5 Ways To Transform Consumer Lending With Machine Learning

How significant of a role will – or should – machine learning play in lending decisions? While artificial intelligence helps institutions lend at scale, it is machine learning that improves decisioning algorithms. As a bottom-up approach to traditional lending decisioning, machine learning crunches data points usually outside the scope of what a single loan officer can accomplish. The machine learning algorithm looks for relationships between variables that improve its predictions over time, thus teaching itself and improving a lender’s traditional credit models and risk profile.

This concept is not new, in fact lenders have analyzed data for decades to gain a better understanding of events or trends in past that impacted their business. With machine learning lenders are now able to analyze the same data and predict future behavior. With the industry moving towards an end-to-end digital loan, it subsequently produces large volumes of external, internal, and social media data, creating a remarkable opportunity for machine learning to gather this large-scale data, consolidate it, and identify lending history patterns to improve consumer lending decisions. 

Deploying a machine learning model allows lenders to detect patterns and apply known rules to predict outcomes, yield insights and detect potential anomalies in credit risk. Based on these patterns, machine learning algorithms can judge whether a borrower is creditworthy or not and regulate the process of the loan decisioning engine. Transforming the following five areas with machine learning can lead to a transformation in consumer lending.

1.) Improved Credit Decisioning

According to a recent report from McKinsey, machine learning may reduce credit losses by up to 10 percent, with more than half of risk managers expecting credit decision times to fall by 25 to 50 percent. With machine learning, lenders are able to combine credit history with wider range of data sources improving not only the accuracy, but providing more predictive insights than traditional human analyst methods.

Machine learning models can reduce the cost of assessing credit risks for applicants, as well as increase the number of potential borrowers for whom banks can evaluate credit risk. Clustering algorithms lets institutions better understand how a consumer’s credit history, transaction volume and profile have changed over time and can quickly, easily and efficiently make improved credit decisions for both existing and new customers. With ML, borrower evaluations are automated significantly lowering the associated operational costs and providing faster loan decisions. 

2.) Reduced Customer Churn

Lenders are expanding their usage of ML tools across the organization to boost not only operational efficiencies, but also for gaining new and retaining current customers. Given the costs associated with acquiring new customers (nearly five times higher than retaining current customers) reducing customer churn is pivotal to future success. With machine learning, lenders are now able to access invaluable insight regarding customer behavior that was previously unavailable.

Deep learning algorithms such as Recurrent Neural Networks (RNN) and Long Short Term Memory (LSTM) uses profile and transaction volume history to determine which customers might be ready for a change and predict the propensity of a customer to churn or move away using refinancing. Using this information, the lender can then take appropriate steps to address concerns and retain business.

3.) Enhanced Loan Prediction Accuracy

Some current processes in place at lending institutions are somewhat data-driven but use simple linear statistical techniques, such as investors’ credit risk models. Machine learning has an important advantage over these traditional statistically-driven algorithms, especially when applied to complex nonlinear problems where there is a large amount of data, particularly unstructured data.

Leveraging this machine learning data can significantly improve the loan prediction accuracy. The availability of large amounts of data to revamp the machine learning model is steadily becoming less and less of a stumbling block. These methods can be used both to price riskier loans more appropriately as well as provide loans to borrowers who would have otherwise been turned away by overly conservative risk models.

4.) Advanced Fraud Detection

Machine learning can dramatically accelerate the application verification process with predictive models that instantly assess fraud risk. Using various data types including historical application data, models can be designed to determine the likelihood of fraud associated with each application. Machine learning uses complex algorithms that iterate over large data sets and analyze the patterns in data. The algorithm facilitates the machines to respond to different situations for which they are not explicitly programmed. Pattern recognition algorithms such as Long Short-Term Memory (LSTM), can identify a specific sequence of transactions within highly unorganized data that could indicate fraud or money laundering.

Machine learning allows for creating algorithms that process large datasets with many variables and help find these hidden correlations between user behavior and the likelihood of fraudulent actions. This reduces or eliminates the need for manual application review, reducing processing time from days to minutes and resulting in a much better customer experience.

5.) Enriched Marketing Campaigns

Perhaps the greatest potential for machine learning is providing more engaging, personalized marketing campaigns. Association-based algorithms can be used to identify consumer spending patterns and in turn, enable lenders to create tailored campaigns, providing relevant offers to customers. Additionally, algorithms can also predict whether a customer will respond to or engage with an offer, and match customers with the best offer according to their current situation.

Customizing your marketing strategy to your customer’s preferences makes communications relevant and encourages consumers to engage, rather than disconnect. Targeted sales and marketing efforts enable financial institutions to offer customers the services that they need, rather than a one-size-fits-all approach that barrages customers with every possible loan service.

Conclusion

Lenders face complex challenges in the digital era; machine learning is a high technology means to address these problems and facilitate strategic growth. Through disruptive yet sustainable innovation, machine learning can help bankers make more complex and nuanced loan decisions faster and with less effort – all while learning from historical loan performance data. A robust machine learning approach can boost the accuracy of all predictive models deployed in consumer lending, such as a customer’s likelihood of responding, approval, delinquency, default, and many other general customer behaviors. Incorporating machine learning into lending strategy can be transformative for not just the financial institution, but also the communities they serve. When consumers have access to affordable lending options from an insightful and discerning source – with the same, or decreased credit risk profile – the effects can be boundless.