Banking Software Innovations

Robinhood's CEO just said the most important financial infrastructure of the next decade will look less like a bank branch and more like a developer platform. He's right. But he's only describing half the stack. Vlad Tenev is talking about the transactional layer. Tokenization, blockchain rails, developer APIs for trading and payments. And he's correct - the future of that layer is clearly programmable infrastructure. But transactions are only half of what financial institutions do. The other half is servicing. A borrower calls about a late payment. A collector reaches out about a delinquent account. An applicant needs help completing their application. A customer in hardship needs a payment plan. Most of that is still handled by humans, using the same infrastructure it ran on twenty years ago. If you automate the transactional layer but still need a floor of human agents to handle every inbound call, every outbound collection, every payment reminder - you're only halfway there. You've modernized the plumbing but left the most expensive, most customer-facing part of the operation untouched. The real unlock is fully automated, intelligent servicing. From origination through collections, powered by omnichannel AI agents handling inbound and outbound across voice, email, SMS, and chat. And all powered by an intelligent layer that knows which channel to use, when to reach out, and what offer to make for each individual borrower. That's what we're building at Veritus. Not just voice agents - a servicing brain that automates the entire communication layer of consumer lending with the same precision that Robinhood is bringing to the trading layer.

For twenty years, commercial lending bolted software systems of record onto a hundred-year-old distribution model and called it innovation. Core banking systems. Lease accounting platforms. CRMs. They are designed to store, track, and reconcile what has already happened. The next generation of financial infrastructure is not about recordkeeping. It’s about execution. We are now entering the era of AI-powered systems of action, directly serving borrowers by bringing them online—from initial credit application through final loan or lease payment. A system of action doesn’t wait for employees to: • re-key data, • switch between platforms, • interpret policies, or • initiate downstream workflows. Instead, it acts in real time—at the moment of intent—across origination, credit, documentation, funding, and ongoing loan servicing. Embedded lending enables small and medium-sized business borrowers to self-serve financing transactions end-to-end. Loan and lease workflows are no longer processed by bank employees on behalf of borrowers. Borrowers initiate, complete, and manage transactions directly—instantly, 24/7/365. These distinctions matter enormously in commercial lending. In the $1.34 trillion U.S. business equipment finance market, lending still occurs after a sales process concludes—through disconnected systems, manual handoffs, and delayed decisions. That architecture was tolerable when speed and cost didn’t matter. Today, transactions are processed by bank employees interacting with internal systems, while communicating with borrowers through calls and emails—tracked in Salesforce and other systems of record. This internal busy-work disappears when lending is embedded and borrower-driven. Traditional workflows are a structural constraint. This transition cannot be incremental. It requires abandoning employee-centric workflows entirely. Embedded lending places financing inside the transaction itself, rather than downstream from it. AI enhances this model by enabling decisions and workflows to occur at the point of action, rather than after employee review. Together, embedded lending and AI fundamentally transform how business lending is done. This isn’t about replacing systems of record. Many will persist for some time. But value creation is shifting upstream—to systems that: • put borrowers online end-to-end, • initiate decisions, • orchestrate workflows, • interact directly with customers, and • and trigger systems of record, rather than the reverse. This shift is already visible across global software markets. Having built lending platforms in both eras, I’ve seen firsthand why this transition is inevitable. At QuickFi, we believe embedded lending—enhanced by AI—will define the next operating model for the $1.34 trillion U.S. equipment finance industry. — Bill Verhelle

What if AI could run the entire credit process — from data ingestion to lending decisions — while humans focus on strategy and oversight? In this edition of Oliver Wyman Credit Bytes, we explore what AI-First Credit Transformation could mean for banks. Rather than just adding AI on top of existing processes, there’s an opportunity to re-imagine lending around a super-agent ecosystem, where AI agents orchestrate the credit chain end-to-end within defined risk appetite and human oversight. The potential impact is significant: 🔹 Up to 50% efficiency gains across the credit chain 🔹 10-15% revenue uplift 🔹 Better data quality and faster, more seamless credit decisions for clients Swipe through the carousel to explore the concept and get in touch with our team to discuss how this could work for your bank.

This lender went from 7-day clear-to-close to a 7-day close using agentic process automation. Most "AI solutions" in mortgage are just rebranded BPO operations with fancy interfaces. But one of the largest wholesale lenders just proved AI can actually transform lending operations. Here's what they did: They implemented agentic process automation in their closing department. The results? • Went from 7-day clear-to-close to 7-day total close time • Reduced closing staff from 14 people to just 3 • Maintained compliance and quality standards • Scaled operations while cutting costs Here's why this matters: Most lenders are rushing to implement chatbots and "AI assistants" with: • No clear strategy • No compliance guardrails • No real value proposition But this lender focused on applied AI - automating existing processes with measurable ROI. The key difference? Applied AI takes work you're already doing and makes it more efficient. Like using AI to process closing packages in seconds instead of days. That's real value.

AI in Lending Isn’t Coming. It’s Already Here. Let’s be clear: AI is no longer theoretical. It’s operational. Tangible. Transformational. It’s not “the future of lending.” It’s today’s competitive advantage. Here’s how leading lenders are using AI right now, not in pilots or sandboxes, but in production: *AI-Powered Lead Management Conversational AI is now the “front door” to lending. Borrowers get instant answers, pre-qualifications, and appointments, even at midnight. One lender uses AI to predict with 89% accuracy whether a loan will close on the first call. *Document & Income Automation AI can classify over 1,000 document types in seconds. Income calculations, fraud checks, and inconsistencies are flagged instantly, no more manual stare-and-compare. *Fraud Detection in Real Time AI models are spotting altered documents and duplicate submissions that even seasoned underwriters might miss. *Proactive Servicing Speech analytics detect borrower stress during calls, alerting servicers before a missed payment happens—turning risk into retention. *Predictive Lending Intelligence AI is flagging refinance opportunities before the borrower even thinks to call. Some lenders are closing business before the competition even sees it. *UWM’s “Mia” Chatbot Mia handles borrower questions, schedules appointments, leaves personalized voicemails—and never sleeps. AI isn’t just improving mortgage operations—it’s redefining them. The organizations embracing AI today are: *Cutting costs *Speeding up cycle times *Delivering superior borrower experiences So here's the real question: Is your organization an Avoider, an Experimenter, or a true Leader in AI adoption? The future won’t wait. And the market isn’t pausing. Now is the time to decide: Will you adapt or be left behind? Eric Kujala Paul Orlando Jenna Nelson, CSM Ashley Gravano Fobby Naghmi Kathleen Mantych Ruth Lee, CMB Todd Feager Jake Vermillion Ana Cramer Faith Murphy, CMB® Suzy Lindblom Eileen Andersen Brian Vieaux, CMB Christine Beckwith Julia Brown Stew Scott Ed Kourany Jr., JD, MBA Dana Georgiou, CPLA, CFM Suha Zehl, CMB® Kortney Lane- Schafers

Agentic AI is moving from experimentation to core lending infrastructure. Lyzr AI has been featured by Everest Group under Lending & Credit in their latest view on Agentic Intelligence across Banking & Financial Services. What’s changing in lending is not UI-level automation, it’s the control plane. Agentic systems are now being embedded directly into credit decision workflows, handling multi-step reasoning, orchestration, and execution across systems. Where we see the highest-impact lending use cases today: 1) Loan origination agents: --> Intake → document parsing → data validation --> Cross-system checks (LOS, LMS, CRM, bureau data) 2) Underwriting & credit assessment agents --> Financial statement analysis --> Policy-based and model-assisted risk evaluation --> Explainable decision trails for audit & compliance 3) Credit memo & approval workflow agents --> Automated credit note generation --> Exception routing to human reviewers --> Multi-level approval orchestration 4) Post-disbursal monitoring agents --> Early warning signals from transaction data --> Covenant monitoring and breach detection --> Continuous risk re-scoring 5) Collections & recovery agents --> Segmentation-based action planning --> Personalized outreach strategies --> Compliance-aware engagement flows How Lyzr AI approaches this technically: 1) Event-driven, stateful agents (not stateless prompts) 2) Policy-aware execution with auditability by design 3) Secure, private deployment aligned with BFSI compliance 4) Integration-first architecture across LOS, LMS, core banking, and data platforms 5) Human-in-the-loop controls where regulation demands it As banks and NBFCs move beyond rule engines and point automation, agentic architectures are emerging as the next abstraction layer for lending systems. Grateful to the Everest Group team for the recognition and excited about what agent-driven lending unlocks next. #AgenticAI #BFSI

Casca just raised $29 million in Series A funding, and if you think that's just another fintech headline, you're not paying attention. This is a company born in 2023 out of Stanford University and Y Combinator's Summer batch that has gone from late-night coding sessions to landing banks as both #customers and #investors. Live Oak Bank, Huntington National Bank, and Bankwell didn't just sign up for the product, they wrote checks to fuel it. That's the equivalent of Vegas letting the card counter keep playing because the pit boss knows the math is real. Led by CEO and co-founder Lukas Haffer and CTO and co-founder Isaiah Williams, Casca isn't another layer of software slappes onto legacy systems. It's an AI-native #loanorigination platform built from the ground up to kill the inefficiencies banks have just learned to tolerate. We're talking about automating 90% of the manual grind, funding loans ten times faster than fintech peers, thirty times faster than industry averages, and trimming days off the process. #Loanofficers save 20 hours a week they used to waste on document collection, conversion rates triple, and borrowers actually get answers in human time instead of bank time. This Series A, led by Canapi Ventures with participation from Live Oak Bank, Huntington National Bank, Bankwell Bank, Y Combinator, Peterson Ventures, and Alliance Funding Group, brings Casca's total to $33 million. The fact that it closed just 15 months after their $3.9 million pre-seed tells you everything you need to know about momentum. Even more telling is that institutions defined by #riskmanagement are leaning into #AIinfrastructure crafted by two founders who know both the guts of #bankingIT and the edge of #machinelearning. That is not hype, it is hard-earned validation. Casca has already proven it can read and analyze 10,000 pages in five minutes, generate documents instantly, run automated KYB checks, and carry out real-time loan discussions through its #AIassistant. The banks aren't betting on a prototype; they are betting on the next default OS for #lending. Add in a Best of Show win at Finovate Spring 2024 and traction with the country's top SBA lenders, and the signal is loud: the industry's center of gravity is shifting. This round fuels expansion, hiring, and product evolution. The target is clear, bring community banks, regional players, and national lenders into the fold and open up faster access to capital for more than 30 million small businesses in the U.S. The future of lending isn't inching forward, it's being pulled forward at the speed of Casca. #Startups #StartupFunding #VentureCapital #SeriesA #AI #Banking #BankingTech #Lending #LendingTech #FinTech #Infrastructure #Technology #Innovation #TechEcosystem #StartupEcosystem #Hiring #TechHiring If software engineering peace of mind is what you crave, Vention is your zen.