Fintech

Learn how Terrace Finance used NeuroID behavioral analytics to detect fraud faster, respond to attacks, and strengthen risk management.

Customers rarely announce their departure. Instead, they quietly reduce engagement, move deposits and explore competing offers. By the time attrition shows up in reporting, competitors may have already captured meaningful wallet share. For lenders, the risk isn’t just lost accounts, it’s silent revenue erosion within relationships that still appear intact. The hidden risk in your portfolio Today’s consumers often hold less than half of their deposits or loans with a single provider. At the same time: Competition for prime borrowers continues to intensify. Cross-sell remains one of the most effective and efficient growth strategies available. Even small improvements in retention can drive outsized profitability gains. The opportunity is real, but only if you can see momentum early and act before competitors do. From static reviews to strategic signals Traditional monthly and quarterly reviews confirm what has already happened, but they rarely surface early indicators like emerging behavioral shifts or improving credit capacity. Modern portfolio management requires continuous visibility into behavioral signals, trended credit data and event-based triggers that highlight change as it happens. When you can see momentum forming, you can act with precision, intervene before balances leave, engage customers as capacity strengthens, and activate compliant prescreen cross-sell campaigns at the right moment. Our new interactive strategic snapshot outlines the modern approach to portfolio management, one that connects ongoing account review with timely, event-based signals, helping you protect, retain and grow high-value customers. Download it now to see how to turn early signals into stronger customer lifetime value. Read the snapshot

Financial services leaders are dealing with numerous pressures at the same time. These growing challenges for financial services organizations include sophisticated fraud, rapid Artificial Intelligence (AI) adoption without clear regulatory direction, rising customer expectations and the need for compliant, sustainable growth. Businesses are rethinking how they manage risk, growth and customer trust. These financial industry challenges are no longer confined to internal risk teams. They directly impact long-term customer loyalty. How organizations navigate these challenges will determine how effectively they deliver value to their customers. We’ve outlined the six challenges for financial services oranizations that consistently rank highest among industry leaders today. Challenge 1: Fraud is becoming harder to detect and eroding customer trust 72% of business leaders expect AI-generated fraud and deepfakes to be major challenges by 20261 As fraud tactics evolve quickly, driven in part by AI, customers are being targeted through identity-based attacks from account takeovers to synthetic identities and misuse of personal information. When these threats go undetected, or when legitimate activity is incorrectly flagged, the result isn’t just financial loss. It’s a breakdown of trust. Organizations that want to stay ahead must move beyond isolated fraud controls. By embedding identity management and monitoring into the customer experience, organizations can move from reactive fraud response to proactive identity protection. Identity theft protection and monitoring help organizations turn fraud prevention into a visible, trust-building experience for customers — offering early alerts, guidance, and peace of mind when identity risks arise. Challenge 2: AI decisions must be trusted by customers, not just regulators 76% of businesses say implementing responsible AI is one of their biggest challenges2 As AI becomes more embedded in financial services, it shapes the experiences customers see every day. From credit decisions to eligibility outcomes and personalized offers. While AI can drive faster and more inclusive decisions, it also introduces a new expectation: customers want to understand why a decision was made. Responsible AI is no longer just about regulatory compliance. It’s about delivering outcomes that feel fair, consistent and easy to understand. When decisions appear unclear, confidence erodes. When organizations can clearly explain outcomes, not just internally, they build confidence across regulators, partners and customers. This allows AI to scale responsibly while reinforcing trust in every interaction. Financial wellness tools such as credit scores, reports and education help make AI-driven decisions more transparent, giving customers clarity into outcomes and confidence in how their financial health is assessed. Challenge 3: Digital experiences are failing to deliver clarity and confidence 57% of U.S. consumers remain concerned about conducting activities online3 Customer confidence is affected by day-to-day interactions such as onboarding, payments and issue resolution. Inconsistent decisions, unclear outcomes and friction in digital journeys can quickly erode confidence and increase confusion, disengagement and abandonment. Financial services leaders will need to rebuild and strengthen confidence. Improving key decision points with better data and analytics helps ensure customers receive timely insights, understandable outcomes and meaningful guidance, turning everyday interactions into opportunities to build stronger relationships. By delivering ongoing financial wellness insights and education, organizations can replace confusion with clarity — helping consumers better understand their financial standing and stay engaged over time. Challenge 4: Gen Z continues to raise the bar It's no secret that Gen Z stands out for its strong preference for digital financial services and digital interactions, but Gen Z is also pushing the envelope on financial wellness. 48% of Gen Z report that they do not feel financially secure, indicating strong demand for financial support and tools4 Their expectations for instant decisions, seamless digital experiences, transparency and tools that help them manage their financial lives are quickly becoming the baseline. To meet and exceed these expectations, financial institutions will need to support real-time, data-driven decisioning that adapt to individual needs. Delivering modern, app-like financial experiences, without compromising risk management. Increasingly, organizations are meeting Gen Z expectations by offering financial wellness and protection tools through employee benefits, supporting everyday financial confidence beyond traditional compensation. Challenge 5: Limited data limits meaningful consumer engagement 62 million U.S. consumers are thin-file or credit invisible under traditional credit scoring.5 Growth will always be a priority, but it must be responsible and inclusive. Traditional credit data alone often provides an incomplete picture of consumer financial behavior, limiting visibility and making it harder to confidently expand access. By incorporating alternative and expanded data, organizations can gain a more holistic view of consumers. This broader perspective supports smarter decisions, personalized insights and more inclusive engagement, which enables growth while maintaining compliance and managing risk responsibly. Expanded data supports more personalized financial wellness experiences, enabling organizations to provide relevant insights, responsible access and guidance tailored to individual consumer needs. Challenge 6: Disconnected decisions create inconsistent customer experiences Increasingly, fintech leaders are moving toward unified risk and decisioning strategies to deliver more personalized experiences6 While customers interact with a single institution, decisions are often made across disconnected data sources, systems and teams. These silos create inconsistent experiences, slow responses and operational complexities that customers feel directly through conflicting messages and uneven outcomes. Experian helps organizations break down these silos by unifying data, analytics and decisioning across the enterprise. When data incidents occur, integrated experiences enable faster data breach resolution, helping consumers understand what happened, take action, and recover with confidence. Looking ahead These challenges for financial services organizations are not emerging; they’re already here and reshaping how financial institutions engage with consumers. Leaders who proactively address financial industry challenges by connecting data, analytics, and responsible AI are better positioned to deliver trusted, transparent and meaningful experiences. Learn More References:1. https://www.experian.com/blogs/insights/2025-identity-fraud-report2. https://www.techradar.com/pro/businesses-are-struggling-to-implement-responsible-ai-but-it-could-make-all-the-difference3. https://www.experian.com/blogs/insights/2025-identity-fraud-report4. https://www.deloitte.com/global/en/issues/work/genz-millennial-survey.html5. https://www.experian.com/thought-leadership/business/the-roi-of-alternative-data6. https://us-go.experian.com/2025-state-of-fintech-report?cmpid=IM-2025-state-of-fintech-report-livesocial-share

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For financial institutions to achieve success, they need to develop high-performing models with easy access to top-tier data sources. It’s also important to focus on data governance, compliance, and risk management throughout the lending lifecycle. Industry leaders implement advanced analytics and AI solutions to improve their lending decisions, and they also incorporate integrated, efficient feature engineering into their business operations. What’s feature engineering? Feature engineering helps organizations turn raw data into comprehensive model development, following this process: Data collection Data cleaning and transformation Feature engineering Model training and evaluation Decision-making Effectively transforming data into valuable insights depends heavily on creating new custom features to enhance model performance, as well as the quality of the data being used. When data is fragmented or managed poorly, it can lead to increased operational costs, missed revenue opportunities, and compliance risks. Our feature engineering solution: Experian Feature Builder Financial institutions require optimized workflows that can accelerate development while supporting governance and ensuring transparency. Experian’s feature engineering tool, Experian Feature Builder, streamlines custom feature development and deployment across the modeling lifecycle. Providing access to 20+ years of proprietary data, Experian Feature Builder enables organizations to: Break data silos by creating unified access across multiple data types Ensure trust and compliance by embedding audit and lineage tracking at each stage Enable strategic agility with faster and more consistent feature experimentation, testing, and deployment Download our latest e-book to find out more about how Experian’s Feature Builder provides centralized feature development to accelerate time-to-market, enhance compliance, and minimize risk. Download the e-book