In this excerpt from the latest Franklin Templeton Thinks, Franklin Templeton Fixed Income Group examines how machine learning techniques can measure the risks of consumer and home loans—helping pinpoint credit risks they think are worth taking. They review digital loans, a relatively new asset class, and explain why quantitative approaches may be more applicable to some fixed income sectors than others.

In today’s world of new technologies, it’s easy to grasp how digital innovations touch our personal lives. We can order groceries with a simple voice command, leave tips with our phone, or apply for online loans without leaving our couch. Less obvious are the ways data science and digital analytics have transformed the methods some asset managers use to analyze risks and generate returns.

The goal of risk analysis isn’t to avoid risks. On the contrary, generating positive returns over cash requires taking some risks. The chief job of a fixed income manager involves distinguishing which risks are more likely to pay off for investors versus those that probably won’t.

Using predictive algorithms—statistical modeling techniques that forecast outcomes—we can quickly analyze thousands of loans to spot ones that we believe offer better risk profiles.

Don’t Worry: Machines Are Watching You

Algorithms written in computer code are ever-present these days, predicting our behaviors. Some forecast how we’re likely to vote, while others anticipate our next purchase on websites like Amazon or Taobao in China. If you’ve used a credit card recently, it’s a certainty that computers are analyzing all your transactions. Not necessarily to send you tailored marketing promotions, but for your protection.

Credit card companies like Visa and Mastercard use machine learning tools to stop fraudulent charges that you might otherwise be obligated to pay. By monitoring your charges, self-improving algorithms can pinpoint suspicious patterns much faster and cheaper than humans can.

Machines aren’t just analyzing your spending or scrolling patterns, however. Advanced algorithms can also measure your creditworthiness—the likelihood you’ll pay back a loan—often with greater predictive accuracy than rudi­mentary credit scores like “SCHUFA” in Germany or “FICO” in the United States. These algorithms have given technology upstarts a leg up over traditional banks, many of whom still rely on one-dimensional credit scores. In emerging economies, these credit-scoring algorithms are a boon for millions of small businesses and consumers for whom brick-and-mortar banks are still out of reach.