February 2020 Newsletters

Quantifying Financial Well-Being With Credit Karma

On September 17, 2019 the Consumer Financial Protection Bureau (CFPB) published a new report titled “Credit Characteristics, Credit Engagement Tools, and Financial Well-Being.”  The report detailed the CFPB’s findings from a joint research study with Credit Karma. For the non-millennials out there, Credit Karma is a personal finance technology company that offers a collection of products for its users to monitor and improve their credit health. Using the online and app-based service, users can access their credit scores and reports from TransUnion and Equifax, with weekly updates.

The purpose of the study was to examine how consumers’ subjective financial well-being generally relates to objective measures of consumers’ financial health, specifically, consumers’ credit report characteristics. Additionally, the study aimed to find how consumers’ subjective financial well-being related to consumers’ engagement with financial information through educational tools, like those provided on Credit Karma. Ultimately, the goal of the study was to better understand these relationships to uncover the factors that work together to determine consumers’ financial well-being and provide the CFPB with a long-term strategy for improving consumers’ overall financial capability.

For this study, Credit Karma administered a voluntary survey to 4,599 consumers, 4067 of which provided complete answers (shame on you, remaining 492), of which 2,966 had matching data (shame on you too, remaining 1,101). The survey consisted of the full 10-question version of the CFPB’s Financial Well-Being Scale, and the results were then merged by Credit Karma with the respondent’s background, credit report, and website usage data. Using the Financial Well-Being Scale (FWB Score), a standardized number between 0 and 100 which represents an individual’s level of overall financial well-being, the CFPB was able to compare the subjective financial well-being of consumers with their overall financial well-being. 

The general conclusions of the study were described by the CFPB as intriguing and novel, and warranted a more in-depth examination to assist the CFPB develop its strategy for improving the financial capability of consumers using valuable resources such as Credit Karma. Among the report’s most significant findings were: 

• A consumer’s credit score is very strongly positively connected to the FWB score, as indicated by a correlation coefficient of 0.44, meaning that people with higher credit scores also tend to have higher FWB scores. 
• The study showed that users of Credit Karma were significantly less likely to have low FWB scores, (scores of 40 or below). However, users of Credit Karma were also slightly less likely to have very high scores (70 or higher). Rather, most Credit Karma users had scores somewhere in-between. 
• Individuals regularly using the credit score simulator tool through Credit Karma had an improved FWB score equivalent to a credit limit increase of $7,000.  
• Users who regularly utilized the credit factors tool on Credit Karma, which helps individuals understand the factors affecting their credit score, had an improved FWB score equivalent to a credit limit increase of $8,300.  

So, what does this mean for you? Nothing yet. But, it’s something to keep an eye on as a potential resource for your customers to help improve their overall financial well-being.  

Sign on the Dotted Line

by C/A Staff

Is there a regulatory requirement to have the _________ disclosure signed by the consumer under the regulation? If your first thought is yes, the answer may surprise you. Fill in the above blank with any one of the many disclosures or notices required by the federal regulations, and we’ve most likely received a question regarding the signature requirement for it. Surprisingly, the answer to whether a signature is required by regulation is usually no. While signatures, per se, are not typically required by regulation, it is worthwhile reviewing some of the instances in which as practical matter signatures are obtained.  

Reg. B generally prohibits banks from requiring signatures from a co-applicant on a credit application of an applicant that qualifies on their own. Joint applicants are an exception to the rule; the regulation requires evidence of the person’s intent to be a joint applicant at the time of application. While the regulation doesn’t preclude other methods of establishing intent, the commentary specifies that a signature on the credit application affirming intent to jointly apply for credit may be used to establish joint intent. Because intent would be tricky to demonstrate otherwise, we have heard that as a matter of policy, most of our members obtain signatures from the applicants affirming their intent to apply for joint credit.

Under the Flood regulations, when applicable, a bank is required to provide a Notice of Special Flood Hazards and Availability of Federal Disaster Relief Assistance to the borrower. The regulation requires the bank to document receipt by the borrower. The FDIC Compliance Examination Manual, provides examples of such documentation which includes:

1. a borrowers signed acknowledgement on a copy of the notice;
2. a borrower-initialed list of documents and disclosures that the lender provided the borrower; and
3. a scanned electronic image of a receipt or other document signed by the borrower.

While the regulation doesn’t specify that a signature is explicitly required for the record of receipt, we have heard that most of our members, as a matter of policy, follow the guidance’s examples and obtain signatures.

Lastly, the Consumer Protection in the Sales of Insurance regulation requires certain disclosures to be provided with the initial purchase of an insurance product or when soliciting an insurance product in connection with a credit application. As a general rule, the regulation requires the bank to obtain a written acknowledgement from the consumer that they received the disclosures when they are provided. To satisfy this requirement we have heard that most of our members, as a matter of policy, obtain signatures on the preprinted disclosures, documenting that the consumer received the disclosures.

Even in cases where signatures are not required by regulation, such as for the Loan Estimate and Closing Disclosure under TRID, the appraisal disclosure under Reg. B, and the overdraft opt-in requirement under Reg. E, many banks still have borrowers sign these disclosures and notices as a matter of policy. It is a commonly-accepted, straightforward way to document that the notices and disclosures were provided, and signatures may be required by investor guidelines in addition to the bank’s internal policy.

Artificial Intelligence and its Unintended Learning Patterns

by C/A Staff

As technology matures, many financial institutions have been adopting artificial intelligence and machine learning algorithms to determine an applicant’s creditworthiness, which allows for a faster and more streamlined process in handling credit applications. It also helps to reduce human error, and enables a leaner operation. However, it is important to understand the possible bias that an algorithm can develop.  An algorithm bias can lead to violations of the Equal Credit Opportunity Act (“ECOA”) and the Fair Housing Act (“FHA”).

As you may know, both ECOA and FHA recognize discrimination if there is either: (1) disparate treatment or (2) disparate impact. To avoid disparate impact claims, it is crucial to monitor the input data to ensure that the artificial intelligence (“AI”) algorithm is not using data that will result in a disparate impact. For instance, an algorithm can determine that people that graduated from certain prestigious schools were less likely to default on a loan. This can sound like a useful metric, but if these schools were biased in their admissions procedure based on one of the prohibited factors, the algorithm can adversely affect the protected class as well.

It may also be a good idea to document the input attributes to show “business necessity” and why it was preferred over an alternative policy or practice to mitigate the risks associated with disparate impact. Documenting and monitoring the attributes that are input is important because AI algorithm can pick up on patterns which are irrelevant and can have unintended consequences. In the earlier days of AI training, researchers created a detection system designed for the military to correctly distinguish of pictures of camouflaged tanks versus picture of just trees. After the algorithm studied 50 of each training set, it was able to distinguish the remaining 100 pictures accurately. The Pentagon tested the algorithm on the field and the results were disastrous. It turned out that the algorithm developed a “brightness bias” because the photos of the camouflaged tanks were taken on cloudy days whereas the pictures of the trees were taken in clear, sunny weather.[1]

Similarly, an algorithm might find that those who shop at a specific franchise serves a higher credit risk. If this franchise is disproportionately located in communities with many minorities, it could adversely affect minorities, leading to disparate impact claims. Oftentimes, it is difficult to keep track of how the algorithm is adapting and developing. To avoid unnecessary disparate impact claims, it is always a good idea to create a tool to monitor what data the algorithm is using to learn and influence its decisions. By monitoring the types of data the algorithm is applying in its decision-making, you will find it much easier to make necessary adjustments to its learning methods to remain compliant with the Fair Lending laws.