Why Everybody is Talking About Lending Technology These Days 

Articles Digital Lending

The 2020s were already destined to be a time of profound change for businesses, but Covid-19 clinched it.  

Just weeks after the World Health Organization declared the coronavirus pandemic in March 2020, businesses began accelerating uptake of hardware as well as conferencing and work-sharing software to support employees suddenly required to work from home. Meanwhile, consumer- and business-facing enterprises sped up plans for payment methods — and in many cases, in-house financing tools — conducive to social distancing, and dependent on digitalization. 

Covid was more of a spotlight than a catalyst  

“It’s unlikely we’ll ever untangle the mystery of which technological innovations came to the fore because of Covid and which were in line for prominence anyway, but it’s possible digitalization falls into both categories,” says Dmitry Voronenko, CEO of lending-technology pioneer TurnKey Lender. “Interest in using digital technologies to enhance entire business models, augment value, and provide new revenue streams has been accelerated by the pandemic, and it remains an inevitable part of a fast-maturing ‘internet of things’ in a post-Covid business landscape.” 

In this article, we’ll mine research firm Gartner’s recent intelligence on new technologies and business rationales central to trends in financial technology, and especially lending tech. Our purpose here is to help owners and executives put recent and impending developments in financing technology in a sharper perspective — and help them understand why, as Gartner asserts, 57% “of financial services firms see technology giants or fintech startups as a major threat to the industry.” Only 49% of these firms pointed to traditional competitors as “major threats.”  

To start, let’s examine some of Gartner’s most arresting predictions linked to the rise of nimble new financing technologies. The firm says that: 

  • By 2025,  at least 40% of customer-facing staff will engage with external ecosystems directly to support client preferences and service their banking needs 
  • By 2023, 25% of automation business cases will fail because they are based on staff reduction rather than customer satisfaction or new revenue 
  • By 2025, more than 50% of financial services supporting vendors will offer no-code or low-code tools to enable non-IT employees 

In addition to these marquee predictions, Gartner sees the following developments playing out with reference to smart, tech-enabled financing. 

Traditional lending will die out  

With interest rates at or under zero around the world, banks are struggling to make any money at all from lending. Hard-pressed to achieve every scrap of efficiency they can muster, these institutions are looking to digitalize every stage and aspect of lending, from application to the loan’s retirement. 

Centralization will increase 

Banks whose core systems have tracked consumer behavior for at least 10 years have found that 80% of their customers use 20% of their defined products, adding weight to the argument that localized functionality should be eliminated where not required, and externalized from the core as needed. 

Digitalization will add revenue 

With a view to accelerating digitalization, and generating new revenue by providing technology services to external customers10% of large traditional companies will have established in-house tech businesses by 2022. It’s not hard to imagine that retailers and B2B players will lead the way toward in-house digital lending in a bid to add sales and increase efficiency.   

Artificial intelligence will get more granular 

Artificial intelligence isn’t science fiction anymore; it’s a pragmatic toolset for achieving specific strategic outcomes. Among businesses surveyed by Gartner, the main drivers for adopting AI are: 

  • Improving customer service/experience – 43% 
  • Increasing efficiency – 24% 
  • Improving risk management – 19%  (up from 7% in early 2020) 
  • Reducing cost – 8% 

Another big jump occurred in uptake around “intelligent applications”– apps primed to learn — which went from 1% of new AI engagement to 7% in 2021.  

Key to broader uptake of AI by banks is public confidence in these institutions. In 2019, Gartner found 67% of consumers trust banks to keep their personal data secure. For comparison, only 19% insurance companies enjoyed such confidence among consumers. 

Returning to risk for a moment, the top risk-management applications for AI are: 

  • Anti-money laundering 
  • Fraud management 
  • Customer churn prediction/prevention 
  • Credit scoring 
  • Mortgage default production 
  • Threat detection and forensics 
  • Portfolio credit risk optimization 

Learn how TurnKey Lender uses AI in lending automation.

AI will become more “democratized”

Generally, the public’s understanding of AI is improving — perhaps even undergoing a process of “democratization,” resulting in more minds concentrated on its potential, and more demand for AI in work-related processes. As Gartner puts it, “Democratization of AI means that AI is no longer the exclusive preserve of data scientists and AI professionals.”  

Increasingly, organizations lacking in-house expertise have access to cloud-based teaching kits, AI marketplaces, and turnkey programs to make AI available to them in digestible bites. “Most of these technologies have ‘high’ impact and mass, meaning their adoption will affect many verticals and transform business processes,” says Gartner. 

Varying types of machine learning will proliferate 

Meanwhile, “machine learning” — “computer algorithms that improve automatically through experience and by the use of data,” — remains the dominant AI-related technology, says Gartner. But the firm sees subdivisions emerging from the broad ML category. Its three parts are:  

  • Now-dominant supervised learning, in which observations contain input/output pairs of “labeled data” 
  • Unsupervised learning, in which labels are omitted 
  • Reinforcement learning, which provides qualitative assessments of how good or bad a given situation is 

To take advantage of the emergence of AI as a commercial force, Gartner recommends that banks eager to extend credit to consumers: 

  • Convert lending activities into financing activities and projects 
  • Replace the revenue from interest rates with a new revenue stream from new services 
  • Increase commission income 

“This applies as well to other businesses that traditionally extend credit — anything from dentists and eye doctors to heavy-equipment manufacturers, trade-loan brokers, and hardware stores,” says TurnKey Lender’s Voronenko. “Everything they need to make secure, bank-grade financing a 100% in-house offering is on the market today, ready for them to sign on and start giving their customers a twenty-first-century experience.” 

Reach out and schedule a personalized TurnKey Lender demo to start your intelligence-driven digital lending journey today.

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