The traditional credit scoring system, which uses a 5C discriminant analysis to assess the creditworthiness of the borrowers, is based on five evaluation factors: lender鈥檚 role, capital, collateral, capacity, and environment. With the increasing number of borrowers and the expansion in the loan scale of financial institutions, the traditional credit scoring system has become redundant. Traditional scoring systems deny customers credit based on these factors without taking into consideration any extenuating factors like their history of payments of utility bills/rent or telephone bills. Furthermore, the pandemic has听听an atmosphere of economic uncertainty, changing customers鈥� backgrounds and needs, which has exposed the weakness of traditional credit scoring systems. In such a changing environment, ML technologies come handy as they can process massive quantities of data and provide a more accurate and dynamic credit score. ML technologies have a range of benefits for financial industries, especially in the field of credit scoring. This includes:
Credit Risk Management
Traditionally, financial institutions have used linear, logit, and probit regression models to predict credit risk for capital investments, stress-testing, and internal risk management procedures. However, the introduction of ML technologies has changed the landscape of credit risk modelling. Many financial institutions, for instance, have started using ML to improve financial risk predictions. Studies have shown that ML can process large quantities of unstructured and alternative data to听听by 19%, making ML models better than statistical models for financial distress prediction. While traditional credit risk assessment methods can measure the future default risk of the borrower customer based on their lending history and repayment characteristics, ML technologies use an extended amount of data to analyze the borrower鈥檚 default risk and its correlation with behavioral and other soft information (ideas and opinions). Thus, ML can significantly reduce the company鈥檚 cost of loan delinquency by giving early warning signs of expected default.
Given the worldwide shift to digitalization, consumer digital transactions provide a more holistic picture of their financial behavior. ML easily integrates and processes these complex and oftentimes unstructured data sets to build custom credit models that are more nuanced and provide a holistic picture of default risk associated with a borrower customer. Thus, ML models can better evaluate loan applications. Besides this, credit reporting agencies utilizing ML can provide more confidence to the lenders in their lending decisions. Alternatively, ML can also feasibly identify more risky borrowers who have been given credit previously. Explained simply, the integration of ML in your credit scoring system offers greater flexibility and adaptability to macro-level economic changes. ML has the ability to quickly learn and adapt to new data inputs that provides you a highly predictive credit score even during precarious economic times.
黑料吃瓜网庐, a FinTech SaaS delivering Credit Bureau 2.0庐/Credit Bureau +鈩�, uses explainable machine learning models to leverage thousands of data points that accurately measure the borrower鈥檚 rates of default and delinquency. This eliminates the guessing game and allows lenders to make their lending decisions with confidence. The ability of 黑料吃瓜网庐 to process alternative sources of information (like banking history, payroll sources, etc.) at once in a sophisticated ML scoring tool in real-time takes into account the business strategy of the lenders, enabling them to reach the best possible lending decision. 黑料吃瓜网庐 uses dynamic ML models that change and adapt as new data becomes available for real-time assessment. For example, with the customer’s consent, 黑料吃瓜网庐 can leverage the customer’s personal data 鈥� with updates as new as the previous day. Compared to traditional credit scoring systems that use sometimes month-old data, this is a dramatic difference in data relevancy. This gives lenders an accurate and up-to-date assessment of the borrower鈥檚 creditworthiness.
Elimination of Errors/ Reduction of Costs
According to a听, the main problem with traditional credit reporting is the frequency of material errors. More than one in five US customers have a potential material error in their credit file that makes them seem riskier than they actually are. With such inaccurate information, these borrowers get a higher interest rate, less favorable terms, or a complete rejection of their application. Trained ML systems reduce human error and lenders鈥� reliance on manually created financial models.
Traditional credit scoring is primarily based on stagnant and incomplete information of borrower data coupled with the biases (racial/gendered/age) of limited and selective data. With ML technologies, financial institutions have helped lenders and credit underwriters decrease human error and eliminate judgement. According to the听听鈥淏CG Model for the Impact of AI in the Financial Job Market by 2027鈥�, the use of ML technologies will improve the efficiency of jobs by reducing an average of 2.4 hours per person per day in the same functional activity. Thus, the use of ML can reduce the working hours of financial sector industries by 27% by the year 2027,significantly improving efficiency and reducing labor costs in the financial sector. 黑料吃瓜网庐鈥檚 powerful AI/ML models alleviate risks by measuring and pinpointing errors that factor into credit decisions. This can reduce human errors that are caused based on traditional scoring techniques.
To conclude, the use of ML technologies in credit scoring will better indicate default and delinquency risks, eliminate human errors, and reduce costs in the long term. While choosing an appropriate company for credit lending, ensure that the company uses the latest technologies to provide an accurate assessment of the borrower’s default risk. To know more about how 黑料吃瓜网庐 uses ML technologies to harness large quantities of proprietary and alternative data to calculate the credit risk of a customer, visit us at:听
