It is the second week of April and somewhere a credit analyst at a direct lending fund is staring at a spreadsheet with 47 tabs. The quarter just ended. Over the next two to four weeks, this analyst and a small team will manually pull borrower financials from three different custodians, normalize the data in Excel, reconcile it against internal records, and assemble the quarterly portfolio report that their LPs have been waiting for. They will do this for every position in the portfolio. Some of the data will arrive as PDFs. Some will come as CSVs with inconsistent column headers. Some will require a phone call to a borrower who has not returned the last two emails.
This is not one fund. This is an industry managing over two trillion dollars in assets. Capital deployment hit $592.8 billion in 2024, up 78 percent from the year prior. Teams have grown tenfold in a decade. But the operational infrastructure underneath them barely changed. Portfolio data still lives across disparate systems. Compliance reporting is still assembled by hand. And the people managing billions in credit exposure are spending a disproportionate share of their time on tasks that have nothing to do with making good credit decisions.
I have spent the last few years building automation across fintech, venture capital, building operations, and security. The pattern is always the same. Smart, capable people buried in operational overhead that technology solved years ago in adjacent industries. Private credit is the latest place I have seen it, and the gap between the scale of capital being deployed and the maturity of the operations supporting it is hard to ignore.
The Spreadsheet That Runs Everything
Here is the thing about credit fund operations. The spreadsheet is not just a tool. It is the system of record, the workflow engine, and the reporting platform, all in one file. Financial spreading, the process of standardizing a borrower's financial statements into a structured format for analysis, is typically the most time-consuming step in the entire underwriting process. An analyst receives borrower financials, manually enters the data into a standardized template, normalizes line items across different accounting formats, calculates ratios, and builds the credit model. This takes roughly 45 minutes per borrower. Multiply that across a portfolio of hundreds of positions and the math gets painful quickly.
Traditional credit fund underwriting takes four to eight weeks from application to decision. Fintech lenders with machine learning pipelines do it in hours, sometimes within a single day. The same financial spreading that takes 45 minutes manually can be done in under 60 seconds with a trained model. That is not a marginal improvement. That is a different category of operation entirely.
And the data quality problem compounds everything. 76 percent of North American credit fund participants report data quality issues across their portfolios. 62 percent say they lack valuable information about their own credit investments. The data exists. It is just trapped in formats and systems that do not talk to each other. Three custodians, two internal platforms, a shared drive full of files nobody wants to open. When the spreadsheet is wrong, everything downstream is wrong. The compliance report. The LP update. The credit decision itself.
Finding Out a Month Too Late
Covenant monitoring is where the real risk lives. Most credit funds check covenants reactively, at quarterly financial reporting dates. When a borrower breaches a covenant, the fund typically discovers it 30 to 45 days after the quarter ends. That is 30 to 45 days of exposure where the lender has no idea their credit protection has been violated. No alert. No flag. Just silence until someone manually reviews the numbers and realizes something is off.
The technology to do this differently already exists. AI-powered monitoring systems can identify early warning signs of financial distress weeks or months before a formal covenant breach occurs. They watch for declining revenue trends, tightening liquidity ratios, changes in payment behavior, and shifts in peer cohort performance. Automated document processing can validate covenant compliance documents in real time. But adoption in private credit remains remarkably low. The tools exist. The urgency, apparently, does not.
Think about what that gap actually costs. The difference between knowing about a deteriorating credit in March versus discovering it in May can be the difference between a managed workout and a write-off. Between restructuring the loan while the borrower still has options and showing up after the options have already closed. Early detection is not a nice-to-have in credit. It is the entire point of portfolio surveillance. And most funds are doing it with the same quarterly cadence they used a decade ago.
Billions for AI, None for the Back Office
Here is the irony that is hard to look away from. Credit funds are among the most active investors in AI and fintech startups. They deploy billions into companies building machine learning platforms, automated underwriting tools, and data analytics infrastructure. Then they go back to their own offices and manually spread financials in Excel. They fund the future of automation and then operate like it has not arrived yet.
Overall AI adoption in finance is at 71 percent. But private credit lags well behind consumer lending and fintech. A recent survey found that 54 percent of general partners say they plan to upgrade their technology infrastructure within the next two years. "Plan to" is doing a lot of work in that sentence. Meanwhile, 29 percent of credit fund managers cite lack of technology integration between investment analysis and operations as their single biggest frustration. The frustration is real. The action is not.
So why does the status quo persist? A few reasons. Regulatory conservatism plays a role. There is a widespread belief that regulators want to see manual, human-driven processes, even when the regulation itself does not require it. There is the comfort of "we have always done it this way," which is a powerful force in any industry but especially in credit, where the consequences of getting something wrong are measured in millions. There is the genuine complexity of migrating legacy systems that were custom built a decade ago. And perhaps most importantly, the people who would benefit most from automation are the same people who are too busy doing manual work to evaluate automation tools. It is a trap, and it feeds itself.
What the Fintech Side Already Figured Out
Fintech lenders have already solved many of the same problems that credit funds are still struggling with. They process loan applications roughly 20 percent faster than traditional lenders. 73 percent achieve same-day funding using machine learning driven workflows, compared to 31 percent for legacy systems. 67 percent of fintech loan approvals are fully automated, with no human intervention required. These are not pilot programs or proof-of-concept demos. These are production systems making real lending decisions at scale, every single day.
Now, private credit is a different market. Nobody is suggesting you automate a $200 million unitranche facility the way you automate a $50,000 small business loan. The deal sizes are larger, the structures are more bespoke, and the relationships matter more. That nuance is real and it is important. But here is what often gets lost in that conversation: the operational layer underneath the deal does not require human judgment. Financial spreading does not require judgment. Covenant tracking does not require judgment. Quarterly reporting does not require judgment. These tasks require accuracy, speed, and consistency. Exactly the things that automation is good at and that humans, doing them manually at scale, are not.
And the growth trajectory makes this urgent. Private credit is projected to reach $2.8 trillion in AUM by 2028. Some estimates put it at $5 trillion by 2029. You cannot ten-x your assets under management and keep the same spreadsheet-driven back office. The operational model that works at $500 million in AUM does not work at $5 billion. The firms that figure this out early will have a structural advantage in deal execution speed, portfolio monitoring quality, and LP confidence. The ones that do not will be hiring more analysts to maintain the same spreadsheets, quarter after quarter.
Why This Matters
Private credit has become a critical part of the financial system. It is funding companies that banks will not touch, filling gaps in the capital markets, and providing flexible financing to middle-market businesses that are the backbone of the economy. When these funds operate inefficiently, it is not just an internal problem. It affects deal execution speed, credit monitoring quality, and ultimately borrower outcomes. A fund that takes eight weeks to underwrite a deal loses that deal to the one that takes two.
The fund that catches a deteriorating credit early protects its LPs. The fund that can produce investor reports in days instead of weeks builds trust that compounds over time. The fund that frees its analysts from spreadsheet assembly gives them back the hours they need to do what they were actually hired for: making good credit decisions.
The technology is not new. It is not experimental. It is the same automation stack that has already transformed fintech lending, wealth management, and insurance. The question for private credit is not whether it will adopt these systems. It is how much value gets destroyed in the meantime by waiting.
If you work in private credit, direct lending, or credit fund operations and any of this resonates, I would enjoy the conversation.