Feb 21, 2022
Unlike traditional credit bureau scores that primarily take a person’s payment history, credit utilisation rate, length of credit history, types of credit accounts and recent credit account openings into consideration, new credit scoring methods have the technology to collect new data and process it to find those people who are creditworthy yet excluded from the financial system. This opens up endless opportunities for banks to explore new market segments while reducing their credit risk.
Alternative data is the set of information about a person’s habits, interests and behaviours obtained from non-traditional sources. This data comes from various sources such as social networks, satellites, sensors, e-commerce transactions and purchase receipts stored in emails, among others.
Unlike the traditional approach, new scoring methods can go beyond a person’s financial history and understand their behaviour in different contexts. Alternative scoring uses data enrichment systems that, thanks to machine learning mechanisms, complete each person's profile in real-time, allowing banks to get to know their applicants better and make more informed credit decisions.
Alternative credit scores can offer multiple advantages to banks. The most outstanding is the ability to approve deserving customers, obtain a complete user profile, offer a quicker response time, greater security, and protect personal data.
The main issue with traditional scoring processes is that it excludes individuals that, for different reasons, are not yet part of mainstream financial services. Many of these people are trustworthy, but they are labelled as insolvent because they lack a credit history. How can students, millennials, gig-economy workers, recent white collar immigrants be considered uncreditworthy if they never had a chance to prove themselves?
Alternative qualifications come to break the vicious circle for those who cannot access loans because they do not have a financial history. By cutting the linearity between credit history and credit score, it can be seen that there is a large mass of people who are reliable despite being new to credit.
Among these populations, we find millennials and young professionals are very attractive segments for banks since many are disappointed with traditional scoring systems. It is common that, due to their little or no credit history, their applications are rejected, or they are being offered high-interest rates that prevent them from obtaining profitable loans.
Millennials are the young workforce in the world today. In Latin America, 30% belong to this segment, and therefore, if the banks want to prosper, they must carefully look at these young people and try not to discourage them. The dynamism and accuracy of alternative credit scores can help banks onboard these valuable customers and beat the competition in capturing this new segment.
Understanding each customer's individuality is the key. Through the possibility of accessing fresher and more orthogonal customer data, banks can obtain a much more complete profile of each customer. This allows them to be much more effective when launching a product or service.
It is not about trying something new and figuring along the way if it works, but knowing customers in-depth and offering them what they need when they require it, avoiding the greatest possible risks.
Another advantage of alternative credit scores, which make them stand out from traditional credit scores, is their speed of processing a loan. Hand in hand with artificial intelligence, they can analyse multiple profiles and offer a score in just seconds. This allows banks to obtain more customers in a shorter time with offers tailored to their needs.
One of the main concerns of banks and their customers is data security. Alternative credit scoring does not use personal data but rather anonymous and non-intrusive information that travels in the form of metadata.
For instance, credolab’s mobile solution converts the customer's smartphone metadata into credit scores after obtaining consent to access the data on the device. This way, the financial capacity of clients is improved while safeguarding their privacy.
Some banks may have overly strict cybersecurity processes that can harm the customer experience, slow down the application, or require additional paperwork to prevent fraud. Solutions based on artificial intelligence and machine learning that leverage alternative scoring are ideal to detect suspicious activities in real-time and without affecting the overall customer experience.
Banks will be able to distinguish fraudsters from good consumers based on device recognition, context and reputation. For example, artificial intelligence algorithms can trigger different alerts when a device is in a location marked as risky, on a blacklist, or similar to a confirmed fraudulent device.
Credolab offers a real case example on how capturing new populations can help businesses grow. The goal of one of the top 10 banks in Indonesia was to increase loan approval rates among newcomers to banking, using an underwriting system that was fair to applicants and predictive of their behaviour. Since more than 85% of people who applied were rejected, they contracted the CredoSDK solution, credolab's integrated scoring tool for collecting alternative data from smartphones. The result was a 107% increase in approval rates, a 61% increase in new users and a 5-second waiting time between request and response.
Today credolab has more than 130 clients and more than 80 million data sets uploaded, which helps improve the analysis. Thanks to technology, banks can access a highly predictive and untapped source of behavioural data so they can make faster and better credit scoring decisions with no room for error.
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