As financial institutions shift from traditional to data-centric risk models, artificial intelligence is reshaping how customer behavior is understood and managed. This evolution allows organizations to proactively identify credit risks, personalize outreach, and optimize collection strategies, marking a fundamental change in the industry.
Surbhi Gupta, a technology specialist with 19 years of experience in IT and risk management, has developed systems for major financial and telecommunications firms like Morgan Stanley and T-Mobile. Her work involves building scalable frameworks that integrate data analytics into core financial operations, converting large datasets into actionable intelligence for risk mitigation.
Uncovering needs in credit risk
The push to integrate AI into financial systems is largely a response to the shortcomings of traditional methods. Applying a uniform collection strategy to all delinquent accounts is often inefficient and can result in negative customer interactions.
Gupta explains that this realization came from observing systemic gaps, noting, “There were significant unmet needs in collections & credit risk. After working with large portfolios, I saw that treating all delinquent accounts the same wasted effort and created negative customer experiences.”
AI offers a path to more customized and effective interventions by analyzing complex data to segment customers based on their likely actions. “AI offered a way to predict which customers were likely to self-heal, which needed flexible payment arrangements, and which customers could pose systemic risk,” Gupta states.
This capability is foundational to modern predictive collections and is supported by systems that can classify defaulters into specific risk categories, leading to more efficient resource allocation.
Designing behavior scoring models
Creating an accurate behavioral scoring model requires a careful selection of diverse data sources. The most effective models blend quantitative metrics with qualitative indicators to construct a complete view of the customer.
According to Gupta, “These models incorporate data from credit bureaus, demography of the customer, customer transactional behavior, total customer spending by credit card utilization, and frequency of product purchase.”
However, transactional data alone does not provide a full picture. Intangible attributes reflecting a customer’s history are also critical for accurate risk prediction.
Gupta adds that, “Models should factor in intangible attributes like number of payment arrangements done in past 2 years, how many times payment arrangement was defaulted, and the number of times customer is on the collection path.”
While some studies find that features like age and income are the most critical for default prediction, others highlight the predictive power of alternative data from bill payment habits.
Combining data to uncover insights
The primary advantage of predictive analytics emerges when different data sources are integrated to reveal patterns that traditional methods miss. Combining credit bureau data with historical payment information can signal potential financial distress even in customers with a solid history.
Gupta offers an example: “Consider a customer who’s an old customer for more than 2+ years with excellent payment history and credit score, but all of a sudden his credit score dropped significantly.”
This proactive insight allows for a more strategic and empathetic response that avoids alienating a valuable customer. Instead of defaulting to a standard collections process, the approach can be tailored to the situation.
“Since the customer is a good customer with a good history, he will be given options for payment arrangement, might be placed on a hardship plan based on documents shared, and collection treatment will also be different,” Gupta explains. This responsiveness is enabled by architectures built for real-time risk scoring, which can analyze streaming data with very low latency.
Balancing complexity and transparency
While AI models provide powerful predictive insights, their complexity can be a barrier for business leaders who need clear, interpretable results. Achieving a balance between advanced analytics and practical usability is crucial for adoption.
“To balance the complexity, we use a hybrid approach. For defining and creating strategies, we use a strategy tree showing clear decisions made at each step,” says Gupta. This method breaks down the decision-making process into understandable components.
Visual aids are also essential for translating complex data into actionable business intelligence. Gupta adds that, “My teams use data-driven dashboards for business leadership using tools like PowerBI and Tableau, where data can also be represented in the form of graphs for easy interpretation.”
This aligns with regulatory expectations, as entities like the Consumer Financial Protection Bureau (CFPB) require lenders using AI to provide specific reasons for adverse actions. Methods like SHAP and LIME are often used for this, though their explanations can vary depending on the underlying model.
The role of behavior analysis
Customer behavior analysis is fundamental to reducing default rates and refining outreach strategies on a large scale. By examining historical data, organizations can identify patterns that signal future financial challenges.
“Customer behavior definitely plays a vital role in understanding future defaults example, his payments from past payments, the number of credit lines in the past, sudden changes in products, sudden increase in the number of products, and loans nearing the line limit,” Gupta states.
These insights allow for proactive interventions and more informed decision-making regarding credit allocation. “All these factors help us predict the mindset of the customer and help in strategizing eligibility limits for existing customers and help in the reduction of bad debt,” she continues.
Ensuring fairness in predictive models
A significant challenge in developing predictive models is ensuring they remain fair and unbiased, especially when using sensitive customer data. Models can inadvertently perpetuate biases if not designed and tested carefully.
According to Gupta, “Multiple factors can contribute to predictive models being biased based on customer sensitive data examples, employment history, education, and demographics – location, race, behavior score, and payment channels.”
To mitigate this risk, a robust framework for testing and refinement is essential. “For a model to be unbiased, the model should be built with protected classes, such as excluding demography, performing stress testing and ‘What-if’ scenario testing,” Gupta explains.
This involves a continuous process of improvement to avoid reinforcing outdated biases. Advanced statistical techniques like Double/Debiased Machine Learning (DML) are designed to mitigate biases from high-dimensional data. Other frameworks, like Bias-BERT, are being developed to penalize biased outputs in language models.
Breakthroughs in data-driven strategies
The transition toward truly data-driven credit strategies involves several key breakthroughs that enable organizations to move beyond legacy systems. Gupta identifies these as: “Proactive and reactive behavior score calculation, and advanced AI/ML technologies that not only enable near real-time predictions but also increase revenue while reducing bad debt.” The ability to calculate scores dynamically allows for a more immediate and accurate assessment of risk.
Furthermore, technological infrastructure plays a pivotal role in this transformation. The “Migration from legacy platforms to cloud platforms makes the system more scalable and a central repository of data with minimal latency, making it easy, quick, and correct determination of strategies,” Gupta notes.
A well-defined MLOps architecture is crucial for managing the entire machine learning lifecycle, from data ingestion to model deployment. A comprehensive framework for measuring impact through a multi-layered causal model can also help quantify the return on these technology investments.
The future of AI in credit risk
Looking ahead, the evolution of AI and behavioral analytics is set to further transform credit risk management and customer engagement. AI models can be trained to adhere to fairness principles, improving both outcomes and customer trust.
As Gupta sees it, “AI can definitely help in the reduction of credit risk for any firm. AI can easily be trained for non-biased algorithms and adoption of fairness in the system, leading to a high net promoter score.”
AI will also enhance the ability to engage with customers more effectively, especially during times of financial hardship. “Customers with a good behavior score but currently on default can be treated with empathy and be put on payment plans considering current hardships,” she explains.
This empathetic approach, guided by data, fosters stronger relationships. As the industry adopts innovations like a Smart Debt Collection System, regulatory bodies recommend that firms update their risk management frameworks to address challenges such as model explainability and data privacy.
The integration of AI into credit risk is not just a technological upgrade; it represents a strategic shift toward a more predictive, personalized, and responsible financial ecosystem. By leveraging data to understand customer behavior more deeply, organizations can better manage risk while building stronger, more resilient customer relationships.
