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Typically, the thought of writing a blog post about a spreading database would give me shudders. As someone with far too much experience with Moody's Risk Analyst, I wouldn't think of a spreading database as having much value beyond holding some form of customer financials. 

Not all financial statements are made the same. In fact, almost none of them are made the same! Financial statements are like snowflakes. They come in just as many shapes, sizes, and formats as there are businesses to create them. At first glance this might sound strange. One would think that accepted accounting principles, such as GAAP and IFRS, would help standardize this. However, there is also flexibility left to business in how they share this data.

In today’s world, a superficial reading of tech newsletters and startup press releases leads one to believe we are on the cusp of a machine-driven society. Indeed, “Artificial Intelligence” and “Machine Learning” have become table-stakes marketing terms, with companies like Tesla (autopilot) and Google (duplex) leading the charge toward fully automated solutions to everyday problems. However, if you take a closer look at the technologies you see they are targeted to a specific type of problem (e.g. self-driving and speech emulation) and as such the underlying algorithms are tuned to be successful within those problem spaces. That by no means reduces the impressive results achieved, but the hype around these technologies has created a false belief that anything and everything can be automated. There's a big gap of understanding. 

The banking market continues to change every day. PPP legislation has proven to create moving targets for the financial services industry, forcing lenders to rapidly iterate on their plans and tech companies to support them.

Process innovation has been everywhere since the pandemic began in March. Technology innovation is coming next, and it will transform the way that lenders work with their borrowers. 

Direct lenders are fundamentally in the business of credit risk management. Over the past few decades, lenders have built powerful processes and technologies for evaluating and mitigating the credit risk of potential borrowers. Lenders are now realizing risk management practices can be expanded and augmented by using data to place credit decisions in the context of an investment portfolio. Portfolio-level risk management practices have become table stakes for institutional investors in public debt and equity, but a lack of equivalent data for private companies has prevented the same practices from being implemented by middle market lenders. For financial data to be useful to lenders, it must be processed and structured in a manner allowing lenders to evaluate and respond to real-time portfolio risk exposures.

Well, it might finally be happening. The recession that has eluded for the last 12 years is right on our doorsteps. Recessions aren't new, and the economy will be fine. However, recessions mean that lenders have to work differently. Rather than focusing on speed, competition, and growth, lenders generally have to invest far more time in keeping their existing business safe, rather than generating new business.

The leveraged lending market may have finally met its match in the Coronavirus.

All of us have seen the FinTech revolution that's taken place over the last few years. I never expected to see a FinTech acquire a bank.

There's an unsustainable trend in business lending. The lenders prioritize speed over credit quality.