As competition increases and defaults rise, banks, credit card companies and non-bank lenders all seek to create a more accurate credit scoring model. No matter what type of financial institution involved or what model they employ, consumer credit scoring is facing new and increasing challenges.

With traditional credit scoring models like FICO proving increasingly inadequate, machine learning and artificial intelligence offer a way for lenders to create more comprehensive and adaptable risk profiles. With even standard scoring platforms enabling the combination of traditional and alternative financial data, lenders now face challenges in speed: rapid feature generation and even more rapid deployment to production. This furthers the need for machine learning credit scoring models.

Banks, credit card companies and non-bank lenders face similar challenges as they integrate ML into their current credit scoring models, just as this integration presents each with unique opportunities. However, no matter what amount of data available or product offered, the next generation of AI and machine learning solutions presents technology that will significantly improve credit scoring.

As the amount of data available to lenders increases, so does the need for technology that’s capable of gathering this data at a large scale, consolidating and enriching data and building stable and accurate validations and predictions. Machine learning solutions can then generate new columns of data and new features to enrich the credit scoring model.

Each type of lender presents different opportunities for ML- based credit scoring to create a significant advantage for their business:

Companies like Quicken Loans, PennyMac and LoanDepot are rising in use due to their ability to quickly process loan applications and their service of consumers who struggle from a lack of established credit. Using machine learning to create a credit scoring model that relies less on traditional credit scores allows non-bank lenders to tap into an underserved demographic.

Unfortunately, relying heavily on the application model leaves a void in financial data that must be filled through data enrichment. Using ML to automatically enrich the data taken from the application form of thin-file customers with online web data from government databases, credit bureaus and other sources can create a much more complete view of customers. Non-bank lenders that employed ML for credit scoring that uses data enrichment have seen a typical accuracy lift of 15-20%, depending on data availability (and even as high as a 50% lift).

Unlike non-bank lenders, credit card companies do not suffer as much from the lack of data. Credit card issuers are already leveraging artificial intelligence and a wealth of behavioral data to customize card limits, monitor subscriptions, identify credit fraud, and – most importantly – create a more robust credit risk model.

The main challenge in using machine learning for credit card companies is maintaining explainability just as the amount of data incorporated into credit scoring models is exponentially increasing. With a mix of financial data, spending activity, social media activity and additional public web data, these features must be properly weighted to maintain stability and explainability.

Machine learning credit scoring solutions that can automatically re-engineer features can greatly improve Gini scores. Organizations that were already using machine learning saw a 10-25% improvement in model accuracy by moving to a model that included feature re-engineering.

As we mentioned above, non-bank lenders are growing in popularity in large part due to the ease of use compared to the long, complicated process of traditional bank lending. According to a recent research study from the American Bankers Association, the most common challenge in lending among 74% of banks was efficiency. ML offers traditional banks a way to close the gap and offer a more efficient credit scoring model that can reduce the overall friction of the lending process.

As banks use the behavioral model and rich financial data, they also need a way to intelligently build algorithms that can leverage traditional and alternative financial data, compliant with regulations.

Another significant challenge banks see most is the legal requirements of presenting a machine learning model that is explainable. The future of credit risk management includes machine learning models that remove bias through more transparent and stable models.

Financial institutions have shown a limited use of machine learning for credit risk modeling in the past due to a variety of issues with the technology. The new generation of machine learning models have overcome these initial problems and are helping lenders of all types improve their risk assessment and innovate their businesses.

Ultimately, adopting the latest in ML technology for credit scoring can help banks, credit card companies and alternative lenders improve their credit and risk modeling with AI. Determining everything from credit card limits to loan length and interest rates can be bolstered by such a platform, growing profitability without increasing risk.