All posts by Masood Akhtar, Global Portfolio Marketing Manager (Analytics)

In an era where businesses are inundated with data and options for consumer engagement, it is paramount to use sophisticated targeting techniques that reach and resonate deeply with the intended audience. Pre-screen targeting solutions are becoming increasingly sophisticated, offering a strategic advantage by enabling more precise and impactful outreach, especially within the financial services sector. Technological evolution and targeting precision The core innovation behind pre-screened targeting solutions is extensive data analytics and predictive modelling. These systems integrate detailed consumer data, such as purchasing behaviors and credit scores, with advanced algorithms to identify potential customers most likely to respond positively to specific promotional campaigns. This methodological approach streamlines campaign efforts and enhances each interaction's accuracy and tactical effectiveness. Effective targeting with direct mail Understanding the dynamics of various targeting channels is crucial for deploying effective strategies. In the competitive landscape of financial services in North America, direct mail has been shown to have distinct advantages. Direct mail offers substantial engagement. For credit products, this is typically 0.2-2% for prime consumers and 1-3% for near prime and subprime consumers**. This channel’s effectiveness stems from its tangible nature, which cuts through digital clutter and captures consumer attention.​ Benefits of pre-screened targeting solutions Maximized response rates—Direct Mail response models can dramatically boost prospect response rates by targeting a well-defined, high-propensity audience likely to be interested in specific offers. Using a custom response model could improve the average response rate of pre-screen direct mail campaigns by 10-25%**. Reduced risk—Traditional broad-spectrum marketing campaigns waste resources on uninterested parties. Pre-screened targeting via direct mail aims to gain the right through-the-door prospects, minimizing the risk of fraud and delinquencies, thus leading to significant cost savings on underwriting costs. Enhanced customer engagement and retention—Targeted and personalized direct mail strengthens customer relationships by making recipients feel valued. This leads to higher engagement and loyalty, essential for long-term business success. Robust compliance and enhanced security—Pre-screened solutions simplify adherence to industry regulations and consumer privacy standards. These systems come equipped with compliance safeguards that help prevent data breaches and ensure that all communications meet regulatory standards, which is especially critical in the highly regulated financial sector. Looking forward: The strategic imperative of advanced targeting and optimization As markets evolve, the strategic importance of deploying precise and efficient marketing techniques will only grow. Financial institutions harnessing pre-screened targeting and optimization solutions gain a significant competitive edge, achieving higher immediate returns and long-term customer loyalty and brand strength. Optimization ensures that the right customer prospects are targeted and done within business constraints such as resources and direct mail budgets. Future enhancements in AI and machine learning are expected to refine the capabilities of pre-screened targeting solutions further, offering even more sophisticated tools for marketers to engage with their target audiences effectively. For businesses aiming to lead in efficiency, customer satisfaction, and innovation, adopting advanced pre-screened targeting solutions is not just an option—it’s a necessity for staying relevant in a crowded and competitive marketplace. About Ascend Intelligence ServicesTM (AIS) Target AIS Target is a sophisticated pre-screening solution that boosts direct mail response rates. It uses comprehensive trended and alternative data, capturing credit and behavior patterns to iterate through direct mail response models and mathematical optimization. This enhances the target strategy and maximizes campaign response, take-up rates, and ROI within business constraints. Find out more ** Experian Research, Data Science Team, July 2024

As credit card issuers grow, the size of their customer base expands, bringing both opportunities and challenges. One of the most critical challenges is managing growth while controlling default rates. Credit Limit Optimization (CLO) has emerged as a vital tool for banks and credit lenders to achieve this balance. By leveraging machine learning models and mathematical optimization, CLO enables lenders to tailor credit limits to individual customers, enhancing profitability while mitigating risk. Recent trends in credit card debt To understand the significance of Credit Limit Optimization, it is essential to consider the current economic landscape, particularly in North America. The first quarter of 2024 saw total household debt in the U.S. rise by $184 billion, reaching $17.69 trillion. While credit card balances declined slightly (a reflection of seasonal factors and consumer spending patterns), they remain a substantial component of household liabilities, with total credit card debt standing at approximately $1.26 trillion in early 2024. On average, American households hold around $10,479 in credit card debt, which is down from previous years but still significant. The average APR for credit cards in the first quarter of 2024 was 21.59%.* The rising tide of delinquencies In the first quarter of 2024, about 8.9% (annualized) of credit card balances transitioned into delinquency. This trend underscores the need for credit card issuers to adopt more sophisticated methods to assess credit risk and adjust credit limits accordingly. The rising rate of credit card delinquencies is a key driver behind the adoption of CLO strategies. What is Credit Limit Optimization Credit limit optimization uses advanced analytics to assess individual customers' creditworthiness. By analyzing various data points, including payment history, income levels, spending patterns, and economic indicators, these tools can recommend optimal credit limits that maximize customer spending potential while minimizing the risk of default, all within the constraints set by the business in terms of its appetite for risk and capacity. For instance, a customer with a strong payment history and stable income might receive a higher credit limit, encouraging more spending and enhancing the lender's revenue through interest and interchange fees. Conversely, customers showing signs of financial stress might see their credit limit reduced to prevent them from accumulating unmanageable debt. Benefits of Credit Limit Optimization Improved Profitability - By setting credit limits reflecting customers' credit risk and spending potential, lenders can increase their revenue through higher interest and fee income. Reduced Default Rates - Lenders can significantly reduce the incidence of bad debt by identifying customers at risk of default and adjusting their credit limits accordingly. Improved Customer Satisfaction - Personalized credit limits can improve customer satisfaction, as customers are more likely to receive credit that matches their needs and financial situation. Regulatory Compliance - CLO can help lenders comply with regulatory requirements by ensuring that credit limits are set based on objective, data-driven criteria. Economic indicators and CLO Impact Several economic indicators provide context for the importance of CLO in the current market. For instance, the Federal Reserve reported that in 2023, fewer than half of adult credit cardholders carried a balance on their cards, down from previous years. This indicates a more cautious approach to credit use among consumers, likely influenced by economic uncertainty and rising interest rates. Moreover, the disparity in credit card debt across different states highlights the varying economic conditions and the need for tailored credit strategies. States like New Jersey have some of the highest average credit card debts, while states like Mississippi have the lowest. This regional variation underscores lenders' need to adopt flexible, data-driven approaches to credit limit setting. Enhanced profitability and risk mitigation Credit limit optimization is critical for credit card issuers aiming to balance growth and risk management. As economic conditions evolve and consumer behaviors shift, the ability to set personalized credit limits will become increasingly important. By leveraging advanced analytics and machine learning, CLO enhances profitability and contributes to a more stable and resilient financial system. One such solution is Experian’s Ascend Intelligence Services (AIS) Limit™, which provides an optimized strategy designed to enhance the precision and effectiveness of credit limit assignments. AIS Limit™ combines best-in-class bureau data with machine learning to simulate the impact of different credit limits in real time. This capability allows lenders to quickly test and refine their credit limit strategies without the lengthy trial-and-error period traditionally required. AIS Limit™ enables lenders to set credit limits that align with their business objectives and risk tolerance. By providing insights into the likelihood of default and potential revenue for each credit limit scenario, AIS Limit™ helps design optimal limit strategies. This not only maximizes revenue but also minimizes the risk of defaults by ensuring credit limits are appropriate for each customer's financial situation. In a landscape marked by rising delinquencies and varying regional debt levels, the strategic use of CLO like AIS Limit™ represents a forward-thinking approach to credit management, benefiting both lenders and consumers. Ascend Intelligence Services * HOUSEHOLD DEBT AND CREDIT REPORT (Q1 2024) – Federal Reserve Bank of New York

How predictive modelling and optimization can maximize recovered amounts with a focus on Next Best Action assignment. In 2023, the US economy outperformed expectations, with strong job growth, impressive GDP (annual growth rate was 2.5%, up from 1.9% in 2022), and lower inflation. Increased consumer spending and reduced trade deficit highlighted its resilience and adaptability, fostering a stable economic environment. However, the story around consumer debt and delinquencies has not been so positive. In the latest quarterly report on household debt and credit released in February 2024 by the Federal Reserve Bank of New York, total household debt saw a notable increase of $212 billion (1.2%) in the fourth quarter of 2023, reaching $17.5 trillion. Within this surge, credit card balances increased by $50 billion, alongside mortgage balances which rose by $112 billion to hit $12.12 trillion. Auto loans, which have been trending upwards since 2011, saw an additional $12 billion increase, totalling $1.61 trillion. Other balances, encompassing retail cards and various consumer loans, witnessed a growth of $25 billion. Despite the economic recovery post-Covid, the level of debt in credit cards and auto loans, transitioning into delinquency remains higher than pre-pandemic levels. In Q4 2023, aggregated delinquency rates reached 3.1%, signifying a persistent financial strain for many lower income households. Transition rates into delinquency increased across all debt categories except for student loans. Approximately 8.5% of credit card balances and 7.1% of auto loans transitioned into delinquency on an annualised basis. Serious credit card delinquencies (90 days +) surged across all age groups, especially among younger borrowers, surpassing pre-pandemic levels. With such elevated debt and early-stage delinquency rates, lenders face many challenges. We look at how predictive modelling and optimization can maximize recovered amounts with a focus on Next Best Action assignment. Collections managers and teams within financial institutions face a range of challenges in maintaining portfolio growth while effectively managing increases in early-stage delinquencies. The top five challenges include: 1. High operating costs Contacting delinquent customers, negotiating payments, and managing recovery efforts entail labor-intensive and costly processes. This encompasses expenses related to staffing call centres, sending mailers, and deploying collections management software. 2. Regulatory compliance Navigating federal, state, and local regulations governing debt collection practices presents a complex challenge. Compliance with laws such as the Fair Debt Collection Practices Act (FDCPA) and the Telephone Consumer Protection Act (TCPA) is imperative, dictating the permissible methods and timing of borrower contact. 3. Customer retention and satisfaction Balancing effective debt recovery with maintaining positive customer relationships is essential. Employing aggressive collection tactics risks damaging customer relationships and tarnishing brand reputation, potentially impacting long-term customer retention. 4. Technological integration Incorporating modern technologies like machine learning, and automation into the collections process can enhance efficiency but poses implementation challenges. These technologies require substantial investment and expertise to streamline operations effectively. 5. Data management and predictive analytics Efficiently managing and analyzing vast amounts of data to identify at-risk accounts early and customise collection strategies is a significant endeavour. Accurate data analysis is pivotal for predicting delinquencies likely to self-cure and determining appropriate contact channels, such as; SMS, Email, Phone, Outbound IVR or social media. Applying a customer-centric, strategic approach These challenges underscore the critical need for credit lenders to adopt strategic, compliant, and customer-centric approaches to early-stage delinquency management. Currently, financial institutions use a multitude of strategies to maximize revenue collection. These range from data-driven customer segmentation to profile customers, Regulatory Technology (RegTech) for compliance, proactively identifying vulnerable customers needing financial relief, offering flexible repayment solutions and predictive modelling. Some credit lenders are also using machine learning models, such as Next Best Action (NBA) to personalize collection strategies based on customer behaviour, financial status, and communication preferences. This approach predicts recovery rates by tailoring channel contact to each individual customer in the most effective way. However, NBA models alone are not enough. To maximise collections, within known business constraints (call centre resources, budget, regulations), NBA needs to be augmented with non-linear optimization techniques to ensure not only the right communication preferences are adhered to, but also the business constraints mentioned above. Without the optimisation component businesses are left with NBA modelling that is unadjusted for business constraints. Next Best Action (NBA) Optimization NBA optimization presents a game-changing opportunity for lenders, particularly given the current economic challenges consumers are facing. Here's how NBA optimization can drive value: Personalized communication NBA optimization uses sophisticated customer modelling to pinpoint the most effective communication channels for each borrower, be it email, text, phone, or another preferred method. By personalizing communications, lenders significantly increase the chance of response and engagement from customers, which will also streamline the collections process with greater efficiency and reduced intrusion. Dynamic strategy adjustment NBA solutions continuously learn from outcomes, enabling strategy adjustments. This dynamic capability empowers lenders to swiftly adapt to changing economic conditions, borrower behaviours, and regulatory landscapes, ensuring the maintenance of effective collections practices. Optimized timing Leveraging predictive modelling, NBA optimization empowers lenders to identify the best times to contact their customers. This strategic approach ensures their communication attempts yield higher success rates, minimizing the need for repeated contacts and reducing operational costs. Regulatory compliance NBA optimization solutions can be configured to seamlessly adhere to regulatory requirements, including permissible contact times and frequency limits. This automation ensures compliance, protecting lenders from legal penalties and upholding their standing with regulatory bodies. Operational efficiency Through automated decision-making processes, NBA optimization assists lenders in allocating resources more thoughtfully. By prioritizing accounts with higher payment probabilities and determining the most cost-effective collection strategies, lenders can streamline operations and minimize costs. Improved customer experience (CX) NBA optimization facilitates a tailored approach to debt collection, significantly enhancing the borrower's experience. By considering the borrower's unique circumstances and preferences, lenders can offer more relevant and flexible repayment options, while also boosting customer satisfaction and loyalty. By implementing NBA optimization customised to channel contact preferences and operational constraints, lenders can navigate the complexities of early-stage collections with precision. This strategic approach not only addresses operational challenges but also aligns with the evolving expectations and financial pressures of consumers, leading to improved outcomes for both lenders and borrowers. Businesses can assign the most profitable, cost-effective treatment and channel to contact customers. Ascend Intelligence Services™ Collect delivers an optimized collections decision strategy, driven by predictive analytics, that determines the next best action and contact channel for each individual customer to improve recovery rates, increase efficiency, and stay within day-to-day constraints and regulatory requirements. Find out more