Tag: experian assistant

Generative AI (GenAI) is transforming the financial services industry by boosting operational efficiency, cutting costs, and enhancing customer experience. Today, industry leaders are leveraging GenAI technology to accelerate the modeling lifecycle, streamline workflows, and ensure regulatory compliance. However, financial institutions face several headwinds in their efforts to achieve strong business results. What industry challenges do financial institutions face? To drive profitability while fueling growth, organizations need to reduce costs, manage risks, and identify new revenue streams while complying with regulatory requirements. Growing customer bases are also a top priority for banking leaders in 2025, requiring personalized services and improved customer experiences to attract and retain customers.1 Staying one step ahead of the competition is another hurdle that many organizations need to overcome. A recent study states that 23% of U.S. consumers surveyed have opened a new bank account, and 28% have considered switching to a new bank in the past six months.2 Traditional financial institutions must continuously innovate to stay on pace with smaller, more agile fintech companies. Adopting technologies like GenAI is an effective way to stay relevant and top-of-mind with consumers. Why use GenAI technology in financial services? Financial organizations that use GenAI are achieving success by: Increasing productivity and efficiency Minimizing costs Strengthening customer relationships GenAI has revolutionized productivity, customer service, risk management, and financial data analysis within the financial services industry. Of all the various measurements of AI use, improved productivity was reported to be the leading indicator of successful implementation.3 Online tools like virtual assistants and chatbots provide personalized experiences to consumers and resolve issues in real time, leading to enhanced customer satisfaction. This AI technology reduces the workload on human agents and enables organizations to deliver value more quickly and with less friction. GenAI adoption at Experian Experian® is a leader in GenAI solutions, using advanced technology to manage and improve data. We champion responsible AI use, ensuring proper consumer data privacy, compliance, fraud prevention, and greater financial access and inclusion. Experian Assistant is our latest innovation in GenAI helping financial institutions to accelerate the modeling lifecycle, which enhances efficiency, reduces expenses, and promotes customer growth. Experian Assistant allows businesses to build and deploy models, monitor performance, and go to market more quickly and with less friction, which can translate to more business success. The tool provides instant expert recommendations and insights with comprehensive support, enabling users to make smarter and faster data-driven decisions. This technology offers multiple functions that are crucial for optimizing business efficiency: Natural language interface Deep insights into underlining data tables and metrics Reduced operational and cloud expenses Decreased risk of penalties Read our latest white paper to discover more about how our latest GenAI innovation, Experian Assistant, is empowering organizations to drive business growth and profitability. Read the white paper 1  BAI, 2025. Acquiring new customers and growing quality deposits are the top business challenges in 2025. 2 MX, 2023. What Influences Where Consumers Choose to Bank. 3 Forrester, Q2 AI Pulse Survey, 2024.

GenAI is pushing financial institutions to focus on improving efficiency, productivity, and time to value during the modeling lifecycle. Experian Assistant provides robust tools for data exploration, model building, deployment, and performance monitoring, allowing users to drive better decision-making.

Scott Brown presents at Reuters Next “If I were to look into a crystal ball, traditional lending methodology and processes will not be replaced; they will be augmented by consumers connecting to banks via APIs, contributing the data they are comfortable with, and banks using that in conjunction with historical credit data to offer newer and better products they didn’t have access to before. The convergence of data, tech and AI leads to more financial inclusion and a more modern way of underwriting.”Scott Brown, Group President Financial Services, Experian North America Scott Brown, Group President of Financial Services for Experian North America, recently presented at Reuters Next discussing the transformative power of AI and data analytics in financial services. The session also covered the top challenges that financial institutions face today and how advances in technology are helping organizations overcome those challenges. This keynote presentation was a timely follow-up to Brown’s previous appearance at the Money20/20 conference in Las Vegas, where he revealed the details of Experian’s latest innovation in GenAI technology, Experian Assistant. Brown, in a conversation with TV writer, producer and anchor Del Irani, spoke about the ethical considerations of AI innovation, what the future of underwriting may look like, and how open banking can drive financial inclusion and have a significant positive impact on both businesses and consumers. “If you are extending a line of credit to a given consumer, how do you do so in a way that’s integrated into their everyday lives? That’s where the concept of embedded finance comes in, and how to embed finance into a consumer’s life, and not the other way around.”Scott Brown, Group President Financial Services, Experian North America By embedding finance into consumers’ lives, and not the other way around, organizations can develop better strategies to balance risk and generate more revenue. He also focused on three foundational steps to take advantage of the capabilities AI offers: data quality, transparency, and responsibility. Areas of focus for implementing AI As organizations rely on more sophisticated approaches, data quality inputs are more important than ever. Inaccurate data can lead to poor business decisions that can have a negative impact on organizations’ bottom line. Transparency is also a crucial component of implementing AI solutions. Companies should be able to explain how their models work and why the end results make sense while avoiding biases. Leveraging data with AI tools allows organizations to get a better view of the consumer, which is a goal of most banks and lending institutions. Using that consumer data responsibly is important for financial institutions to establish and maintain trust with the people who use their services. While incorporating AI solutions into everyday business operations is important for financial institutions to better serve their consumers and remain competitive in the industry, a lack of access to AI tools can prevent some organizations from doing so. A fragmented approach leads to higher costs, lower efficiency, and greater risk Until recently, financial institutions have had to rely on several different technology providers and tools to optimize customer experience and operational efficiency while protecting consumers from the risk of identity theft and fraud. This fragmented approach can result in increased costs for organizations and higher risk for consumers. Now, AI technology is solving this issue by integrating functionality into a single platform, such as the Experian Ascend Technology Platform™. This streamlined access to a comprehensive suite of tools can help accelerate time-to-value while also eliminating compliance risks. Full interview available now Brown’s full interview at Reuters Next reveals more details about how Experian is empowering organizations to better serve their consumers’ financial needs through AI innovation while also helping more than 100 million Americans who don’t have access to the mainstream credit ecosystem due to being credit invisible, unscoreable, or have a low credit score. Watch the full interview to learn more about how Experian is continuing to bring financial power to all through innovative technology. Watch the full interview now

Generative AI (GenAI) is transforming the financial services industry, driving innovation, efficiency and cost savings across various domains. By integrating GenAI into their operations, financial institutions can better respond to rapidly changing environments. GenAI is reshaping financial services from customer engagement to compliance, leading to streamlined operations and enhanced decision-making. The strategic role of GenAI in financial services Adopting GenAI in financial services is now a strategic imperative. A 2024 McKinsey report (The State of AI in 2024) notes more than a 10% revenue increase for companies using GenAI. As institutions strive to stay competitive, GenAI provides powerful tools to enhance customer experiences, optimize operations, accelerate regulatory compliance, and expedite coding and software development. Key areas where GenAI is making an impact Enhanced customer engagement Financial institutions use GenAI to offer personalized products and services. By analyzing real-time customer data, GenAI enables tailored recommendations, boosting satisfaction and retention. Streamlining and optimizing operations GenAI automates tasks like data entry and transaction monitoring, freeing up resources for strategic activities. This accelerates workflows and reduces errors. Further, GenAI-driven efficiency directly cuts costs. By automating processes and optimizing resources, institutions can lower overhead and invest more in innovation. Deloitte’s Q2 2024 study found AI automation reduced processing times by up to 60% and operational costs by 25%. Accelerating regulatory compliance GenAI simplifies compliance by automating data collection, analysis and reporting. This ensures regulatory adherence while minimizing risks and penalties. According to a 2024 Thomson Reuters survey, AI-driven compliance reduced reporting times by 40% and costs by 15%. Developer coding support for efficiencies GenAI is an invaluable tool for programmers. It aids in code generation, task automation and debugging, boosting development speed and allowing focus on innovation. Gartner’s 2024 research highlights a 30% improvement in coding efficiency and a 25% reduction in development timeframes due to GenAI. Accelerating credit analytics with Experian Assistant Within the credit risk management space, GenAI offers a powerful solution that addresses some known pain points. These relate to mining vast amounts of data for insight generation and coding support for attribute selection and creation, model development, and expedited deployment. Experian Assistant is a game-changer in modernizing analytics workflows across the data science lifecycle. Integrated into the Experian Ascend™ platform, it’s specifically designed for analytics and data science teams to tackle the challenges of data analysis, model deployment and operational efficiency head-on. Capabilities and skills of Experian Assistant Data tutor: Offers comprehensive insights into Experian’s data assets, enabling users to make informed decisions and optimize workflows Analytics expert: Provides tailored recommendations for various use cases, helping users identify the most predictive metrics and enhance model accuracy Code advisor (data prep): Automatically generates code for tasks like data merging and sampling, streamlining the data preparation process Code advisor (analysis): Generates code for risk analytics and modeling tasks, including scorecard development and regulatory analyses Tech specialist: Facilitates model deployment and documentation, minimizing delays and ensuring a seamless transition from development to production Driving more-informed decisions Adopting GenAI will be key to maintaining competitiveness as the financial services industry evolves. With projections showing significant growth in GenAI investments by 2025, the potential for enhanced efficiencies, streamlined operations and cost savings is immense. Experian Assistant is at the forefront of this transformation, addressing the bottlenecks that slow down analytical processes and enabling financial institutions to move faster, more informed and with greater precision. By integrating the capabilities of the Experian Assistant, financial institutions can leverage GenAI in credit risk management, automate data processes, and develop customized analytics for business decision-making. This alignment with GenAI’s broader benefits—like operational streamlining and improved customer experience—ensures better risk identification, workflow optimization, and more informed decisions. To learn more about how Experian Assistant can transform your data analytics capabilities, watch our recent tech showcase and book a demo with your local Experian sales team. Watch tech showcase Learn more

 Technology has dramatically transformed the financial services landscape, fostering innovation and enhancing operational efficiency. In an interview at this year’s Money20/20 conference, Scott Brown, Group President of Financial and Marketing Services for Experian, sat down with Fintech Futures’ North America Correspondent Heather Sugg to share how Experian is leveraging data, analytics, and artificial intelligence (AI) to modernize the financial services industry. During the discussion, Scott highlighted the recent launch of Experian Assistant — our newest generative AI tool designed to accelerate the modeling lifecycle, resulting in greater productivity, improved data visibility, and reduced delays and expenses. While Experian Assistant is a business-to-business solution built alongside our clients, Scott also noted its broader impact — helping increase credit access for underserved consumers. “At Experian, we’re really focused on addressing the underserved community who doesn’t have access to credit,” said Scott. “And we think that this tool helps lenders reach those customers in an easier way.”  Learn more about Experian Assistant and watch our tech showcase to see the solution in action. Learn more Watch tech showcase

For businesses across all sectors solutions that improve productivity are more important than ever. As technology advances, organizations across industries are looking to capitalize by investing in artificial intelligence (AI) solutions. Studies have recently shown that productivity is a leading measure of how well these AI tools are performing. About 60% of organizations surveyed are using “improved productivity” as a metric to measure the success of implementing AI solutions.[1] Experian research shows it takes an average of 15 months to build a model and put it into production. This can hinder productivity and the ability to quickly go to market. Without a deep understanding of key data points, organizations may also have difficulty realizing time to value efficiently. To improve upon the modeling lifecycle, businesses must examine the challenges involved in the process. The challenges of model building One of the most significant challenges of the modeling lifecycle is speed. Slow modeling processes can cause delays and missed opportunities for businesses which they may have otherwise capitalized on. Another difficulty organizations face is having limited access to high-quality data to build more efficient models. Without the right data, businesses can miss out on actionable insights that could give them a competitive edge. In addition, when organizations have inefficient resources, expenses can skyrocket due to the need for experts to intervene and address ongoing issues. This can result in a steep learning curve as new tools and platforms are adopted, making it difficult for organizations to operate efficiently without outside help. Businesses can combat these challenges by implementing tools such as artificial intelligence (AI) to drive efficiency and productivity. The AI journey While generative AI and large language models are becoming more prevalent in everyday life, the path to incorporating a fully functional AI tool into an organization’s business operations involves multiple steps. Beginning with a proof of concept, many organizations start their AI journey with building ideas and use cases, experimentation, and identifying and mitigating potential pitfalls, such as inaccurate or irrelevant information. Once a proof of concept reaches an acceptable state of validity, organizations can move on to production and value at scale. During this phase, organizations will select specific use cases to move into production and measure their performance. Analyzing the results can help businesses glean valuable information about which techniques work most effectively, so they can apply those techniques to new use cases. Following successful iterations of an efficiently functioning AI, the organization can then implement that AI as a part of their business by working the technology into everyday operations. This can help organizations drive productivity at scale across various business processes. Experian’s AI journey has been ongoing, with years of expertise in implementing AI into various products and services. With a goal of providing greater insights to both businesses and consumers while adhering to proper consumer data privacy and compliance, Experian is committed to responsibly using AI to combat fraud and foster greater financial access and inclusion. Our most recent AI innovation, Experian Assistant, is redefining how financial organizations improve productivity with data-driven insights. Introducing Experian Assistant Experian Assistant, a new GenAI tool announced in October at Money20/20 in Las Vegas, is helping organizations take their productivity to the next level by drastically speeding up the modeling lifecycle. To drive automation and greater intelligence for Experian partners, Experian Assistant enables users to interact with a virtual assistant in real time and offers customized guidance and code generation for our suite of software solutions. Our experts – Senior Director of Product Management Ankit Sinha and Director of Analyst Relations Erin Haselkorn – recently revealed the details of how Experian Assistant can cut down model-development timelines from months to days, and in some cases even hours. The webinar, which took place on November 7th, covered a wide range of features and benefits of the new tool, including:  Spending less time writing code  Enhancing understanding of data and attributes Accelerating time to value Improving regulatory compliance A case study in building models faster Continental Finance Company, LLC’s Chief Data Scientist shared their experience using Experian Assistant and how it has improved their organization’s modeling capabilities: “With Experian Assistant, there is a lot of efficiency and improvement in productivity. We have reduced the time spent on data building by almost 75%, so we can build a model much quicker, and the code being generated by Experian Assistant is very high quality, enabling us to move forward much faster.” For businesses looking to accelerate their modeling lifecycle and move more quickly with less effort, Experian Assistant provides a unique opportunity to significantly improve productivity and efficiency. Experian Assistant tech showcase Did you miss the Experian Assistant Tech Showcase webinar? Watch it on demand here and visit our website to learn more. Visit our website [1] Forrester’s Q2 AI Pulse Survey, 2024