Weekly readings – 11th July 2020

What I wrote last week

I wrote a bit about the challenges of corporations in addressing different stakeholders’ needs

Here is a what I wrote about the company behind FICO score

My thoughts on the latest suspension of H1B visas till the end of the year, a self-inflicting move by the US

Business

How I grew my Shopify micro-SaaS to $25k MRR and 20k users in 14 months

A very good analysis on Twitter, discussing the company’s valuable network and challenges

Exclusive: Inside Uber’s billion-dollar bet to deliver food, people, and everything else

Technology

The Post-Covid-19 Agenda for Technology and Media Companies.

What I think is interesting

How to understand things

Charlie Munger: Turning $2 Million Into $2 Trillion

Peter Kaufman on The Multidisciplinary Approach to Thinking: Transcript

In Praise of Idleness

Growth without goals

Money Is the Megaphone of Identity

This company affects your life every day. Get to know it!

FICO

Most of the US consumers should be familiar with FICO. Credit score is a metric that tells lenders how likely you are going to pay back loans. The score is widely used by banks or lenders in the country for credit cards, mortgages and other types of loans. The most popular score is FICO and it’s developed by a company called Fair Isaac & Company. Take the initials of those three words and you arrive at FICO. Typically, FICO ranges from 300 to 850. The higher your FICO is, the more trustworthy you are in the eyes of lenders and hence the lower interest rates may be.

Figure 1 – FICO Range. Source: MyFICO
How FICO Scores are calculated
Figure 2 – How FICO is calculated. Source: MyFICO

The current version FICO is FICO10, but there may be other clients that are still using legacy FICO9. According to FICO, the new score was developed off of FICO9 and offered significant benefits

We also announced some innovations in our Scores business, the release of the FICO Score 10 suite. The suite has 2 new scores. FICO Score 10 relies on credit bureau data and is consistent with previous FICO score versions that are in the market today. It reflects a normal model development cadence, extending features that were introduced in FICO Score 8 and FICO Score 9. FICO Score 10 is designed to be backward compatible with previous Score versions. FICO Score 10T incorporates a broader set of credit bureau data, including trended data, which captures unique aspects of the consumer’s financial profile over time. While the blueprint design is similar, it uses new characteristics to enhance predictive power. Both FICO Score 10 and FICO Score 10T demonstrate greater predictive power over all previous versions of the FICO score and were developed on recent data sets. By adopting the FICO Score 10 suite, a lender can reduce the number of defaults in its portfolio by as much as 10% among newly originated bank cards, 9% among newly originated auto loans compared to using FICO Score 9. The reduction in default is even higher for newly originated mortgage loans at 17% compared to the version of the FICO score used in that industry. These improvements in predictive power can help lenders safely avoid unexpected credit risk and better control default rates, while making more competitive credit offers to more consumers.

Source: FICO’s Q1 2020 Earnings Call

FICO is pretty popular among consumers and lenders. According to a presentation by FICO in 2019, 90% of the US credit lending decisions involved FICO scores and 90 out the largest 100 US lenders use FICO scores. 300 consumer accounts have access to free FICO scores

Fair Isaac & Company

In 1956, an engineer named Bill Fair and a mathematician named Earl Isaac founded a company together on the belief that data could improve business decisions, if used properly and wisely. They were ahead of their times, weren’t they? In 1992, FICO risk scores were made available to three major reporting agencies: Equifax, Experian and TransUnion. In 1995, Fannie Mae and Freddie Mac recommended the use of FICO in evaluating mortgage loans. (Source: FICO)

The company generates revenue from both consumers and corporate clients, but most of the pie comes from the latter. In addition to the popular scores, Fair Isaac & Company also offers other services such as Applications and Decision Management Software (DMS). While the names may indicate the same services, there is a major difference. Applications refer to FICO’s packaged software that meets specific-industry needs whereas DMS consists of tools that clients can use to build tailored apps that have the same function as applications. In 2019, Applications and DMS combined for 64% of FICO revenue (52% and 12% respectively) while Scores was responsible for the other 36%. In terms of YoY growth, the company recorded 16% growth in 2019, compared to 7% in 2018. Applications revenue was about $605 million, a 7% growth. Scores notched about $421 million in revenue, a 25% growth, while DMS brought home approximately $134 million, a 34% growth.

Figure 3 – FICO 2019 Revenue Breakdown. Data source: FICO 2019 Annual Report
Figure 4 – FICO 2019 Segment Revenue Growth. Data source: FICO 2019 Annual Report

Under the three main segments, there are three sub-segments: transactional & maintenance, professional services and license. Transactional & Maintenance Bookings are on transaction basis. The more transactions there are, the more revenue FICO has. Professional services refers to “he estimated number of hours to complete a project multiplied by the rate per hour” while “Licenses are sold on a perpetual or term basis and bookings generally equal the fixed amount stated in the contract.”

The composition of the main segments differs from one to another. Transactional & Maintenance made up the most revenue for Scores in 2019 while Professional Services and License occupied 33% and 29% of DMS’s revenue. Growth behavior of each sub-segment under the main segments also varied. While license recorded the biggest expansion across all segments, Scores’ 2nd biggest growth came from Transactional & Maintenance and that of DMS came from Professional Services

Figure 5 – FICO Sub-segments’ Revenue Breakdown in 2019. Data source: FICO 2019 Annual Report
 ApplicationsScoresDecision Management Software
Transactional & Maintenance6%25%8%
Professional services-4%14%38%
License47%62%86%
Segment Total7%25%34%
Figure 6 – YoY Revenue Growth of Sub-segment under the main lines. Data source: FICO 2019 Annual Report

Although FICO doesn’t break down operating margin for the sub-segments, it does provide operating margin for the main business lines. Scores is the most profitable while DMS is the only unprofitable line. Looking at the composition of the lines, it’s not surprising that is the case. Transactional & Maintenance should have low marginal costs. FICO only generates more revenue and operating income as it signs up more clients and records more transactions. As Professional Services tend to have low gross margin due to high labor costs and Professional Services makes up a third of DMS’s revenue, it’s entirely possible that high SG&A and Sales & Marketing expenses sunk the segment’s profitability.

Figure 7 – Operating Margin in 2019. Data source: FICO 2019 Annual Report

A few other notable stats:

  • 34% of FICO revenue came from outside the US
  • Banking industry is responsible for 87% of its 2019 revenue
  • Commercial agreements with the three main reporting agencies made up 29% of FICO’s revenue (13% from Experian)
Figure 8 – Deal information. Data source: FICO Q1 2020 Financial Highlights Presentation

As a customer myself and somebody who works at a bank, FICO is instrumental in not only risk management, but also marketing. Given the popularity of the FICO risk scores and the proprietary nature of those scores, the company should be able to reap benefits for a foreseeable future. With regard to its solutions, FICO has some stiff competition and frankly, I have no idea how it will fare in the future. I like to get to know different types of business because that’s part of my investment effort and because I am just curious. I hope this post offers you a helpful primer on a company that is under-radar, yet plays a role in our life.

Complexity of credit card data

This entry is just a little bit of what I have experienced after more than one year working at a bank. By no means am I trying to imply that I am an expert in credit card data. Not even close. This is simply to shed some light or pull the curtain a little bit into the world of banking. It’s also a way to put into writing what I have learned or think I have learned so far.

Lost and stolen

One reality that makes compiling and analyzing credit card data complicated and tricky is that people lose their cards or have them stolen all the time. When that happens, banks issue new cards to customers with totally new account numbers. On the front end, customers don’t see any change. New cards just work like old cards. However, on the back end, it’s a totally different ball game. If an analyst like myself wants to analyze performance of cards, especially accounts that have a complicated history, such as spend, balance and profit history, a reconciliation of all the account numbers associated with the original account number can be daunting and easily lead to mistake.

Tricky balance between risk and profit

If you are like me who uses cards regularly and pays off balance every month to avoid financial charges, you are not a profitable customer to credit card issuers. To make money off customers, issuers usually charge interests on late payments and fees such as annual fees, foreign transaction fees, over limit fees. No late payments mean no financial charges and no profit. On the other hand, customers who are usually late on payments and accrue balance are usually the riskiest bunch. Such customers, as we call revolvers, are more likely to charge off and cost issuers money. As a result, issuers must find a sweet spot between risk and profitability. Having the majority of customers under 680 FICO can lead to higher profitability, but also higher risk at the same time. Acquiring great credit customers poses a lower risk, but also a lower chance of profitability.

One popular practice in the credit card world is balance consolidation. In a layman’s term, it is an offer allowing customers with a balance at a higher interest rate to transfer the balance to another account at a substantially lower rate which can be 0% for several months. This offer appeals to credit trapped customers who are likely to be late on payments, but want to avoid interests. By taking on more accounts and balance, issuers take on more risks, but balance consolidation may not always lead to higher revenue and profit.

Ambiguous definition

Sometimes, metrics in the credit card world may not be as straightforward as they sound. For example, if a customer calls to close a credit card, but chooses to pay balance in installments instead of one big payment, is that credit card considered closed or still open with an outstanding balance? When discussing “active” accounts, are we talking about “balance active” (having balance > 0) or “debit active” (having net purchase which is comprised of purchases minus refund > 0)? When discussing spend per account, are we talking about spend per account or spend per “debit active” account only? Those are just a few examples on top of my mind. There are plenty more cases where a question, straightforward as it may seem, is really not.

Apple to apple comparison

A performance analysis needs a benchmark to compare against. To analyze a cohort of accounts, it’s important to find another cohort that is as similar characteristically as possible to the one at hand. The challenge is that there are multiple elements to consider such as acquisition channels, vintage (when accounts were opened), cashback or rewards cards, FICO, credit bureau segments (more on this later), brands that cards are associated with (FCA, Delta Airlines, Walmart…) and so on. If the task is to perform an analysis on the same set of accounts over time to test the effect of an offer, seasonality needs to be considered. Obviously, consumers spend more during holidays. So, comparing spend in Dec to spend in Feb isn’t really a fair one.

External data

Issuers work with agencies such as Acxiom to acquire demographic data. To my best knowledge, issuers send account information such as addresses and names to Acxiom to match up with their demographic database such as household income, net worth, how likely they have a mortgage, how likely they have children, how they are etc…Such information is sent back to issuers for future marketing purposes. One of the challenges arising from this practice is that an account can have more than one user. Joint accounts can have two users whose demographic profile can be different. In that case, a decision has to be made as to which profile should be used and data needs to be handled with care. Otherwise, it would lead to double accounting.

Issuers can work with credit bureau agencies for credit score and other information. An individual banks with multiple corporations and has multiple cards with different issuers. It’s helpful to know how big of a customer’s wallet share your issuer makes up. For instance, it’s helpful to know how much a customer allegedly tends to spend in a year and the percentage of that spend goes to your issuer. The same goes for all credit card balance and revolving balance. Agencies such as Experian receive data from different issuers and return their estimated data back to issuers for marketing purposes.

Another challenge is that between the time when data is sent to external data partners and the time when data is sent back to issuers, accounts can be lost or stolen. Reconciling that change can be problematic.

I have worked intensely at my current company for the past year. It’s really satisfying to learn how data works, what goes under the hood, what the nuts and bolts are and what can be done to help the business. There are a lot to learn and do such as

  • How to optimize the acquisition process? How to get the best response rates for Direct Mail, an important practice in this industry
  • How to encourage spend?
  • How to balance risk and profitability?
  • Who are the “best” customers?
  • How to use data to improve customer experience
  • How to improve interchange?
  • How to predict charge off?
  • How digitally engaged are customers?

I hope this entry has been helpful somewhat. If I miss anything or any point is incorrect, please leave a comment. I hope I’ll learn more and be able to share with you in the near future.