The financial services industry has always been one where decisions are rational and backed by evidence and quantitative analyses. When evaluating an acquisition, there’s a model to back up the investment decision. When extending credit to a company, you analyze its financial statements and evaluate their credit history. Nevertheless, when it comes to personal loans or credit cards, it is impossible to conduct a thorough evaluation of every single potential customer applying to credit; that is why banks would traditionally rely on credit scores (which are statistical models designed to predict a client’s default probability by analyzing historical data) to decide whether to approve or deny a new credit application.
This is true for banks around the world. Nevertheless, the reality of each society determines specific barriers that harm the ability of banks to penetrate the market in an efficient way. From my experience in Latin America, I could say that the two greatest obstacles that keep the retail banking industry from growing are: Data scarcity and low quality of the data.
Unlike the United States, a large percentage of the population in developing economies has never interacted with a bank, and they don’t want to either. Having a cash based lifestyle, absolutely no credit history or a savings or checking account makes it impossible for traditional credit scores to have a consistent evaluation of these clients. This results in banks not lending to these people, which perpetrates the vicious cycle. Many times (when regulation allows it), banks recur to alternative variables (e.g: demographic variables) to build statistical models of lower predictive value.
Whenever banks have to rely on “alternative variables” (which have to be collected by asking the customer), many times clients lie in their credit applications fearing to not be “good enough” to be deemed worthy in the eyes of the bank. Moreover, some times, sales representatives, incentivized by their sales commissions and empowered by their empiric knowledge of the evaluation models, would game the system by falsifying information in order to get a higher approval rate.
Value Creation & Value Capture
EFL creates value in the industry by providing an alternative statistical model to predict potential customers’ default probability. It collects psychometric information of applicants through a survey that allows banks to create an additional axis to segment a given population. Customers respond to these questions in the banks’ branches during a credit application process, and data input is supervised by a sales representative.
After piloting the predictive power of the model on top of a traditional credit score (Equifax), EFL concluded that its proprietary model could decrease default rates by half while maintaining the same loan volume or increase volume by 140% while maintaining the same default rates. This will automatically translate into higher revenues or lower losses for the client bank, which is shared with EFL in the form of fees.
EFL has managed to successfully build solid relationships with banks and retailers in Perú, and it’s continuing to grow in Latin America and South Asia.
The following graph illustrates how EFL allows for better segmentation:
Risks going further
As with any data analytics business, the most important element is the data that is inputted in the model. EFL might provide a better tool to discriminate risk when traditional models don’t work, but if the data is not true, the whole model is in danger. Customers, or even sales people could still figure out how to manipulate inputs to get better approval rates and ruin the statistical significance of the product. Since the data collection process is decentralized, EFL should ensure appropriate and effective controls are set up to prevent this from happening.
On the other hand, EFL is building and improving its algorithm through data from its clients’ history. When deploying the model with new client, some biases could be transferred from one competitor to the other. In order to avoid this, EFL must ensure that its partners are not using arbitrary evaluation policies on top of the psychometric tool, or again, the data will lose statistical significance.