Strategy & Operations

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.

we explore what businesses are telling us about AI adoption in lending, examining the key barriers and challenges shaping progress.

Grounded in analyst insight, Experian research, and market signals, Experian’s Global Insights 2026: Predictions for credit and fraud risk explores how organisations are responding to rapid change.

Experian recently conducted a global study of 511 financial institutions in the United States, the United Kingdom, and Brazil to explore how firms are navigating the compliance challenge. Find out more about automated model documentation.

Why agile data integration is key to profitability and reduced time-to-market for lenders, and how businesses are looking to cloud, alternative data sources and self-serve to enable this opportunity.

Experian named a leader in Liminal’s Link Index for ATO Prevention in Banking - we look at why identity management integration is key.

We look at how CLO enables lenders to tailor credit limits to individual customers, enhancing profitability while mitigating risk.

How predictive modelling and optimization can maximize recovered amounts with a focus on Next Best Action assignment.

With the potential annual value of AI and analytics for global banking estimated to reach $1 trillion,1 financial institutions are seeking out efficient ways to implement insights-driven lending. As regulators continue to supervise risk management, lenders must balance the opportunity presented by AI to determine risk more accurately while growing approval rates and reducing the cost of acquisition, with the ability to explain decisions. The challenge of using AI in building credit risk models In a recent study conducted by Forrester Consulting on behalf of Experian, the top pain points for technology decision makers in financial services were reported to be automation and availability of data.2 The implementation of accessible AI solutions in credit risk management allows businesses to improve efficiency and time-to-market metrics by widening data sources, improving automation and decreasing risk. But the implementation of AI and machine learning in credit risk models can pose other challenges. The study also found that 31% of respondents felt that their organization could not clearly explain the reasoning behind credit decisions to customers.2 Although AI has been proven to improve the accuracy of predictive credit risk models, these advancements mean that many organizations need support in understanding and explaining the outcomes of AI-powered decisions to fulfil regulatory obligations, such as the Equal Credit Opportunity Act (ECOA). Moving from traditional model development methodologies to Machine Learning (ML) As lenders move away from traditional parametric models like logistic regression, to ML models like neural nets or tree-based ensemble methods, explainability becomes more complex. Logistic regression has for many years allowed for a clear understanding of the linear relationships between model attributes and the outcome (approval or decline). Once the model is estimated, it is completely explainable. However, ML models are non-parametric, so there are no underlying assumptions made around the distribution (shape) of the sample. Furthermore, the relationships between attributes and outcomes are not assumed to be linear – they’re often non-linear and complex, involving interactions. Such models are perceived to be black boxes where data is consumed as an input, processed and a decision is made without any visibility around the inner dynamics of the model. At the same time, it is possible for ML models to perform better when accurately classifying good customers and those deemed delinquent. Ensuring transparency and explainability is crucial – lenders must be able to identify and explain the most dominant attributes that contribute towards a decision to lend or not. They must also provide ‘reason codes’ at the customer level so any declined applicants can fully understand the main cause and have a path to remediation. The importance of developing transparent and explainable models By prioritizing the development of transparent and interpretable models, financial institutions can also better foster equitable lending practices. However, fair credit decisioning goes beyond the regulatory and ethical obligations - it also makes business sense. Unfair lending leads to higher default rates if creditworthiness is not accurately assessed, therefore increasing bad debts. Removing demographics considered to be the ‘unscored’ or ‘underserved’ (those who are credit worthy but do not have a traditional data trail, but instead a digital footprint comprised of alternative data) can also limit portfolio opportunity for businesses. For these reasons, it is critical to remove or minimize model bias. Bias is an upstream issue that starts at the data collection stage and model algorithm selections. Models developed using logistic regression or machine learning algorithms can be made fairer through carefully selecting attributes relevant to credit decisioning and avoiding sensitive attributes like race, gender, or ethnicity. Wherever sensitive metrics are used, they should be down-weighted to suppress their impact on lending decisions. Some other techniques to mitigate bias include: Thoroughly reviewing the data samples used in modelling. Fair Model Training - Train models using fairness-aware techniques. This may involve adjusting the training process to penalise any discrimination that creeps in. According to Forrester, an essential component of a decisioning platform is one that can “harness the power of AI while enhancing and governing it with well-proven and trusted human business expertise. The best automated decisions come from a combination of both.”3 Developing explainable models goes some way towards reducing bias, but making the decisions explainable to regulatory bodies is a separate issue, and in the digital age of AI, can require deep domain expertise to fulfil. While AI-powered decisioning can help businesses make smarter decisions, they also need the ability to confidently explain their lending practices to stay compliant. With the help of an expert partner, organizations can gain an understanding of what contributed most to a decision and receive detailed and transparent documentation for use with regulators. This ensures lenders can safely grow approval rates, be more inclusive, and better serve their customers. “The solution isn’t simply finding better ways to convey how a system works; rather, it’s about creating tools and processes that can help even the deep expert understand the outcome and then explain it to others.”McKinsey: why businesses need explainable ai and how to deliver it Experian’s Ascend Intelligence ServicesTM Acquire is a custom credit risk model development service that can better quantify risk, score more applicants, increase automation, and drive more profitable decisions. Find out more Confidently explain lending practices:Detailed, rigorous, and transparent documentation that has been proven to meet the strictest regulatory standards. Breaking Machine Learning (ML) out of the black box:Understand what contributed most to a decision and generate adverse action codes directly from the model through our patent-pending ML explainability.References: "The executive's AI playbook," McKinsey.com. (See "Banking," under "Value & Assess.") In a study conducted by Forrester Consulting on behalf of Experian, we surveyed 660 and interviewed 60 decision makers for technology purchases that support the credit lifecycle at their financial services organisation. The study included businesses across North America, UK and Ireland, and Brazil. 2023_05_Forrester_AI-Decisioning-Platforms-Wave.pdf https://www.mckinsey.com/capabilities/quantumblack/our-insights/why-businesses-need-explainable-ai-and-how-to-deliver-it Contributors:Masood Akhtar, Global Product Marketing Manager

Lenders are using automation across the credit lifecycle and intend to invest further in the next 12 months. We look at the use cases for automation and address the key challenges lenders face when automating decisions.

Authorised Push Payment fraud is growing, and as regulators begin to take action around the world to try to tackle it, we look at what financial institutions need to focus on now.

We asked lenders how much Gen AI plays a role in their credit customer lifecycle processes and what Gen AI adoption looks like for them