How Tech Companies Using Credit Scoring To Determine Eligibility

In the UK, tech companies are increasingly venturing into the financial sector, offering products like loans, credit cards, and buy-now-pay-later (BNPL) services such as Klarna. A critical component of determining eligibility for these financial products is credit scoring. 

This system assesses a customer’s creditworthiness and risk level, allowing companies to make informed lending decisions. Here’s how tech companies use credit reference agencies, their own scoring systems, AI, data analytics, and customer profiling to manage risk and determine financial product eligibility.

Credit Reference Agencies

Tech companies in the UK often start their credit assessment by checking with credit reference agencies (CRAs), such as Experian, Equifax, or TransUnion. These agencies maintain extensive databases on individuals’ credit histories, including information on credit accounts, payment behavior, defaults, and bankruptcies. When a customer applies for a financial product, companies will run a credit check and use data from these CRAs to evaluate the individual’s creditworthiness.

CRAs in the UK generate a credit score based on a range of factors, including payment history, debt levels, and the length of credit history. Tech companies typically use these scores as a baseline for determining eligibility, with higher scores indicating lower risk and better chances of approval. According to Experian, the average UK credit score in 2023 is around 759, with a range between 0 and 999, indicating the varying levels of risk.

When running a check, it leaves a credit search footprint on the customer’s report and this is just as a way of recording that a search has taken place. Prospective lenders can see the number of recent searches carried out and by what companies and having too many within a short space of time can be a turnoff and warning sign that the individual is in desperate need of funds.

In-House Scoring Systems

Beyond credit reference agencies, tech companies often develop their own scoring systems to manage risk and tailor lending decisions to their specific customer base. These internal scoring systems might consider additional factors, such as customer behavior on the company’s platform, transaction history, and even social media activity. 

The flexibility of in-house scoring systems allows tech companies to adjust their risk tolerance by increasing or decreasing scoring thresholds.

For example, a tech company offering BNPL services might set a higher internal credit score threshold during economic downturns to minimize risk. Conversely, during periods of economic growth, they might lower the threshold to attract more customers and increase market share.

Customer Profiling

Customer profiling is another technique tech companies use to assess credit risk. This approach involves analyzing customer data to identify trends and patterns that can inform credit decisions. 

Profiling can include demographic information, geographic location, purchasing habits, and even behavioral data from a company’s website or app. By profiling customers, tech companies can predict their likelihood of repaying debt or defaulting, helping them make more accurate lending decisions.

AI and Data Analytics

Tech companies leverage AI and data analytics to enhance their credit scoring processes. AI algorithms can analyze vast amounts of data, uncover hidden patterns, and make predictions about customer behavior. Machine learning models can continuously improve as they process more data, allowing tech companies to refine their credit scoring methods over time.

AI-driven data analytics for loans can also help tech companies identify potential fraud, detect anomalies, and assess the overall risk associated with specific customer segments. By using AI and data analytics, tech companies can streamline their credit scoring processes and make more informed lending decisions.

Conclusion

Tech companies in the UK use a combination of credit reference agencies, in-house scoring systems, customer profiling, AI, and data analytics to determine eligibility for financial products. By leveraging these tools and techniques, they can manage risk effectively while offering financial products to a broader range of customers. As these technologies continue to evolve, tech companies can refine their credit scoring methods, leading to more accurate assessments and improved risk management in the financial sector.