At A Glance
AI in lending is moving from experimentation to enterprise-wide adoption, helping lenders make faster, more accurate decisions across the entire credit lifecycle. The future of AI in lending matters now as rising data complexity, economic uncertainty and regulatory expectations demand more efficient approaches to managing risk and growth.From chatbots to image generators, artificial intelligence (AI) has captured consumers’ attention and spurred joy — and sometimes a little fear. It’s not too different in the business world. There are amazing opportunities and lenders are increasingly turning to AI-driven lending decision engines and processes. But there are also open questions about how AI can work within existing regulatory requirements, how new regulations will impact its use and how to implement advanced analytics in a way that increases equitable inclusion rather than further embedding disparities.
How are lenders using AI today?
Many financial institutions are already using AI in lending — or actively testing AI-based lending tools — across the customer lifecycle to:
- Target the right consumers: With tools like Ascend Intelligence ServicesTM Target (AIS Target), lenders can better identify consumers who match their credit criteria and send right-sized offers, which enables them to maximize their acceptance rates.
- Detect and prevent fraud: Fraud detection tools have used AI and machine learning techniques to detect and prevent fraud for years. These systems may be even more important as new fraud risks emerge, from tried-and-true methods to AI-powered fraud threats.
- Assess creditworthiness: ML-based models can incorporate a range of internal and external data points to more precisely evaluate creditworthiness. When combined with traditional and alternative credit data*, some lenders can even see a Gini uplift of 60 to 70 percent compared to a traditional credit risk model.
- Manage portfolios: Lenders can also use a more complete picture of their current customers to make better decisions. For example, AI-driven models can help lenders set initial credit limits and suggest when a change could help them increase wallet share or reduce risk. Lenders can also use AI to help determine which up- and cross-selling offers to present and when (and how) to reach out.
- Improve collections: Models can be built to ease debt collection processes, such as choosing where to assign accounts, which accounts to prioritize and how to contact the consumer.
Additionally, businesses can implement AI-powered tools to increase their organizations’ productivity and agility. GenAI solutions like Experian Assistant accelerate the modeling lifecycle by providing immediate responses to questions, enhancing model transparency and parsing through multiple model iterations quickly, resulting in streamlined workflows, improved data visibility and reduced expenses.
Watch: “The Future of Underwriting: Rethink, Rebuild, Redefine” to explore the latest trends, like agentic AI, transforming lending decisioning.
The benefits of AI in lending
While lenders can apply machine learning in many areas, the primary drivers for adopting AI in lending include:
- Improving credit risk assessment
- Faster development and deployment cycles for new or recalibrated models
- Unlocking value from large and complex datasets
- Staying competitive in a data-driven lending market
Many AI-based lending use cases for machine learning solutions have a direct impact on the bottom line — improving credit risk assessment can decrease charge-offs.
Others are less direct but still meaningful. AI for lending can increase efficiency and allow further automation. This takes the pressure off your underwriting team, even when application volume is extremely high, and results in faster decisions for applicants, which can improve your customer experience.
By incorporating larger and more diverse datasets into their decisions also allows lenders to expand their lending universe without taking on additional risk. For example, they may now be able to offer risk-appropriate credit lines to consumers that traditional scoring models can’t score.
When applied across the full customer lifecycle, AI can help increase customer lifetime value by:
- Preventing fraud
- Improving retention
- Powering up- and cross-selling
- Streamlining for collection strategies
Hurdles to adoption of AI in lending
There are clear benefits and interest in machine learning and analytics, but adoption can be difficult, especially within credit underwriting.
A Forrester Consulting Study commissioned by Experian found that the top pain points for technology decision makers in financial services are automation and availability of data. These challenges often become more pronounced as lenders from experimentation to full-scale deployment.
Explainability comes down to transparency and trust. Financial institutions have to trust that machine learning models will continue to outperform traditional models to make them a worthwhile investment. The models also have to be transparent and explainable for financial institutions to meet regulatory fair lending requirements.
Resources, expertise, and integration
A lack of internal resources and expertise can also slow adoption. Building, validating, and deploying custom AI models takes time and ongoing investment.
- Large lenders may have in-house analytics teams, but often face integration challenges when introducing new models into legacy systems.
- Small and mid-sized institutions may be more agile, but frequently lack the in-house expertise needed to develop or operationalize AI for lending on their own.
Effective AI in lending depends on high-quality, well-governed data. Many organizations struggle with the time and cost required to clean, organize, and maintain internal datasets. While external data sources can enhance AI-based lending models, evaluating, integrating, and governing those datasets often requires significant effort.
How Experian is shaping the future of AI in lending
Lenders are finding new ways to use AI throughout the customer lifecycle and with varying types of financial products. However, while the cost to create custom machine learning models continues to decline, the complexities and unknowns are still too great for some lenders to manage. But that’s changing.
Experian built the Ascend Intelligence Services™ to help smaller and mid-market lenders access the most advanced analytics tools. The managed service platform can significantly reduce the cost and deployment time for lenders who want to incorporate AI-driven strategies and machine learning models into their lending process. The end-to-end managed analytics service gives lenders access to Experian’s vast data sets and can incorporate internal data to build and seamlessly deploy custom machine learning models. The platform can also continually monitor and retrain models to increase lift, and there’s no “black box” to obscure how the model works. Everything is fully explainable, and the platform bakes regulatory constraints into the data curation and model development to ensure lenders stay compliant.
* When we refer to “Alternative Credit Data,” this refers to the use of alternative data and its appropriate use in consumer credit lending decisions as regulated by the Fair Credit Reporting Act (FCRA). Hence, the term “Expanded FCRA Data” may also apply in this instance and both can be used interchangeably.
AI in lending refers to the use of artificial intelligence and machine learning technologies to support and automate decisions across the lending process. AI for lending helps financial institutions assess credit risk, detect fraud, personalize offers, manage portfolios, and improve operational efficiency. By analyzing large and complex datasets, AI-based lending models enable faster, more accurate, and more consistent decisions than traditional approaches
– Marketing and acquisition: Identifying and targeting the right consumers with relevant offers
– Underwriting: Improving credit risk assessment and decision accuracy
– Fraud prevention: Detecting suspicious activity and emerging fraud patterns
– Portfolio management: Optimizing credit limits, retention strategies, and customer engagement
– Collections: Prioritizing accounts and tailoring collection strategies
– Lack of explainability: AI-based lending models must be transparent to support regulatory compliance and fair lending requirements
– Data quality issues: Poor or biased data can negatively affect model performance
– Operational complexity: Integrating AI into legacy systems can be difficult
– Governance and oversight concerns: Models require ongoing monitoring and validation
– Traditional credit bureau data
– Alternative credit and non-traditional data sources
– Transactional and behavioral data
– Internal lender performance data
– Macroeconomic and market indicators
