Financial institutions rely on risk models to determine the probability of whether a consumer will repay a loan.
What is Credit Risk and Credit Analysis?
Credit risk is the chance of a borrower defaulting on a debt by failing to make the required payments. Risk is an inherent part of the lending paradigm for financial institutions and other lenders. Pinpointing the amount of risk that comes with each loan is a difficult task. Some of the factors that go into the complex credit risk calculation include the probability of default, the amount of exposure at the time of default, how much the loan is expected to be worth at the time of default, and the overall loss if there is a default.
Using Data and Analytics for Credit Modeling
To predict the likelihood of default, lenders leverage historical data to guess how a consumer will behave in the future. Traditionally, credit risk models look for behavioral patterns in factors ranging from payment history to current level of indebtedness to average length of credit history. This is typically measured as a person’s FICO score, an analytically derived score that assesses credit risk to help lenders determine whether a consumer is a good candidate for a loan and what interest rate is most appropriate.
The rise of analytics and Big Data have helped enhance the process of credit risk measurement. By leveraging data, there is less guesswork and more science behind the ability to predict whether someone will default on any given loan.
Credit Invisibles are Changing the Game
The way consumers spend, save and borrow money is changing. Previous generations were taught to establish credit by making large purchases such as homes, furniture and cars, then make monthly payments on time. This determined a consumer’s creditworthiness, which could be boiled down into a FICO score.
Millennials have developed a different approach. Many rent homes rather than buy and ride bikes or share cars rather than buy vehicles. They often spend a large portion of their income on experiences rather than material items. This has resulted in a lack of established credit history for a cohort that tends to have a decent amount of income and financial responsibility.
The Consumer Financial Protection Bureau Office of Research reports that as many as 26 million Americans are “credit invisibles,” or people with limited credit histories and non-existent credit scores including millennials and others. On top of that, there are another 19 million “unscorables,” or people with insufficient credit history to generate a credit score.
Without a credit score, lenders have historically turned down requests for credit from these people and have declined to market loan offers to these groups. However, many of these consumers actually have banking and non-credit product history that can be used to create a better risk assessment. This alternate data can lead to greater access to credit markets for these consumers and an opportunity for lenders to offer financial products at reasonable risk as well as assess that ongoing risk during the life of the loan.
How the Financial Industry is Digitizing Credit Risk Modeling
The financial industry is also now using alternative financial data for credit analysis and modeling. This new approach looks not only at a consumer’s credit score, but at other real-world factors such as rent and utility payments. This hybrid report shows a clearer picture of a consumer’s transactional data, their assets/liabilities and non-income and deposit information, leading to better lending decisions.
With the recent prominence of innovative Big Data tools and analytics, it’s easier for consumers to capitalize on their credit opportunities and for financial institutions to reach and extend credit to consumers previously thought to be too risky.
A Better Way to Model Credit Risk
Envestnet | Yodlee offers several innovative solutions for lenders looking to reach credit invisibles while also performing accurate credit risk mitigation and modeling.
Envestnet | Yodlee Data Aggregation provides lenders with access to user permissioned financial data from credit cards, financial institutions, investment and loans for a real-time, enabling lenders to have a comprehensive look inside a borrower’s financial health and history.
Envestnet | Yodlee Data Analytics for Credit Risk Modeling offers lenders a robust dataset of billions of anonymized, de-identified consumer transactions that can be incorporated into models and lending decisions for new customer segments. Lenders can mine this data to find new cohorts of customers that traditional credit scoring marked as unworthy.
The Future of Lending Decisions
Envestnet | Yodlee believes that alternative data is an important part of the future of lending. Insight into a consumer’s assets, income, and expenses when appended to traditional credit reporting opens credit markets to otherwise underserved consumers like the “credit invisibles” – and it helps lenders close more loans with confidence in their risk decisions.
Contact an Envestnet | Yodlee sales representative today and learn how you can leverage consumer-permissioned data to qualify, decision, underwrite, and manage your portfolio.
