more of an all-encompassing “super app” with services spanning bank accounts, currency exchange, insurance, travel bookings and airport lounge passes. “Our ambition is very much to be the financial super app,” Massoudi told CNBC. “This is really in that direction.” “Travel is a huge value prop that we’ve always had, and it’s still remained super important for our users,” Massoudi added. More info/source: https://lnkd.in/eyuthsZ2 Find this helpful? [ 𝗿𝗲𝗽𝗼𝘀𝘁 ] Anything to add about this subject? [ 𝗶𝗻𝘃𝗶𝘁𝗲𝗱 𝘁𝗼 𝗰𝗼𝗺𝗺𝗲𝗻𝘁 ] Nice story, Marcel. Next! [ 𝗹𝗶𝗸𝗲 ]  #revolut #neobank #superapp #bankingapp #challengerbank #banking #bankingindustry #esim #fintech #financialtechnology #fintechindustry #fintechnews 

⚡ How to move from a legacy banking system to a thriving #financialservices ecosystem via Zühlke Group ⚡ Traditional banks grappling with legacy systems, high ownership costs, and slow product expansion can break free from innovation constraints through digital transformation. Shifting to a modern, cloud-based banking platform enables quicker time to market, data-driven decision-making, and personalised customer experiences, ensuring not just survival but thriving in the evolving financial landscape. In the UK market, companies like NatWest, Lloyds Banking Group, Klarna, and Apple showcase the benefits of an ecosystem approach. From digital ID apps enhancing customer verification to #BNPL integration and #openbanking initiatives, these examples highlight improved customer experiences, streamlined processes, and increased sales, emphasising the necessity of a modern, cloud-based banking platform for effective ecosystem integration. 💎 Use Case 💎 Zühlke's successful collaboration with an innovative virtual bank in Asia exemplifies the advantages of a financial ecosystem. Utilising cloud-hosted third-party products, including general ledger and anti-money laundering tools, Zühlke's multidisciplinary engineering and DevOps expertise facilitated the development of core banking systems, resulting in market deployment within an impressive 18-month timeframe. The seamless operation of services, such as near real-time ledger and KYC, on Thought Machine in Amazon Web Services (AWS), now enhances customer and staff experiences with an efficient #corebanking solution. 💥 The Bottom Line Mounting expenses, prolonged time to see value, intricate customer connections, heightened competition, and various market forces are placing significant strain on #banks. This underscores the urgent need for digital transformation. Banks must take immediate and decisive action to modernise their platforms, embracing the ecosystem approach to fortify their market position and ensure sustained growth in the future. 👋 Kudos to all contributors involved in collating this white paper: Mark Hipperson | Ziglu Brendan Gilmore | BPG Strategy Wolfgang Emmerich | CEO Zühlke UK  Mark Venn | #Zühlke UK Liz Wray | Zühlke UK Chris Joyce |  Zühlke UK 🌐 Link to guide below in the comments 🌐

The Architecture of the Embedded Payments Ecosystem 💡 The PayFac Ecosystem orchestrates transactional, regulatory, fraud and risk requirements necessary to accept and make digital payments on behalf of the merchants, marketplaces and software platforms that want to embed payments into their businesses without taking the steps required to be a payments business. Additionally, the PayFac ecosystem has grown increasingly dynamic because any given entity can play multiple parts. 👨💻 Platforms and Intermediaries  Platforms and intermediaries are the heart of the digital economy. These are the businesses that have a connection with the end user and where the core transactions that drive the need for an embedded digital payments experience take place. These can be businesses or marketplaces connecting buyers and sellers of products. 💳 PayFacs  PayFacs are the payments infrastructure that makes it possible for intermediaries to offer payments as part of their end-to-end experience. They play a critical role in enabling embedded payments for software providers and do so by providing payment capabilities that are easy for platforms and intermediaries to set up and start using. ⏱ Acquirers/Payment Processors  Payment processors acquire and onboard merchants and play a central role in payment transactions within this ecosystem. They are specialized entities that connect merchants with card networks, acquiring banks and issuing banks. They have advanced technology infrastructure and systems designed specifically for efficiently handling payment transactions. 🏦 Sponsor Banks  A sponsor bank enables non-bank entities to offer financial services by providing necessary banking infrastructure, regulatory guidance and access to payment networks while facilitating compliance with financial regulations and the management of associated risks. 🙋♂️ Issuing Banks  Issuing banks are an important part of the PayFac ecosystem, as they are responsible for issuing payment cards to consumers. They also manage the authorization, clearing and settlement of digital payments for the cards they issue, while other banks partner with separate issuing processors that do so on their behalf. ⏱ Issuer Processors  The issuing processor connects the issuing bank with the card networks that authorize and settle transactions. 🌐 Card Networks  Card Networks clear and settle transactions between banks and merchants for customers who use their card products to complete purchases. These networks provide a connection between banks, allowing for card credentials to be routed correctly from the merchant's bank to cardholder's bank. In addition, they set operating rules that allow for authorization and approval, maintain security standards and help resolve disputes. Source: PYMNTS - https://bit.ly/3OjYDeG #Fintech #Banking #EmbeddedFinance #APIs #CoreBanking #Cards #Payments #Processing #Acquiring #Issuing #PayFac 

Following the launch of Chat GPT, GenAI has captured all the focus, making us often forget its traditional sibling. Let's take a look how they compare and differ using financial services’ use cases. #artificialintelligence existed long before GenAI. Known under the term Predictive AI, it is all about predicting outcomes, future developments, and forecasting trends. Examples: — JPMorgan Chase utilizes predictive #AI algorithms to detect and prevent fraud (by analyzing patterns of transactions and user behavior to identify anomalies that may indicate potential fraud). — FICO, a provider of credit scoring models, employs predictive analytics to assess creditworthiness by analyzing individual financial behaviors and history, assigning a credit score that helps lenders make lending decisions. — Investment banks like Goldman Sachs use predictive AI algorithms for algorithmic trading. These algorithms analyze market #data, news, and various financial indicators to make real-time trading decisions and optimize investment portfolios. — Morgan Stanley employs predictive AI to assess and manage risks in their investment portfolios. AI models analyze market trends, economic indicators, and other factors to make informed decisions on risk exposure. On the other hand, GenAI refers to deep-learning models that can generate text, images, audio and other media based on vast amounts of data. Examples: — Klarna partnered with OpenAI on a ChatGPT plugin that generates personalised shopping recommendations. Shoppers ask ChatGPT for shopping ideas, and the tool generates recommendations and product links within Klarna's search and comparison functionality. — Brex, a spend platform for companies, uses language processing #technology from OpenAI, that goes beyond automated dashboards and charts to actually create conversations with the tool and to provide insights. — JPMorgan Chase developed a GenAI tool called IndexGPT that automatically analyses and selects investments for customers based on their profile and needs. — OCBC, a Singaporean bank, has rolled out to its 30,000 employees an internal AI-powered chatbot that helps with tasks such as writing, research, summarizing documents or idea generation. OCBC has outsourced a large part of non-strategic decision making to AI, which makes more than 4 million decisions daily, in areas such as risk management or customer service. The use of predictive AI in FS is fairly advanced and its combination with GenAI quite promising, but despite impressive pilot use cases it’s still very early days. Still chances are that transformation will come from bringing the best of both worlds under one roof. Before this happens a series of challenges need to be addressed: 1) data use and availability 2) integration into existing IT 3) business model adjustment 4) workforce upskilling. Opinions: my own, Graphic sources: UK Finance & Oliver Wyman, BCG, Workweek & Alex Johnson