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Harry Singh, SVP of Global Decisioning discusses how synthetic and alternative data can help lenders navigate the current credit risk challenges

As we enter the beginning of the end of this global crisis, the role of data, analytics, and credit risk decisioning takes on even greater significance than before. Consumers face uneven roads to recovery, with some ready to spend again and others still mired in pandemic-related financial stress. And businesses of all sizes report their operations are recovering but there’s still a way to go. A key difference we saw is that companies that adapted to serve customer needs digitally are faring much better. Our 2021 Global Decisioning eBook, Navigating a new era of credit risk decisioning, looks at how consumers are stabilizing their finances and how businesses are returning to growth. A recent survey among 9,000 consumers and 2,700 businesses across ten countries worldwide reveals the importance of lenders prioritizing digital transformation, and the role of advanced data and analytics in enhancing the customer experience. The pandemic fall-out is impacting everyone differently: 1 in 3 consumers remains concerned about their finances – paying bills and managing credit Whereas high-income households are no longer reducing their discretionary spending Navigating this varied credit landscape requires a deep understanding of customer needs on both ends of the spectrum. However, business confidence in the consumer credit risk management analytics models dropped over the past year from 71 percent to 61 percent. Smaller lenders with revenues ranging from $10M to $49M have seen the sharpest decline from 72 percent to 57 percent in the past six months. Adapting data and analytics to a rapidly changing customer base: Almost 50% of businesses surveyed said their dedicate more resources to enhance analytics One-third of businesses are planning to re-build their models from scratch Recalibrating credit models is one thing, but lenders also need to rethink their data sources to better understand current customer profiles. The data inputs generated by the pandemic have impacted credit risk models and machine learning applications in unexpected ways. For example, widespread payment holidays and government stimulus programs may be masking customers’ true financial circumstances. According to Recovery Insights, a separate study published by Experian North America: Delinquency prior to the pandemic is a strong indicator of future risk. Accounts exiting an accommodation period are 2x more likely to become delinquent than are accounts that never received an accommodation. Payment on debt during accommodation indicated a reduced risk for subsequent delinquency. Amidst the pandemic lockdown, consumers turned online to manage finances and connect with lenders – including older consumers.  And while the pandemic pushed consumers online out of necessity, now that they’re there – it’s become a preference – as overall digital gains are holding above pre-pandemic levels. Lenders have a new digital imperative to meet consumers’ evolving needs for continued digital engagement. Consumer expectations of digital experiences 55% of consumers have higher expectations of their digital experience since Covid-19 began 43% of consumers surveyed age 70+ reported digital banking throughout the pandemic 14% of consumers surveyed age 60-69 applied for a new loan or card online The importance of a digital-first approach has revealed itself and many companies have put a digital customer journey in place since Covid-19 began. The future, however, is more than providing online services. It’s about knowing your customers well enough to anticipate their credit needs and using tools to automate the process and reduce risk. Adapt or lose customers 9 in 10 businesses have a digital customer journey in place 1 in 4 consumers have taken their business elsewhere because a company didn’t adapt to their digital needs Online customer experience and credit risk management are more connected than ever before. And, businesses need technology that supports the entire customer journey, from onboarding to customer management to collections. Five digital investments businesses are prioritizing the new era of credit risk management: Implement new machine learning models for customer decisions Increase digital acquisitions and engagement Understand their customer base (affordability, value, behavior) Automate customer decisions Increase value of existing customers Access the report here to get more consumer trends and find out what the future of decisioning means for businesses looking to return to growth. Stay in the know with our latest insights:

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We talk to Neil Stephenson, Vice President of Strategic Client Development, about how businesses can address a lack of data

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Shri Santhanam, speaks to Forbes' Peter High on his Technovation podcast about Experian's Analytics and AI solutions.

Over the past decade, advances in data availability, modelling techniques and machine learning have materially improved predictive performance across retail banking. Credit risk is more finely segmented, fraud detection more adaptive, and customer insight more granular. At the same time, expectations of analytics are changing. Chartis Research, in its inaugural Retail Banking Analytics50, evaluates providers based on how they help financial institutions use analytics to inform strategy, modelling and go-to-market decisions. Experian was recognised as one of the top vendors in the Retail Banking Analytics 50, receiving awards for Best Overall Strategy, Retail Analytics as a Service, and its Retail Analytics Governance Framework. This reflects a broader shift in emphasis – from model performance in isolation to how analytics is applied across the business. From analytical capability to decisioning systems Most financial institutions now operate with a substantial portfolio of models across acquisition, underwriting, fraud and customer management. Many of these models perform well in isolation. The challenge is how they are applied in practice. In many organisations, analytics still sits across fragmented environments, with separate data layers, different deployment approaches and limited feedback between decisions and outcomes. This does not prevent progress, but it does make it harder to achieve consistency at scale. What is emerging instead is a more integrated approach, where analytics is treated as a continuous system across the lifecycle. In its summary, Chartis notes, "Financial institutions are increasingly prioritizing platforms that are straightforward to implement and offer tightly integrated capabilities across the value chain.” Strategy: aligning analytics to outcomes Aligning analytics to business outcomes remains one of the more complex aspects to execute. Different functions optimise for different objectives. Data definitions are not always consistent. Decision strategies evolve independently over time. Even where underlying models are strong, this can lead to divergence in how decisions are made. Addressing this depends on shared data foundations, reusable features and clearer feedback loops between decisions and outcomes. In practice, achieving this level of alignment remains a work in progress for many organisations. Delivery: enabling scalable execution Cloud-based, API-enabled environments are making it easier to deploy models, update them more frequently and embed decisioning into operational workflows. They also allow multiple models to be applied within a single decision process. However, adoption remains uneven. Many financial institutions continue to operate hybrid environments, where newer capabilities sit alongside legacy infrastructure. This can introduce friction, particularly when scaling changes across multiple decision points. Chartis highlights "financial institutions increasingly are adopting a modular approach to retail analytics, seeking best-in-class external solutions rather than relying solely on legacy systems.” This changes how analytics is consumed, but also increases the importance of how it is integrated into decisioning processes. Governance: supporting scale and confidence As analytics becomes more embedded in decisioning, governance is becoming more operational. Expectations around data quality, model explainability and regulatory compliance continue to increase. At the same time, governance approaches are evolving, moving from periodic validation towards more continuous monitoring and control. The challenge is how governance is implemented. When embedded into development and deployment workflows, it can support scale and consistency. When applied retrospectively, it often introduces delay. Chartis states, “solutions that strengthen governance across data, model risk, controls and compliance also streamline regulatory alignment, reducing the operational burden.” For many institutions, embedding this consistently across the lifecycle remains an area of ongoing development. A more realistic benchmark for analytics success Improvements in model performance increase the need for consistent deployment. Faster deployment introduces new governance requirements. Greater alignment depends on more standardised data and features. As a result, analytics success is increasingly defined at the system level. More broadly, this points to a shift towards evaluating how effectively analytics can be applied across the lifecycle, rather than how individual models perform in isolation. For most institutions, progress will be incremental rather than immediate. The next phase of value will come not from isolated advances in modelling, but from the ability to apply those advances consistently across the business, with the right balance of scalability, control and flexibility. Winner's Summary Chartis’ Retail Banking Analytics50 and the winner’s summary provide additional detail on the capabilities shaping retail banking analytics, including integration across the value chain, analytics as a service and governance.

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Experian has been named as a Leader in the IDC MarketScape: Worldwide Identity Verification Financial Services 2025 Vendor Assessment