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As more consumers lean towards adaptable and efficient vehicles that fit their everyday lifestyle, it’s no surprise to see the nuanced shifts in consumer preferences over recent years. For instance, compact utility vehicles (CUVs) have resonated with those seeking versatility—emerging as the most registered new vehicle segment in the first quarter of 2024 at 51.1%, according to Experian’s Automotive Consumer Trends Report. When exploring the depths of CUV registrations, data showed Toyota led the market share for the non-luxury segment at 14.9% in Q1 2024. They were followed by Chevrolet (12.1%), Honda (11.4%), Subaru (10.4%), and Hyundai (10.0%). On the luxury side, Tesla accounted for 28.0% of the market share this quarter and Lexus trailed behind at 14.1%. Rounding out the top five were BMW (12.2%), Audi (8.6%), and Volvo (6.2%). CUV registration trends by generations It’s notable that different generations are drawn to CUVs for a multitude of personal preferences that align with their respective lifestyles. For example, Baby Boomers made up 32.3% of new retail registrations for CUVs and Gen X was close behind at 30.4% in Q1 2024. They were followed by Millennials (23.6%), Gen Z (7.9%), and the Silent Generation (5.4%). While some generations seek a vehicle that strikes a balance between practicality and comfort, others may prefer smaller and more maneuverable vehicles. Nonetheless, CUVs making up just over half of new retail registrations is something that should be watched closely. By leveraging multiple data points such as who is in the market for a CUV as well as the types of makes and models they’re interested in, professionals have the opportunity to strategize new ways to effectively reach shoppers. To learn more about CUVs, view the full report at Automotive Consumer Trends Report: Q1 2024. Or

Dealing with delinquent debt is a challenging yet crucial task, and when faced with economic uncertainties, the need for effective debt management and collections strategies becomes even more pressing. Thankfully, advanced analytics offers a promising solution. By leveraging data-driven insights, you can enhance operational efficiency, better prioritize accounts, and make more informed decisions. This article explores how advanced analytics can revolutionize debt collection and provides actionable strategies to implement treatment. Understanding advanced analytics in debt collection Advanced analytics involves using sophisticated techniques and tools to analyze complex datasets and extract valuable insights. In debt collection, advanced analytics can encompass various methodologies, including predictive modeling, machine learning (ML), data mining, and statistical analysis. Predictive modeling Predictive modeling leverages historical data to forecast future outcomes. By applying predictive models to debt collection, you can estimate each account's repayment likelihood. This helps prioritize your efforts toward accounts with a higher chance of recovery. Machine learning Machine learning algorithms can automatically identify patterns in large datasets, enabling more accurate predictions and classifications. For debt collectors, this means better segmenting delinquent accounts based on likelihood of repayment, risk, and customer behavior. Data mining Data mining involves exploring large datasets to unearth hidden patterns and correlations. In debt collection, data mining can reveal previously unnoticed trends and behaviors, allowing you to tailor your strategies accordingly. Statistical analysis Statistical methods help quantify relationships within data, providing a clearer picture of the factors influencing debt repayment and focusing on statistically significant repayment drivers, which aids in refining collection strategies. Benefits of advanced analytics in delinquent debt collection The benefits of employing advanced analytics in delinquent debt collection are multifaceted and valuable. By integrating these technologies, financial institutions can achieve greater efficiency, reduce operational costs, and improve recovery rates. Enhanced prioritization and decisioning With data and predictive analytics, you can gain a complete view of existing and potential customers to determine risk exposure and prioritize accounts effectively. By analyzing payment histories, credit scores, and other consumer behavior, you can enhance your collectoins prioritization strategies and focus on accounts more likely to  pay or settle. This ensures that resources are allocated efficiently, and decisions are informed, maximizing your return on investment. Watch: In our recent tech showcase, learn how to harness the power of our industry-leading collection decisioning and optimization capabilities.  Reduced costs Advanced analytics can significantly reduce operational costs by streamlining the collection process and targeting accounts with higher recovery potential. Automated processes and optimized resource allocation mean you can achieve more with less, ultimately increasing profitability. Better customer relationships With debt collection analytics, digital communication tools, artificial intelligence (AI), and ML processes, you can enhance your collections efforts to better engage with consumers and increase response rates. Adopting a more empathetic and customer-centric approach that embraces omnichannel collections can foster positive customer relationships. Implementing advanced analytics: A step-by-step guide Step 1: Data collection and integration The first step in implementing advanced analytics is to gather and integrate data from various sources. This includes payment histories, account information, demographic data, and external data such as credit scores. Ensuring data quality and consistency is crucial for accurate analysis. Step 2: Data analysis and modeling Once the data is collected, the next step is to apply advanced analytical techniques. This involves developing predictive models, training machine learning algorithms, and conducting statistical analyses to identify notable patterns and trends. Step 3: Strategy development Based on the insights gained from the analysis, you can develop targeted collection strategies. These may include segmenting accounts, prioritizing high-potential recoveries, and choosing the most effective communication methods. It’s essential to test and refine these strategies to ensure optimal performance continually. Step 4: Automation and implementation Implementing advanced analytics often involves automation. Workflow automation tools can streamline routine tasks, ensuring strategies are executed consistently and efficiently. Integrating these tools with existing debt collection systems can enhance overall effectiveness. Step 5: Monitoring and optimization Finally, continuously monitor the performance of your advanced analytics initiatives. Use key performance indicators (KPIs) to track success and identify areas for improvement. Regularly update models and strategies based on new data and evolving trends to maintain high recovery rates. Putting it all together Advanced analytics hold immense potential for transforming delinquent debt collection and can drive better return on investment. By leveraging predictive modeling, machine learning, data mining, and statistical analysis, financial institutions and debt collection agencies can perfect their collection best practices, prioritize accounts effectively, and make more informed decisions. Our debt collection analytics and recovery tools empower your organization to see the complete behavioral, demographic, and emerging view of customer portfolios through extensive data assets, advanced analytics, and platforms. As the financial landscape evolves, working with an expert to adopt advanced analytics will be critical for staying competitive and achieving sustainable success in debt collection. Learn more *This article includes content created by an AI language model and is intended to provide general information.

In this article…What is fair lending?Understanding machine learning modelsThe pitfalls: bias and fairness in ML modelsFairness metricsRegulatory frameworks and complianceHow Experian® can help As the financial sector continues to embrace technological innovations, machine learning models are becoming indispensable tools for credit decisioning. These models offer enhanced efficiency and predictive power, but they also introduce new challenges. These challenges particularly concern fairness and bias, as complex machine learning models can be difficult to explain. Understanding how to ensure fair lending practices while leveraging machine learning models is crucial for organizations committed to ethical and compliant operations. What is fair lending? Fair lending is a cornerstone of ethical financial practices, prohibiting discrimination based on race, color, national origin, religion, sex, familial status, age, disability, or public assistance status during the lending process. This principle is enshrined in regulations such as the Equal Credit Opportunity Act (ECOA) and the Fair Housing Act (FHA). Overall, fair lending is essential for promoting economic opportunity, preventing discrimination, and fostering financial inclusion. Key components of fair lending include: Equal treatment: Lenders must treat all applicants fairly and consistently throughout the lending process, regardless of their personal characteristics. This means evaluating applicants based on their creditworthiness and financial qualifications rather than discriminatory factors. Non-discrimination: Lenders are prohibited from discriminating against individuals or businesses on the basis of race, color, religion, national origin, sex, marital status, age, or other protected characteristics. Discriminatory practices include redlining (denying credit to applicants based on their location) and steering (channeling applicants into less favorable loan products based on discriminatory factors). Fair credit practices: Lenders must adhere to fair and transparent credit practices, such as providing clear information about loan terms and conditions, offering reasonable interest rates, and ensuring that borrowers have the ability to repay their loans. Compliance: Financial institutions are required to comply with fair lending laws and regulations, which are enforced by government agencies such as the Consumer Financial Protection Bureau (CFPB) in the United States. Compliance efforts include conducting fair lending risk assessments, monitoring lending practices for potential discrimination, and implementing policies and procedures to prevent unfair treatment. Model governance: Financial institutions should establish robust governance frameworks to oversee the development, implementation and monitoring of lending models and algorithms. This includes ensuring that models are fair, transparent, and free from biases that could lead to discriminatory outcomes. Data integrity and privacy: Lenders must ensure the accuracy, completeness, and integrity of the data used in lending decisions, including traditional credit and alternative credit data. They should also uphold borrowers’ privacy rights and adhere to data protection regulations when collecting, storing, and using personal information. Understanding machine learning models and their application in lending Machine learning in lending has revolutionized how financial institutions assess creditworthiness and manage risk. By analyzing vast amounts of data, machine learning models can identify patterns and trends that traditional methods might overlook, thereby enabling more accurate and efficient lending decisions. However, with these advancements come new challenges, particularly in the realms of model risk management and financial regulatory compliance. The complexity of machine learning models requires rigorous evaluation to ensure fair lending. Let’s explore why. The pitfalls: bias and fairness in machine learning lending models Despite their advantages, machine learning models can inadvertently introduce or perpetuate biases, especially when trained on historical data that reflects past prejudices. One of the primary concerns with machine learning models is their potential lack of transparency, often referred to as the "black box" problem. Model explainability aims to address this by providing clear and understandable explanations of how models make decisions. This transparency is crucial for building trust with consumers and regulators and for ensuring that lending practices are fair and non-discriminatory. Fairness metrics Key metrics used to evaluate fairness in models can include standardized mean difference (SMD), information value (IV), and disparate impact (DI). Each of these metrics offers insights into potential biases but also has limitations. Standardized mean difference (SMD). SMD quantifies the difference between two groups' score averages, divided by the pooled standard deviation. However, this metric may not fully capture the nuances of fairness when used in isolation. Information value (IV). IV compares distributions between control and protected groups across score bins. While useful, IV can sometimes mask deeper biases present in the data. Disparate impact (DI). DI, or the adverse impact ratio (AIR), measures the ratio of approval rates between protected and control classes. Although DI is widely used, it can oversimplify the complex interplay of factors influencing credit decisions. Regulatory frameworks and compliance in fair lending Ensuring compliance with fair lending regulations involves more than just implementing fairness metrics. It requires a comprehensive end-to-end approach, including regular audits, transparent reporting, and continuous monitoring and governance of machine learning models. Financial institutions must be vigilant in aligning their practices with regulatory standards to avoid legal repercussions and maintain ethical standards. Read more: Journey of a machine learning model How Experian® can help By remaining committed to regulatory compliance and fair lending practices, organizations can balance technological advancements with ethical responsibility. Partnering with Experian gives organizations a unique advantage in the rapidly evolving landscape of AI and machine learning in lending. As an industry leader, Experian offers state-of-the-art analytics and machine learning solutions that are designed to drive efficiency and accuracy in lending decisions while ensuring compliance with regulatory standards. Our expertise in model risk management and machine learning model governance empowers lenders to deploy robust and transparent models, mitigating potential biases and aligning with fair lending practices. When it comes to machine learning model explainability, Experian’s clear and proven methodology assesses the relative contribution and level of influence of each variable to the overall score — enabling organizations to demonstrate transparency and fair treatment to auditors, regulators, and customers. Interested in learning more about ensuring fair lending practices in your machine learning models?    Learn More This article includes content created by an AI language model and is intended to provide general information.