The behavioural analytics blindspot undercutting creditworthiness

Credit history has long stood as the grand gatekeeper of financial opportunity. It determines who gets a loan, a mortgage, a credit card. It acts as the arbiter of an individual’s financial trustworthiness, but its basic structure rewards time as opposed to behavior. In a fully digitized world, this model has rapidly become an insufficient metric. Millions of would-be creditworthy individuals are completely locked out because they lack the opportunity to build a traditional history. In short, creditworthiness has a blindspot problem. 

Tossing out credit history altogether is not the solution. Methodology based solely on credit behavior indicators may work for severely underbanked populations, but in many cases we should be augmenting credit history with these behavior-based methods. Creditworthiness for anyone could one day encompass both history and behavior. Maybe it should. 

Credit history’s stodgy challenges 

Traditional credit scoring is a combination of timelines. Data includes how long someone has had a credit account, how long it’s been since they defaulted, and how consistently they’ve made payments over years. All of these are somewhat reliable indicators of whether or not a person is trustworthy, which is why they’ve been so popular for so long.

While recent innovations like adding trended credit card data, Buy Now Pay Later (BNPL) repayment history, or cash flow underwriting data do make traditional credit scores more accurate, these methods primarily benefit individuals who are already part of the mainstream system and have an existing credit file. They deepen the assessment for the ‘credit visible’ but fail to address the fundamental challenge of the credit invisible. As a result, these approaches do not bring new people into mainstream financial services or help solve the core problem of financial exclusion.

Many people with no credit history, however, are no less trustworthy, although they are treated as high-risk by default. And there are many people who fall through this particular crack. An estimated 25.9 million Americans and 5.6 million Britons are credit invisible. This isn’t the result of poor financial decisions they’ve made but because they’ve had no opportunity to make any at all. This zero-score scenario sticks, and unwinding it takes years of incremental progress, assuming one can even access credit-building tools in the first place. That timeline is unrealistic in a rapid, digital, inflation-challenged economy. It’s also somewhat unjust. Letting behavioral analytics make up the difference is crucial. 

Behavior plus time equals better analysis 

On its own, credit history only indicates how an individual has previously behaved. It lacks a real-time, present-day element. Consequently, even individuals with excellent traditional credit scores may still experience payment defaults or delinquencies. Statistically, these are false positives—borrowers deemed creditworthy by historical standards who ultimately underperform. This is a critical blind spot than behavioral analytics can fill. Behavior from ten years ago is a far from perfect indicator of future intent, discipline, and reliability. But digital behavior metrics pulled from a person’s phone that same week gives a surprisingly accurate reflection of the nature of their habits and decisions. With the right methodology and guardrails in place, this data can be used to make better credit decisions for more people. 

Device-level analytics also build on an existing high-penetration reality. According to the World Bank, 900 million adults in low- and middle-income countries who don’t have financial accounts still have a mobile phone, including some 530 million with smartphones. To that end, the number of adults worldwide who don’t have any kind of financial account is rapidly dwindling. The same World Bank study found that 80% of adults worldwide have at least one financial account, and 40% of adults in developing economies saved in a financial account as of 2024.

This global increase in financial account ownership bodes well for the potential comprehensive combination of credit history and behavioral analytics. Behavioral analytics provides a crucial supplement to, or replacement for, traditional credit scoring by adding a predictive layer based on device usage. This method works by establishing a statistical correlation between the observed patterns of a genuine customer’s smartphone and device interactions and the distinctive device footprints associated with confirmed fraudulent or high-risk accounts. 

What behavioral data can (and can’t) show

When anonymized, behavioral scoring isn’t inherently invasive or reductive. It’s also much broader and more nuanced than most people realize.

It measures behavior that’s directly applicable, such as whether or not someone reads the terms and conditions on a loan application or skips through quickly, and then whether or not they finish the application in one sitting or over multiple sessions. But it also can see things like gambling app use, or erratic spikes in general device activity that could indicate device sharing or fraud.

In isolation, none of these indicators tell a full story, nor will they make or break a credit application. Taken together, which is to say in the millions of metadata features, they’ll create a highly predictive pattern of risk and intent. 

The metadata provided by analyzing device usage is incredibly useful, even for something as simple as monitoring a basic savings account. Behavior analytics can close the gap using this data, which then puts people on the road to generating traditional credit history. Through this combination, a person can simultaneously build trustworthiness in real-time and over an extended period of years. 

The financial inclusion springboard

After decades of efforts led by governments, NGOs, and banks, financial inclusion has made major strides in the last decade alone. The work is far from complete, but global finance has never been more accessible than it is right now. These efforts, combined with behavioral analytics that work with anyone who owns a smartphone, are the key to finishing the job.

Ultimately we want to be in a place where behavioral analytics can close the gap for credit invisible people. The sooner that gap is closed, the sooner traditional credit scoring can kick in for everyone. Once that happens, real-time behavioral analytics and extended credit history can be combined to give lenders incredibly reliable portraits of an individual’s overarching creditworthiness, assessing both capacity and character, from the past to the future. This is the backbone of a more intelligent lending system.

None of this is speculative. Lenders currently using behavioral data are seeing better prediction accuracy, lower default rates, and improved customer acquisition in underbanked markets. It only makes sense that they would extend that to all customers living in a financial world where behaviors are dynamic, distributed, and digitized.

The future of credit scoring isn’t binary, and it won’t abandon history for behavior. Instead we’re moving towards a hybrid model that mines the value of past performance and the insight of present behavior. By expanding our definition of what counts as creditworthiness, we can unlock opportunity for millions and eventually better serve people who were never credit invisible. A high tide—in this case, a more inclusive global financial system—really does raise all boats.

Quote from the Author:
“The future of credit scoring is about combining past performance with real-time digital behavior to close the blind spot that keeps millions of people credit-invisible.”

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