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Category: Banking

Digital Lending Growth Fueled by Disruptive Business Models

Digital lending

Digital lending is a small but growing market in the U.S., and a thriving ecosystem of fintech companies has arisen to fuel that growth. The innovations and disruptions being introduced in this space have spread to other market segments and industries as they offer consumers and business borrowers advantages over loans through traditional channels.  Digital lending technology offers advantages, the main being lower borrowing rates. Online lending platforms can reach a large consumer base online. They have lower overhead (perhaps as much as 40 percent lower, according to McKinsey & Company), and those savings are passed on to borrowers in the form of lower rates. Other advantages are faster approval and faster loan disbursements.  The digital lending market includes numerous lending models, including commercial and consumer lending, peer-to-peer loans (P2P), buy-now-pay-later (BNPL), and crowdfunding. Many fintechs as well as traditional financial companies like FIS (NYSE: FIS) and Fiserv (NYSE:FI) have either launched new products or started companies to compete in these markets. Loans made through these platforms vary in size, but they are usually small business or personal loans under $1 million, though the institutions enabled via FIS and Fiserv may make larger loans through their platforms.  The volume is still a small fraction of the overall lending market.  Estimations of size vary, but most peg the global digital loan industry around $10-13 billion in revenue, and expect growth between 10-20 percent CAGR. Allied Market Research estimated its size was $12.6 billion in revenue in 2022. The North American digital lending market was $1.7 billion in 2021, according to DataBridge Market Research. The research firm estimates it will grow to $7.6 billion by 2029, a 77 percent increase.  By contrast, U.S. banks alone added $1 trillion in loans to their books in 2022. According to FDIC data, U.S. banks reported $12.2 trillion in total loans in December, 2022, up from $11.2 trillion in 2021, nearly 10 percent annual growth. So, by volume, the digital lending market is quite small.  That said, the digital lending market has many examples of disruptive ideas and business models that have spread throughout the financial world. A blog post on the website BuiltIn offers an excellent roundup of digital lending disruptors. A few examples:  If you can’t beat ‘em, join ‘em  You may have noticed a trend from the examples above: many digital lending platforms use traditional banks. Also, many banks have launched digital lending platforms like eClosing apps for mortages. So, the traditional lending and online lending worlds may not be so far apart. American Express purchased one of them. In 2020, it acquired online lender Kabbage, and replaced it with its American Express Business Blueprint service.   The digital lending market has its limits, though. In a recent survey of mortage banks, Dutch global consultant Wolters Kluwer noted headwinds among banks conducting eClosings and using electronic promisory notes (eNotes), specifically mentioning “lagging investor and warehouse lender acceptance [of eNotes] ” as impediments to “full digital transformation.”  “I don’t always know who I’m going to sell to when I originate a loan,” said one of the survey respondents in a quote published on Wolters Kluwer’s website. “So, I’m not going to close with an eNote and then find out that could have gotten 70 [basis points] more by going to an investor that doesn’t buy eNotes.”  Despite setbacks such as these, nearly a third of the survey respondents said that the overall decline in mortgage originations in 2023 would only increase their implementation of “digital initiatives.” 

Is it AI or Bust for Banks?

Introduction of ChatGPT Extends Need for Banking Industry to Embrace AI

Introduction of ChatGPT Extends Need for Banking Industry to Embrace AI ChatGPT’s launch and rapid mass adoption was one more reminder for the banking industry that artificial intelligence is becoming critical for business.  AI’s use cases cover such a broad swath of the banking industry that it’s hard to find an area that isn’t being affected now, or won’t be in the near future.   Take fraud detection, for example: AI can use troves of historical transaction data to find hidden patterns that fraudsters leave behind. Or credit scoring: AI and machine learning models can quickly sift through huge sets of data to give a more accurate picture of a borrower. Generative AI – an artificial intelligence system capable of generating original content – can create copy and images for ad campaigns, marketing material, or investor material. It can even write code to analyze market data or prepare it for a system to assist in portfolio optimization. ChatGPT went viral by holding realistic human-like conversations through an online chat window, and it can hold its own when discussing portfolio allocation, for example. There’s so many uses for the banking industry that we’ve rounded them up in a separate article.  Further, every bit of information entered in a text box through a system like ChatGPT can be stored and used to train its next version and learn more about its customers, creating a virtuous cycle for any bank that can successfully (and responsibly) harness such a system.  In his most recent annual letter to shareholders at the end of 2022 (delivered as ChatGPT began to capture public imagination), JPMorgan Chase CEO Jaime Dimon underscored the importance of AI.  Dimon further stated that his bank is investing heavily in AI because he knows it has “already added significant value” to his company. “For example, in the last few years, AI has helped us to significantly decrease risk in our retail business (by reducing fraud and illicit activity) and improve trading optimization and portfolio construction (by providing optimal execution strategies, automating forecasting and analytics, and improving client intelligence).”  In a report this past spring, consulting firm McKinsey & Company quantified the impact that generative AI could have on banking.  “Banking, high tech, and life sciences are among the industries that could see the biggest impact as a percentage of their revenues from generative AI,” the report read. “Across the banking industry, for example, the technology could deliver value equal to an additional $200 billion to $340 billion annually if the use cases were fully implemented … Current generative AI and other technologies have the potential to automate work activities that absorb 60 to 70 percent of employees’ time today.”.”  In a separate report on AI, McKinsey outlined where some of that value from generative AI will come from:  McKinsey pointed out that banks that lead in customer satisfaction grow deposits faster: from 2014-2017, banks with top customer satisfaction scores grew 84 percent faster than those in the bottom quartile.   “To remain competitive, incumbent banks must become ‘AI first’ in vision and execution,” the consulting company said.  That may be easier said than done for some banks. To take advantage of AI systems and generative AI, data must be in a system that can communicate with the AI models and algorithms. Increasingly, that system must be an enterprise cloud system that can ensure data regulatory compliance while also seamlessly connecting with the APIs of AI systems. Microsoft’s Azure Stack is an example of such a cloud system.  Risks and Rewards of Generative AI  For banks that can leverage the flexibility of cloud systems, there are numerous other issues to consider with generative AI as well, like customer data. Protecting that data is always major concern for banks as they must protect it in order to comply with a number of financial regulations and agencies in the U.S. and abroad.   The possibility of confidential or private information finding its way, even unintentionally, into a public system like ChatGPT had stoked fears among some of the country’s biggest banks, and earlier this year some banks had responded by restricting employee access to ChatGPT, including JP Morgan Chase, Goldman Sachs, Citi, and Wells Fargo. Also the FTC opened a probe in July into a leak of personal data on ChatGPT as well as inaccuracies on the platform.  Proper handling customer financial data and related compliance rules mean that most banks are likely to experiment with GenAI in the back office before rolling out AI-enabled chat bots to their customers. Dan Faggella, Head of Research and  CEO at Emerj Artificial Intelligence Research, used a poignent analogy in a recent Tearsheet article to make this point.  “If I’m ordering a pizza, and you have a bot that’s going to reply to me when I’m ordering, there’s very few ways you can go to jail. But if you’re a bank and your chat bot says the wrong thing, maybe you’ll go to fricking jail,” Faggella said.  To address concerns such as these, OpenAI introduced its own enterprise version of ChatGPT in August with the promise to protection of corporate customers’ private data. ChatGPT Enterprise will not use customer data to train the model or to learn from customer conversations, the company said.  News of ChatGPT Enterprise will be a major relief for banks looking to leverage the platform, yet other risks remain for banks besides the handling of proprietary data. These risks include errors, bias, and even copyright infringement that bank CIOs and CCOs may need to consider before connecting their systems with a public AI chat bot like ChatGPT Enterprise or Google’s Bard. We have rounded up -up many of these risks in our article “The Ghosts in the Generative AI Machine.”  Risks aside, for those banks that can leverage the flexibility of cloud systems while navigating the risks that GenAI poses, there are options. OpenAI’s paid version of (non-enterprise) ChatGPT offers API access and is only $20 per month, so the cost of experimenting with the system is