Tag: credit decisioning

In today’s fast-moving financial services landscape, fintechs face a dual challenge: scaling profitably while managing increasingly complex risk. From credit underwriting to fraud prevention, every decision carries both opportunity and exposure. That’s why forward-looking fintech leaders are turning to data-driven credit risk management strategies to sharpen decision-making, enhance compliance and unlock growth. Why data-driven risk management matters in fintech Fintechs are navigating an environment shaped by rapid innovation, shifting regulations and evolving consumer expectations. Within this landscape, three challenges come to the forefront: Evolving fraud threats: Fraudsters are advancing quickly, exploiting digital onboarding and consumer data. Siloed functions: Traditionally, credit, fraud and compliance were separate, but as fraud detection becomes a higher priority, forward-looking companies are now integrating these functions, with84% planning to share more data across the industry to help prevent fraud.1 Operational complexity: Fintechs must balance growth with compliance, often with lean teams, tech-debt that demands a strong return on investment (ROI)and aggressive timelines. These challenges make it clear that static, one-dimensional risk measures are no longer sufficient. By leveraging a unified decisioning platform that incorporates behavioral data and advanced analytics, fintechs can gain a more holistic view of consumer financial behavior. This broader perspective not only improves the accuracy of credit assessments but also strengthens defenses against sophisticated fraud threats. Driving efficiency through automation A data-driven risk management strategy is only as effective as its ability to be executed at scale. This is why automation is no longer a nice-to-have, but a competitive necessity in an industry defined by speed, complexity and rising consumer expectations. By embedding automation into credit and fraud risk management processes, fintechs can create systems that are more efficient, resilient and compliant. Key advantages include: Increased underwriting efficiency: Combined with data-driven insights, automated decisioning platforms allow fintechs to evaluate applications quickly and more accurately, resulting in faster and fairer credit decisions. Portfolio growth: Leveraging expanded data and automation allow enables smarter customer segmentation and more precise risk-based pricing, driving broader market reach and greater profitability. Fraud mitigation: Automated identity verification helps fintechs quickly validate customers, reduce friction in the onboarding process and block fraudulent activity before it impacts portfolios. Regulatory readiness: Unified, automated risk processes enable fintechs to adapt quickly to regulatory shifts, fraud trends and market disruptions, building long-term sustainability. Comparing legacy and modern credit risk approaches in fintech Data and automation have become essential for executing risk strategies at scale, highlighting just how far credit risk management has evolved. Below are key differences between traditional and modern approaches to credit risk. FeatureLegacy approachData-driven approachRisk detectionPoint-in-time scoresTrajectory-based modelingFraud preventionManual reviewAutomated, behavioral analyticsComplianceSiloed functionsUnified decisioning platformCustomer experienceSlow, manualFast, fair, automated Why fintechs choose Experian® As fintechs navigate an environment of increasing regulation, fraud sophistication and consumer expectations, the winners will be those who embrace a data-driven, automated and converged approach to credit and fraud risk management. Experian offers fintechs a partner with unmatched data accuracy, robust alternative data capabilities and end-to-end decisioning solutions designed for today’s converged risk landscape, including: Trended 3DTMattributes capture 24 months of key consumer credit activity, enabling fintechs to better manage portfolio risk and determine next best actions. Cashflow Score leverages consumer-permissioned banking transaction data to predict the likelihood of a borrower going 60+ days past due in the next 12 months, providing deeper visibility into financial health and repayment capacity. PowerCurve® is a unified, automated decision engine that incorporates data, strategy design, decision automation and detailed monitoring and reporting to help fintechs streamline credit decisions with speed and consistency. Our behavioral analytics capabilities, powered by NeuroID, provide a seamless, invisible gauge of user risk, allowing fintechs to proactively mitigate fraud while creating a secure, low-friction customer experience. Frequently asked questions What is data-driven risk management in fintech? It’s the application of advanced analytics, behavioral data and automation to help fintechs improve credit risk assessment, fraud prevention and compliance in digital-first environments. How does automation help fintechs manage credit risk? Automation enables fintechs to scale efficiently by streamlining underwriting, minimizing manual errors and ensuring consistent decision-making. What are the benefits of unified decisioning platforms? Unified platforms integrate credit, fraud and compliance decisions into a single workflow, helping fintechs onboard customers faster, respond quickly to fraud threats and maintain compliance without slowing down innovation. Discover how our fintech solutions can help your fintech strengthen credit risk management, reduce fraud and accelerate growth. Learn more 1Experian Vision

Credit decisioning has traditionally relied on static data like credit bureau scores, income statements, and past repayment history. As financial behavior becomes more dynamic and consumer expectations shift toward instant decisions, real-time data is emerging as a powerful tool in reshaping how lenders assess risk.

While today’s consumers expect a smooth, frictionless digital experience, many financial institutions still rely on outdated technology and manual reviews to acquire new customers. These old processes can prevent lenders from making accurate and timely credit decisions, leading to lost opportunities, revenue, and goodwill. By optimizing their customer acquisition strategies, financial institutions can allocate their resources effectively and say yes to consumers faster. This guide will walk you through the current challenges facing customer acquisition and how robust optimization strategies can help. Current challenges in customer acquisition To stay competitive and engage high-value customers, you’ll need an efficient customer acquisition process that weeds out both fraudulent actors and risky consumers. However, achieving this balancing act comes with a unique set of challenges. Because today’s consumers can access goods and services almost anywhere online at any time, more than 54 percent of customers expect a heightened digital and frictionless experience. Failing to meet this expectation can lead to huge losses for lenders. Some of the most common challenges in customer acquisition include: Although 52 percent of consumers prefer digital banking options over visiting branches in person, many lenders still rely on paper documents, which can add weeks to the onboarding process. Requiring consumers to provide substantial information about themselves during an application process can lead to abandoned applications. 67 percent of consumers will leave an application if they experience complications. Verifying consumer identities is growing increasingly important. In fact, about 35 percent of customers drop out of digital onboarding because their identity can't be confirmed. Poorly defined campaign planning can cause businesses to market to the wrong population segments, resulting in wasted time and resources. What is optimization for customer acquisition?  Customer acquisition optimization is the process of implementing new methods and solutions to make acquiring new customers more efficient and cost-effective. For lenders, this means streamlining steps in the credit decisioning process to focus on the right prospects and reduce friction. What types of processes can be optimized for customer acquisition?  You might be surprised just how many processes can be optimized for customer acquisition. Here are just a few examples: Having a holistic view of consumers allows you to take the guesswork out of targeting so you can better identify and engage high-potential customers. Utilizing predictive and lifestyle data enables you to pinpoint a more precisely segmented audience for marketing. Digital application solutions that reach across multiple channels, allowing applicants to leave one channel and pick up right where they left off in another. Real-time identity verification and fraud detection during onboarding and after, helping expedite approvals and mitigate risks. Utilizing API integration to leverage multiple metrics beyond credit scores when screening applicants' financial situation. Building custom risk models that pair to your existing data so you can say yes to more customers and better manage portfolio risk. Benefits of customer acquisition optimization Optimization can bring numerous benefits to your business, providing a faster return on investment. Here are some examples. By better pinpointing your marketing through predictive and lifestyle data, you can achieve increased conversions. Faster onboarding with less friction helps retain more customers. Real-time fraud detection and identity verification reduce customer roadblocks, allowing you to realize significant growth. Custom risk models and decisioning platforms can pair your data with additional data elements, providing more than just a credit score rating for your applicants. This can help you say yes to more customers. Using AI and machine learning tools will reduce the need for manual reviews and thus increase booking rates and applications. A real-life example of these benefits can be found with the Michigan State University Federal Credit Union (MSUFCU.) With over $7.2 billion in assets and 330,000 members, the client was manually reviewing all its applications. Experian reviewed the client's risk levels and approvals, comparing their risk and bankruptcy scores to determine which were most predictive. This analysis led Experian to recommend a new decisioning platform (PowerCurve Originations®) for instant credit decisions, an alternative data score tool, and Experian Advisory Services for risk-based pricing. After implementing these optimization solutions, MSUFCU saw a 55 percent increase in average monthly automations, four times improved online application response time and began competing more effectively in the marketplace. How Experian can help Experian offers a number of customer acquisition tools, allowing companies to be more responsive in an increasingly competitive market, while still reducing fraud risk. These tools include: Acquisition optimization marketing Experian offers a web-based platform that lets clients manage their marketing efforts all in the same place. You can upload and enhance client files, identify lookalike prospects, and use firmographic and credit data to get a holistic view of your clients and your prospects. Data-driven acquisition and decisioning engine PowerCurve Originations® is a data-driven decisioning engine that accepts applications from multiple channels, automates data collection and verification and proactively monitors decision results. It's flexible enough to reach across multiple channels, letting customers set aside their application in one digital channel and resume where they left off in another. It also provides businesses with access to comprehensive data assets, proactive monitoring and streamlined development with minimal coding. Enhanced fraud detection and identity verification Experian's Precise ID® is a risk-based fraud detection and prevention platform that provides analytics to accurately verify customers and mitigate fraud loss behind the scenes, ensuring a smoother onboarding process. Robust consumer attributes for better customized models Experian gives clients access to a wider berth of consumer attributes, helping you better screen applicants beyond just looking at credit scores. Trended 3DTM attributes let you uncover unique patterns in consumers' behavior over time, allowing you to manage portfolio risk, build better models and determine the next best actions. Premier AttributesSM aggregates credit data at the most granular and meaningful levels to provide clear insights into consumer credit behavior. It encompasses more than 2,100 attributes across 51 industries to help you develop highly predictive custom models. Enterprise-wide credit decisioning engine Experian's enterprise-wide credit decision platform lets you combine machine learning with proprietary data to return optimized decisions and quickly respond to requests. Robust credit decisioning software lets you convert data into meaningful actions and strategies. With Experian's machine learning decisioning options, companies are realizing a 25 percent reduction in manual reviews, a 25 percent increase in loan and credit applications and a 26 percent increase in booking rates. Highly predictive custom models Experian's Ascend Intelligence ServicesTM can help you create highly predictive custom models that create sophisticated decisioning strategies, allowing you to accurately predict risk and make the best decisions fast. This end-to-end suite of solutions lets you achieve a more granular view of every application and grow portfolios while still minimizing risk. Experian can help optimize your customer acquisition Experian provides a suite of decisioning engines, consumer attributes and customized modeling to help you optimize your customer acquisition process. These tools allow businesses to better target their marketing efforts, streamline their onboarding with less friction and improve their fraud detection and mitigation efforts. The combination can deliver a powerful ROI. Learn more about Experian's customer acquisition solutions. Learn more