All posts by Masood Akhtar

Generative AI (GenAI) is transforming the financial services industry, driving innovation, efficiency and cost savings across various domains. By integrating GenAI into their operations, financial institutions can better respond to rapidly changing environments. GenAI is reshaping financial services from customer engagement to compliance, leading to streamlined operations and enhanced decision-making. The strategic role of GenAI in financial services Adopting GenAI in financial services is now a strategic imperative. A 2024 McKinsey report (The State of AI in 2024) notes more than a 10% revenue increase for companies using GenAI. As institutions strive to stay competitive, GenAI provides powerful tools to enhance customer experiences, optimize operations, accelerate regulatory compliance, and expedite coding and software development. Key areas where GenAI is making an impact Enhanced customer engagement Financial institutions use GenAI to offer personalized products and services. By analyzing real-time customer data, GenAI enables tailored recommendations, boosting satisfaction and retention. Streamlining and optimizing operations GenAI automates tasks like data entry and transaction monitoring, freeing up resources for strategic activities. This accelerates workflows and reduces errors. Further, GenAI-driven efficiency directly cuts costs. By automating processes and optimizing resources, institutions can lower overhead and invest more in innovation. Deloitte’s Q2 2024 study found AI automation reduced processing times by up to 60% and operational costs by 25%. Accelerating regulatory compliance GenAI simplifies compliance by automating data collection, analysis and reporting. This ensures regulatory adherence while minimizing risks and penalties. According to a 2024 Thomson Reuters survey, AI-driven compliance reduced reporting times by 40% and costs by 15%. Developer coding support for efficiencies GenAI is an invaluable tool for programmers. It aids in code generation, task automation and debugging, boosting development speed and allowing focus on innovation. Gartner’s 2024 research highlights a 30% improvement in coding efficiency and a 25% reduction in development timeframes due to GenAI. Accelerating credit analytics with Experian Assistant Within the credit risk management space, GenAI offers a powerful solution that addresses some known pain points. These relate to mining vast amounts of data for insight generation and coding support for attribute selection and creation, model development, and expedited deployment. Experian Assistant is a game-changer in modernizing analytics workflows across the data science lifecycle. Integrated into the Experian Ascend™ platform, it’s specifically designed for analytics and data science teams to tackle the challenges of data analysis, model deployment and operational efficiency head-on. Capabilities and skills of Experian Assistant Data tutor: Offers comprehensive insights into Experian’s data assets, enabling users to make informed decisions and optimize workflows Analytics expert: Provides tailored recommendations for various use cases, helping users identify the most predictive metrics and enhance model accuracy Code advisor (data prep): Automatically generates code for tasks like data merging and sampling, streamlining the data preparation process Code advisor (analysis): Generates code for risk analytics and modeling tasks, including scorecard development and regulatory analyses Tech specialist: Facilitates model deployment and documentation, minimizing delays and ensuring a seamless transition from development to production Driving more-informed decisions Adopting GenAI will be key to maintaining competitiveness as the financial services industry evolves. With projections showing significant growth in GenAI investments by 2025, the potential for enhanced efficiencies, streamlined operations and cost savings is immense. Experian Assistant is at the forefront of this transformation, addressing the bottlenecks that slow down analytical processes and enabling financial institutions to move faster, more informed and with greater precision. By integrating the capabilities of the Experian Assistant, financial institutions can leverage GenAI in credit risk management, automate data processes, and develop customized analytics for business decision-making. This alignment with GenAI’s broader benefits—like operational streamlining and improved customer experience—ensures better risk identification, workflow optimization, and more informed decisions. To learn more about how Experian Assistant can transform your data analytics capabilities, watch our recent tech showcase and book a demo with your local Experian sales team. Watch tech showcase Learn more

In today's data-driven business landscape, leveraging advanced targeting techniques is crucial for effective consumer engagement, particularly in the financial services sector. Prescreen targeting solutions have evolved significantly, offering a competitive edge through more precise and impactful outreach strategies. The power of data analytics and predictive modeling At the heart of modern prescreen targeting solutions lies the integration of extensive data analytics and predictive modeling. These systems combine detailed consumer information, including purchasing behaviors and credit scores, with sophisticated algorithms to identify potential customers most likely to respond positively to specific promotional campaigns. This approach not only streamlines campaign efforts but also enhances the tactical effectiveness of each interaction. Direct mail: a proven channel for financial services In the competitive North American financial services market, direct mail has demonstrated distinct advantages as a targeting channel. Its tangible nature helps cut through digital noise, capturing consumer attention effectively. For credit products, direct mail typically achieves engagement rates of 0.2-2% for prime consumers and 1-3% for near-prime and subprime consumers[1]. Key advantages of prescreen targeting solutions Enhanced response rates Custom response models can significantly boost prospect response rates by targeting a well-defined, high-propensity audience. These models have the potential to improve average response rates of prescreen direct mail campaigns by 10-25%. Risk mitigation By focusing on well-defined, high-propensity audiences, prescreen targeting via direct mail aims to attract the right prospects, minimizing fraud and delinquency risks. This targeted approach can lead to substantial savings on underwriting costs. Improved customer engagement and retention Personalized direct mail strengthens customer relationships by making recipients feel valued, leading to higher engagement and loyalty – crucial factors for long-term business success. Regulatory compliance and security Prescreen solutions come equipped with compliance safeguards, simplifying adherence to industry regulations and consumer privacy standards. This is particularly critical in the highly regulated financial sector. The future of targeting and enhancement As markets continue to evolve, the strategic importance of precise and efficient marketing techniques will only grow. Financial institutions leveraging optimized prescreen targeting and enhancement solutions can gain a significant competitive advantage, achieving higher immediate returns and fostering long-term customer loyalty and brand strength. Future advancements in AI and machine learning are expected to further refine prescreen targeting capabilities, offering even more sophisticated tools for marketers to engage effectively with their target audiences. Ascend Intelligence Services™ Target Ascend Intelligence Services Target is a sophisticated prescreening 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. Visit our website to learn more [1] Experian Research, Data Science Team, July 2024 

In this article...Recent trends in credit card debtThe rising tide of delinquenciesWhat is credit limit optimization?Benefits of credit limit optimizationEconomic indicators and CLO ImpactEnhanced profitability and risk mitigation This post was originally published on our Global Insights Blog. 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 CLO, it is essential to consider the current economic landscape. 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™ Limit, which provides an optimized strategy designed to enhance the precision and effectiveness of credit limit assignments. Ascend Intelligence Services™ 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. Ascend Intelligence Services 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, Ascend Intelligence Services 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 Ascend Intelligence Services Limit represents a forward-thinking approach to credit management, benefiting both lenders and consumers. Learn More * HOUSEHOLD DEBT AND CREDIT REPORT (Q1 2024) – Federal Reserve Bank of New York