Credit Scoring: Keeping Customer Privacy at the Forefront

January 16, 2020

This article originally appeared in International Finance.

Consumers’ privacy and protection are at the core of efficient data collection. The personal information of consumers is at high risk of being misappropriated by entities seeking to utilise that data for personal gains. Consumers have experienced their data being exposed through company breaches that have left many consumers vulnerable. According to Bloomberg, about 100 million customers from Capital One Financial Corp had their data illegally accessed after a former cloud services employee broke into the bank’s server.

These kinds of breaches have affected consumers in varying degrees and has left some consumers to seek their own counter measures to protect against data being misappropriated. AI-based credit scoring utilising smartphone metadata offers a mutually beneficial solution to both consumers and institutions that consumers wish to conduct credit financing by putting customer privacy at the forefront of the process.

How safe is customer data?


According to the RSA Data Privacy and Security Report, for which RSA surveyed 6,387 consumers in France, Germany, the UK and the U.S., 78 percent of the UK consumers said identity theft resulting in financial loss was a top concern. The report further noted that there was a widening disconnect with how companies capitalise on customer data and consumer expectations around how their data should be used and secured. The trade-off between consumers’ need for protection of their data and companies’ need to be more competitive in a rapidly growing digital marketing place, have aggravated the issue of data protection.

Artificial intelligence (AI) is used to convert metadata into credit scores. Metadata refers to data about other data, the non-personal, binary (1s and 0s) version of the same data.  AI-based credit scoring assesses smartphone metadata to detect predictive patterns. Reliable credit scores are generated from this alternative data.

These methods are in full compliance with local data privacy laws, including General Data Protection Regulation (GDPR). The GDPR has changed the data landscape in the European Union since its implementation in May 2018. According to a survey of UK consumers by Data and Marketing Association (DMA), 62% of consumers indicate GDPR will improve their confidence in sharing data with companies. The use of smartphone metadata complements the GDPR protocols and may help to offer consumers confidence when sharing data for potential credit facilities with companies.

AI based scoring, what’s in it for the user?


Anonymised data collection sounds good, but how does AI-based scoring benefit financial institutions (FIs) and their customers?

First, through the use of anonymous, non-intrusive smartphone metadata to provide scoring for customers. This helps businesses to confidently rely on the credit scores from customers without imposing conditions on their privacy needs.

Second, AI-based scoring allows for multi-dimensional risk assessment. This credit score is used by underwriters along with their existing credit models to assist in more informed decision making. Customers of the financial institutions are assessed based on their willingness to repay, not just based on their ability to repay. As a result, they have higher chances of being approved for credit because they are able to receive feedback to their loan application even if they have a thin credit bureau report, or no report at all.

Third, the use of smartphone metadata allows for real-time analysis. A credit score is generated within seconds of accessing the phone’s metadata. As a result, AI and machine learning can enable a faster processing time and, in some cases, an immediate approval.

Last, non-traditional scoring methods based on the homogeneous collection of metadata produces opportunities to explore entering new markets globally as business are able to cater to wider range of customers regardless of their country. This results in more accuracy in the application of consistent credit policies based on the same data being collected.

The impact of Ai-based credit scoring is global


Regions including emerging economies in Africa, Asia, and Latin America have benefitted thus far from AI-based credit scoring and alternative data collection practices. These regions have financial inclusion and innovation at the forefront of advancing the lives of the unbanked population. Alternative credit scores have become more relevant in economies where the unbanked or underbanked population is relatively large. These individuals have little access to traditional banking services due to limited credit history.

Adoption of alternative methods to access credit for those individuals leads the drive forward for greater financial inclusion and locked improvement in financial literacy among the wider unbanked or underbanked population. According to International Finance Corporation, AI is driving innovation in financial services through better data processing. It plays a key role in improving traditional credit scoring methods, which have left many persons outside of mainstream economy because of inadequate credit history records.

Though alternative credit scoring solutions have targeted emerging economies, more developed regions including Europe do stand to benefit from these solutions. About 40 million citizens across the European Union (EU) do not have access to a bank account. Alternative credit scoring solutions can help close the gap between the financially included and excluded.

The businesses will benefit from that untapped segment of their market and the consumers will benefit from greater inclusion in the mainstream financial services. Additionally, the smartphone metadata complements the privacy and data concerns in the EU, allowing consumers to feel more confident that their personal information will not be misused.

What’s next in AI-based credit scoring?


Alternative credit scoring continues to grow more integrated in the decision-making process, allowing businesses to leverage its uses and consumers to expand their opportunities. Data holds the key to unlocking the possibilities of alternative credit scoring for individuals and data protection should be central in all uses of personal data. The successes of using smartphone metadata methods, leave trails that other more developed and developing nations can take guide from as financial inclusion grows globally.

A unique opportunity comes in the form of smartphone metadata as a means of developing credit scores for individuals and small business owners.  Developed regions, including those in the the EU, are in the driver’s seat to enhance financial inclusion by providing better alternatives to credit scoring for the unbanked population.

Nations within the EU should take advantage of this wave of innovation as the benefits positively impact both the micro and macroeconomic aspects of nations seeking to reduce the unbanked and financially excluded segments of their population. AI-based credit scoring still has far more to offer and smartphone metadata will continue to enhance the effectiveness of anonymised data collection among users.

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