Enova International Inc's Suppliers Performance

Decision Engine
We have developed a fully integrated decision engine that evaluates and rapidly makes credit and other determinations throughout the customer relationship, including automated decisions regarding marketing, underwriting, customer contact and collections. Our decision engine currently handles more than 100 algorithms and over 1,000 variables. The algorithms in use are constantly monitored, validated, updated and optimized to continuously improve our operations. In order to support the daily running and ongoing improvement of our decision engine, we have assembled a highly skilled team of approximately 50 data and analytics professionals as of December 31, 2015.

Proprietary Data, Models and Underwriting
Our proprietary models are built on more than eleven years of history, using advanced statistical methods that take into account our experience with the millions of transactions we have processed during that time and the use of data from numerous third-party sources. We continually update our underwriting models to manage risk of defaults and to structure loan terms. Our system completes these assessments within seconds of receiving the customer’s data.
Our underwriting system is able to assess risks associated with each customer individually based on specific customer information and historical trends in our portfolio. We use a combination of numerous factors when evaluating a potential customer, which can include a consumer’s income, rent or mortgage payment amount, employment history, external credit bureau scores, amount and status of outstanding debt and other recurring expenditures, fraud reports, repayment history, charge-off history and the length of time the customer has lived at his or her current address. While the relative weight or importance of the specific variables that we consider when underwriting a loan changes from product to product, generally, the key factors that we consider for loans include monthly gross income, disposable income, length of employment, duration of residency, credit report history and prior loan performance history if the applicant is a returning customer. Similar factors are considered for small business applicants and also include length of time in business, online business reviews, and sales volumes. Our customer base for consumer loans is predominantly in the low to fair range of FICO scores, with scores generally between 500 and 680 for most of our loan products. We generally do not take into account a potential customer’s FICO score when deciding whether to make a loan. A Vantage score is one of the factors in our credit models for our near-prime installment product in the United States. Since we designed our system specifically for our specialized products, we believe our system provides more predictive assessments of future payment behavior and results in better evaluation of our customer base when compared to traditional credit assessments, such as a FICO score.

Decision Engine
We have developed a fully integrated decision engine that evaluates and rapidly makes credit and other determinations throughout the customer relationship, including automated decisions regarding marketing, underwriting, customer contact and collections. Our decision engine currently handles more than 100 algorithms and over 1,000 variables. The algorithms in use are constantly monitored, validated, updated and optimized to continuously improve our operations. In order to support the daily running and ongoing improvement of our decision engine, we have assembled a highly skilled team of approximately 50 data and analytics professionals as of December 31, 2015.

Proprietary Data, Models and Underwriting
Our proprietary models are built on more than eleven years of history, using advanced statistical methods that take into account our experience with the millions of transactions we have processed during that time and the use of data from numerous third-party sources. We continually update our underwriting models to manage risk of defaults and to structure loan terms. Our system completes these assessments within seconds of receiving the customer’s data.
Our underwriting system is able to assess risks associated with each customer individually based on specific customer information and historical trends in our portfolio. We use a combination of numerous factors when evaluating a potential customer, which can include a consumer’s income, rent or mortgage payment amount, employment history, external credit bureau scores, amount and status of outstanding debt and other recurring expenditures, fraud reports, repayment history, charge-off history and the length of time the customer has lived at his or her current address. While the relative weight or importance of the specific variables that we consider when underwriting a loan changes from product to product, generally, the key factors that we consider for loans include monthly gross income, disposable income, length of employment, duration of residency, credit report history and prior loan performance history if the applicant is a returning customer. Similar factors are considered for small business applicants and also include length of time in business, online business reviews, and sales volumes. Our customer base for consumer loans is predominantly in the low to fair range of FICO scores, with scores generally between 500 and 680 for most of our loan products. We generally do not take into account a potential customer’s FICO score when deciding whether to make a loan. A Vantage score is one of the factors in our credit models for our near-prime installment product in the United States. Since we designed our system specifically for our specialized products, we believe our system provides more predictive assessments of future payment behavior and results in better evaluation of our customer base when compared to traditional credit assessments, such as a FICO score.