Myths and Truth of Alternative Data and Credit Scoring
6 min read
Data is among the most powerful resources, especially in credit scoring and market risk analysis. It transforms our understanding, reveals hidden trends, and helps us make better decisions. As a result, banks and other financial institutions are leaning on data to drive business practices.
Before now, financial institutions and fintech depended on traditional data to make their decisions. Although traditional financial data is effective, it creates some lapses in information, especially for the unbanked and underserved.
Alternative data has come into play as more financial institutions are integrating their data from mobile money, local agents, mobile devices and more sources to make smarter decisions.
Leveraging billions of non-traditional data sources is slowly becoming the best method to get a better picture of your customers and how they interact with your brand.
However, there have been high misconceptions about the accuracy and efficacy of alternative data. In this article, we’ll examine the myths of alternative data and alternative credit scoring.
Myths of Alternative Data
Numerous misconceptions about alternative data have stopped financial institutions from embracing it. We’ve highlighted these myths and debunked them with facts.
There’s a myth that alternative data is unreliable and unsafe for use, especially during credit scoring.
On the contrary, verified alternative data is just as trustworthy as verified traditional data. Some systems analyze datasets before transferring and incorporating them into machine-learning models. As a result, businesses/institutions that employ verified alternative data will have reliable data.
Moreover, financial institutions are already utilizing alternative data to evaluate assets and make better-informed judgments. Today’s business finance strategy is insufficient without the utilization of alternative data. It is employed to monitor and forecast market movements, hence optimizing the portfolio.
Alternative data is reliable for assessing investment opportunities, deciding on financial products to offer every customer, and much more. However, it is essential to use reliable alternative data providers, such as Oystr Finance, to benefit from verified data.
In truth, customer data privacy and protection legislation across the world ensures that third-party access requires the consumer’s consent.
Alternative data gathered from third parties or directly from consumers has been obtained with the agreement of the individuals/businesses concerned. Globally, regulators expect platforms to adhere carefully to customer data privacy norms and regulations.
Furthermore, clients frequently freely disclose their data owing to the increased financial services it affords them in the long run. Meanwhile, smartphone metadata allows them to determine the sort of data to be disseminated and whether access to it should be permitted or denied.
Every day, massive amounts of “alternative data” are created to supplement or substitute traditional financial data (such as loan payments, defaults, and bankruptcies) and open the door to financial services for previously unbanked.
While informal financial data is valuable for accessing financial data, operators are responsible for adhering to data privacy rules. Similarly, end-users are accountable for double-checking the privacy policies offered by their bank operators before committing to any service.
It’s Only Useful for Consumer Financing
Alternative data has a wide range of use beyond consumer financing. For example, it translates to improved SME lending and financing, smart investments, and market forecasts, among several other applications.
Integrating alternative data into your analysis can help you get ahead of the market and identify new opportunities. It helps financial institutions provide services to segments that would otherwise be ignored due to their insufficiency of “official” data on creditworthiness.
Alternative data is one of the best tools for making investment decisions since it allows you to comprehend current market trends and triggers. It provides the lender with new prospects and more revenue without posing a significant risk.
Using alternative data is still considered a new and promising trend in the financial industry. As a result, organizations are actively investing in creating new strategies to generate value from alternative data, which can be directly integrated into their credit analysis and loan approval processes.
On the contrary, financial data institutions can mitigate borrowers’ risk and access their best consumers with alternative data. Much of the alternative data from mobile payments, merchant transactions, and social networks are rich in information. However, most financial service providers are not using these alternative sources comprehensively, despite their untapped potential.
In a world where traditional credit scoring data is used for commercial lending, alternative data has the potential to unlock a wealth of insight. The technology that powers fintech companies’ financial services creates new opportunities for borrowers and lenders alike.
Alternative data has the potential to unlock a vast range of opportunities for financial institutions. With this wide array of information, it is possible to identify customers more likely to default on loan payments and then put them on a fast track to other loans.
These alternative data sources also have the potential to democratize credit access by giving consumers new ways to access capital that isn’t being provided by traditional banks or credit unions.
Alternative data and credit scoring are revolutionizing the world of SME loans and investment opportunities and making the commercial credit underwriting process quicker, safer and smarter for all parties involved.
Fintech companies can use this unique data to find new customers by segmenting them into the right lending categories.
Alternative Data Is Social Media Data
Alternative data represents a wide range of sources and types. It can be generated by the customer, such as purchase data or behavior in social media channels; it can also be derived from internal business systems or partner data for a specific purpose, such as financial transactions or geographic location.
Alternative financial data is generated from sources that do not rely solely on social media, such as mobile phone data, app usage, internet activity, satellite imagery, and other mobile apps - like loyalty programs.
They offer profound and predictive insights about client behavior, ranging from online transactions to web traffic.
Other additional sources of alternative financial data include public records, sensors, product reviews, mobile phone data, internet activity, satellite imaging, app usage, and so on.
The Use of Alternative Credit Scoring
Alternative credit scoring can help borrowers with “no credit files” gain access to credit, but it can also help borrowers with “thin credit files” supplement traditional data.
The use of alternative data in credit reporting has increased significantly over the last couple of years. It can complement traditional credit data and is valuable when used together with traditional credit data.
The potential of alternative data makes it an area to watch as it could fundamentally impact how banks make decisions and establish credit eligibility. This can especially empower working-class individuals and MSMEs who may lack traditional credit history but have a high capacity to repay loans, such as those in emerging markets where mobile penetration is high and online cash transfer systems are common.
Credit data is limited in developing economies. As a result, alternative data sources, such as behavior and social media data, are increasingly leveraged to make lending decisions.
African countries are witnessing a rapid rise in mobile financial services usage. With accurate credit reports, financial institutions can open a new segment of customers with the confidence that their loans will be repaid.
The ever-growing speed of data processing, availability, and growth of smart machine learning algorithms has led to the swift adoption of alternative data. Therefore, it is vital to demystify and establish the facts regarding its characteristics and applicability.
Alternative data is considered a valuable resource for decentralizing the finance industry.
It can assist users in deriving information from existing data and facilitate the acquisition of data from new sources, so presenting a more accurate view of the current status quo and trends.