Experian, a prominent credit reporting bureau, has undergone a significant transformation, evolving into a software powerhouse. Having accumulated vast amounts of data on over a billion individuals and 25 million U.S. businesses, Experian recognized the potential of big-data analytics in the financial sector. As a result, the company shifted its strategy in 2019 to integrate software and data analysis into its operations. This strategic pivot has proven to be highly lucrative, contributing 35% of Experian’s $7 billion annual revenue. This evolution marks a new chapter for Experian as it leverages advanced technologies to enhance credit analytics and decision-making processes across various industries.
The Birth of the Ascend Platform
In 2023, Experian introduced its Ascend Platform, a comprehensive suite powered by artificial intelligence, designed to meet the diverse needs of multiple sectors including financial services, healthcare, automotive, and digital marketing. This platform integrates data, analytics, software development tools, fraud prevention, and loan analysis to commercialize the extensive analytical expertise that Experian has developed over the years. The Ascend Platform automates the creation of risk models, a process that was once labor-intensive and prone to errors. By streamlining this critical function, the platform enables financial institutions to enhance efficiency and accuracy in their operations.
The Ascend Platform addresses two fundamental needs for businesses dealing with high-value transactions: acquiring desirable customers and mitigating fraud risk. Before Ascend, building sophisticated financial models required significant manual effort and was susceptible to human error. The platform automates this process, incorporating comprehensive demographic data, behavioral traits, car-buying habits, credit histories, and lifestyle characteristics. This holistic approach allows organizations to make well-informed decisions regarding loans and other financial products, ultimately enhancing their ability to effectively serve their customers and mitigate potential risks.
Data Management Simplified
One of the standout features of the Ascend Platform is its ability to streamline data management tasks. Ensuring data accuracy and freshness is crucial for generating reliable analytics, and Ascend excels in this area by cleansing data to maintain its integrity. Additionally, it provides a sandbox environment where data scientists can build and refine models using popular coding languages such as Python, R, or SAS. This innovative approach significantly reduces the manual labor traditionally associated with these tasks, allowing data scientists to focus on more complex analytical challenges.
A notable component of the Ascend Platform is the Experian Assistant, a generative AI tool introduced in October 2022. This tool acts as an expert consultant to data scientists, aiding in coding, enhancing model transparency, and accelerating the iteration process by providing optimal coding and deployment recommendations. Although it does not replace the role of a data scientist, the Experian Assistant significantly boosts productivity, with some clients reporting efficiency improvements of up to 60%. This advanced tool exemplifies how AI can augment human expertise to drive better outcomes in data management and analysis.
Tackling Model Drift
Model drift, a common issue in machine learning where a model’s performance degrades over time, is effectively addressed by the AI-powered assistant within the Ascend Platform. As real-world data diverges from the training data, model drift can compromise the accuracy and reliability of predictions. Detecting and rectifying this drift has traditionally been a challenging endeavor, but Experian has automated this process with its generative AI capabilities. The AI alerts users to model drift, identifies the root causes, and suggests necessary adjustments to restore the model’s original efficacy, ensuring that models remain relevant and effective over time.
The automation of model drift detection and rectification simplifies what was once a labor-intensive task, allowing organizations to maintain the high performance of their machine learning models. This capability is particularly valuable for financial institutions and other businesses that rely on accurate predictive models to make informed decisions. By addressing model drift efficiently, the Ascend Platform helps organizations maintain the integrity of their analytics and ensures that their models deliver consistent and reliable results, even as underlying data evolves over time.
Advanced Fraud Detection
The Ascend Platform also excels in the area of fraud detection, offering a fraud sandbox that compiles billions of global events and displays them on a heat map correlated with specific triggers, such as email offers. This innovative tool enables organizations to identify potentially fraudulent activities associated with certain email patterns, helping them avoid fraudulent transactions. By recognizing patterns indicative of fraud, businesses can proactively mitigate risks and protect their financial assets. Additionally, the platform’s Challenger modeling technique allows users to test alternative machine learning models against the current production model, significantly reducing the time required to identify fraudulent patterns from weeks down to just a few days.
Experian’s fraud detection capabilities exemplify the platform’s robust approach to safeguarding businesses against fraud. By leveraging advanced analytics and machine learning, the Ascend Platform provides organizations with the tools they need to detect and prevent fraudulent activities effectively. This comprehensive approach not only enhances security but also allows businesses to respond swiftly to emerging threats, thereby minimizing potential losses and ensuring the integrity of their operations.
Reducing Bias with Reject Inferencing
Another innovative feature of the Ascend Platform is its focus on reducing bias in credit-scoring models through a technique called “reject inferencing.” Traditional models typically analyze only accepted applicants, which can introduce biases into the scoring process. Reject inferencing, on the other hand, incorporates data from previously rejected applicants and those who rejected a loan offer in favor of another, slightly better offer. This approach helps mitigate biases by providing a more comprehensive dataset for analysis, thereby allowing financial institutions to refine their loan terms and fraud guidelines more effectively.
By incorporating this diverse data, financial institutions can make more informed decisions about creditworthiness and enhance their offerings to better serve a broader range of customers. Reject inferencing ensures that credit-scoring models are fairer and more inclusive, ultimately leading to more accurate assessments and improved financial outcomes for both institutions and their customers. This feature underscores Experian’s commitment to leveraging advanced technologies to address complex challenges in credit analytics.
Enhancing Compliance
Regulatory compliance is a critical aspect of the financial sector, and the Ascend Platform streamlines this process through its generative AI capabilities. Compliance with rigorous guidelines often delays the deployment of predictive models, but Experian’s platform addresses this challenge by producing the necessary documentation automatically. The generative AI, trained on the SR 11-7 Guidance on Model Risk Management required by U.S. regulators, ensures that compliance documentation is thorough and accurate, significantly reducing the time and effort required to meet regulatory standards.
By automating the compliance process, the Ascend Platform allows organizations to deploy their models more quickly and with greater confidence, knowing that they meet all regulatory requirements. This efficiency gain is particularly valuable in an industry where compliance is paramount, as it enables businesses to stay ahead of regulatory changes and maintain adherence to standards. Experian’s commitment to enhancing compliance through advanced technology reinforces its role as a leader in credit analytics and underscores the importance of regulatory adherence in the financial sector.
Transforming Credit Analytics
Experian, a leading credit reporting agency, has undergone a significant transformation, evolving into a software powerhouse. With extensive data on over a billion individuals and 25 million U.S. businesses, Experian recognized the potential of big-data analytics in the financial sector. Consequently, the company shifted its strategy in 2019 to integrate software and data analysis into its core operations. This strategic shift has been remarkably successful, with it now accounting for 35% of Experian’s impressive $7 billion annual revenue. This evolution signifies a new chapter for Experian, as it utilizes advanced technologies to improve credit analytics and decision-making processes across a variety of industries. By leveraging the power of big data and software, Experian has positioned itself as not only a key player in credit reporting but also a leader in financial analytics and technology solutions. This move reflects broader trends in the financial sector, where data-driven decision-making is becoming increasingly vital.
