In my last post, I reflected on the critical difference between Uncertainty and Risk.  Uncertainty is the situation where the likelihood, scale, impact of a negative event cannot be measured.  Risk is the proper term when you can quantify the negative.  More precisely, risk is the metric.

Risk is Good.  Rather, moving from uncertainty to risk is good.  This is what makes economic exchange possible.

In the US, we’ve seen growing momentum for addressing the challenge of “Credit Invisibles.”  These are populations who, for one reason or another, are unable to be scored by the traditional credit scoring agencies.  Without a (risk) score, the lending outcome is uncertain.  As a result, these populations face denials in access to credit: a home loan, an auto loan, a small business loan.

The CFPB finds a staggering 1 in 10 Americans are among the credit invisibles.  That’s 26 million consumers.  Specific sub-populations include young adults without a credit history, immigrants, and minority groups who have traditionally been excluded from full participation in the financial mainstream.

With today’s data revolution, there’s hope to change this situation.  Mainstream financial institutions (banks) and nontraditional lenders all are developing solutions to evaluate the underlying financial condition of a credit invisible applicant, to determine ability to repay, probability for loss, and other factors, using a mix of different data sources and different machine learning models.

The end goal: a systemic approach to transforming uncertainty to risk.

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As a data science-centric company, it’s no surprise we see data playing the lead role in transforming uncertainty into risk.  What might surprise you is how keenly aware we are of the limitations of data.  Especially raw data.  Or data that is one-sided in its interpretation.

We’ve come to recognize that high-quality data results from the act of communication.  It flows out of a relationship.  It strengthens connections between two parties so that their exchange brings greater value to both sides.

It seems like we are swimming in data.  Inundated in all directions from multiple sources.  Data is marketed to us as a raw commodity.  Purchase it in bulk.  Watch your profits grow.

Isn’t this what we want?  Isn’t increased data solving the credit invisibles problem?  Classic informaiton theory reminds us the task is to separate information from noise.  I would say with all these new streams of data, the proportion of noise has only increased

Let me use an example from Fafnir’s current work:  In the small business lending space, there have been a number of companies offering data solutions that center on API integrations.  At its heart, this is what Open Banking is about, using APIs to provide access to bank account data.  There’s a larger world to APIs, so that data from eBay or Etsy sellers or dozens of other online marketplaces can be accessed to analyze a small business’ financial condition.

Often, when people talk about a data revolution, their speaking in reference to API pulls and data aggregation.

The temptation with APIs is to simply flow the data right into a machine learning model.  In other words, apply the hottest software package without regard for domain expertise.  Without understanding the subject’s challenges and circumstances.

One use case for API pulls is to analyze a small business’ cash flows.  We agree this is a great potential application for this type of data.  It’s one we’re pursuing as well.  Our efforts reinforce the need to ask how does the data sourced from APIs align with the business’ current cash flow dynamics?  Their liquidity needs?  Is the data alone telling us the whole story?

Let’s say the data shows a healthy pattern of recurring monthly revenue.  Maybe even a recent uptick in sales.  Is the incoming revenue needed to pay back trade credit for inventory purchases (in other words, to cancel out a pre-existing liability)?  Are the funds applied to an outstanding receivable (transforming accrual income into actual cash)?

Perhaps it’s the opposite: the business operates a successful pre-paid model.  In this case, the incoming funds will trigger new expenses.

Without an understanding of the business’ circumstances, a simple model may come up with a very different picture of cash available to pay debt service.  With significant consequences for credit seekers.

On this same example, one fallacy I’ve seen repeatedly these past two years is the equation of reduced expenses with business survival.  Yes, in the Covid economy, business owners did need to reduce expenses to balance out their lost sales.

But for business owners, expenses mean productive activity.  Wages pay for the people who build your product or sell your service.  Increased wage expense is a response to increased demand.  So does an increase in inventory expense.  Or receivables.

Accounting for each of these nuances in the customer’s experience is an impossible task for a data scientist working in isolation.  The necessary understanding of the customer comes as the product of a relationship.  That’s why Fafnir engages with small business lenders and entrepreneurial support organizations.  We want them as partners in our efforts.  Their relationships with their clients, their customers, helps us complete the picture.

With this approach, small businesses gain value from the data relationship as well.  The outcomes are improved business performance, expanded access to credit.

This probably sounds too easy.  And it is.  Establishing, growing a relationship is the hardest part.  And when that goal is reached, the data journey is just beginning.

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What is the alternative?  Underperforming models that have no hope of achieving breakthrough?

A worse outcome than even this is one-sided control of data.  Privileged access to insights, actionable guidance from data.

This is the dark side to a system that uses data and algorithms to allocate credit.  One-sided data means one-sided growth in value.  Unscrupulous lenders charging exorbitant interest rates to a population that lacks choices.  And doing it in the name of “serving a high-risk market.”

Risk becomes a dirty word again.

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Let’s end this on a positive note.  There is genuine momentum behind expanding financial inclusion, and broad recognition of the transformative power that data and data science can play.  The data revolution is just beginning.  In the end, we expect the real solutions to come out of relationship data, value-creating data.