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Download eBook How to deploy a multi-layered approach with a holistic view of the consumer to stay ahead of evolving fraud. Find out how to mitigate against GenAI-enhanced fraud by downloading the eBook GenAI's rise to the top has been rapid. It was only last year that GenAI fully emerged in the public domain as an accessible tool, with the technology's impact and expectations reverberating across businesses worldwide. This massive growth trajectory has led some critics to suggest that GenAI is nearing its hype peak. However, its potential is still unfolding as the technology continues to evolve and be applied to new use cases. Although its positive applications have enormous potential, the technology also poses many risks. In the fraud space, GenAI poses two main threats: The scaling and personalisation of attacks. Criminals today are generating synthetic content with a goal of decieving businesses and individuals. Fraudsters leverage GenAI to produce convincing synthetic identities and deepfakes that include audio, images, and videos that are increasingly sophisticated and practically impossible to differentiate from genuine content without the help of technology. Fraudsters also exploit the power of Large Language Models (LLMs) by creating eloquent chatbots and elaborate phishing emails to help them steal vital information or establish communication with their targets. Mitigation comes in many forms, depending on the business, but the fundamental differentiator in the fight against evolving and increasing fraud attempts is the ability to have a holistic view of the consumer. Businesses today deploy multiple solutions from various vendors to ensure fraud mitigation covers all touchpoints. Although full coverage may exist, businesses often don’t have a holistic offline and digital view of the consumer, meaning losses can accumulate before patterns emerge within these siloed views. Rapidly evolving, highly automated, and large-scale attacks demand an up-to-date cross-industry view of online and offline identity behavior, linkages, and interactions. The flexible solution must similarly leverage GenAI to spot and validate fraud signals, interpret intelligence from fraud analysts, and quickly operationalize new attributes and models to keep pace with attackers. This is where layered fraud and identity controls in real time and a comprehensive offline analytics platform work together Download the eBook to discover: The rise of GenAI GenAI impact by fraud type Deepfakes: The authenticity challenge The challenge of detecting synthetic identoties Scaling up: The emergence of bot-as-a-service Authorised Push Payment Fraud (APP Fraud) Understanding the role of intent and context in fraud prevention A holistic view of the consumer with Ascend Fraud Sandbox Key takeaways: Find out how to mitigate against GenAI-enhanced fraud Businesses that implement these recommendations will be best equipped to manage fraud spikes from GenAI while simultaneously protecting good customer experiences from being negatively impacted by unnecessary friction. Ascend Fraud Sandbox helps businesses to shine a light on the holistic view of consumer activity across the industry, moving far beyond the typical point-in-time, product-specific view of consumers.Mike Gross, Vice President, appled fraud research and analytics, experian Download eBook

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

We explore four fraud trends likely to be influenced the most by GEN AI technology in 2024, and what businesses can do to prevent them. 2023: The rise of Generative AI 2023 was marked by the rise of Generative Artificial Intelligence (GEN AI), with the technology’s impact (and potential impact) reverberating across businesses around the world. 2023 also witnessed the democratisation of GEN AI, with its usage made publicly available through multiple apps and tools such as Open AI's Chat GPT and DALL·E, Google's Bard, Midjourney, and many others. Chat GPT even held the world record for the fastest growing application in history (until it was surpassed by Threads) after reaching 100 million users in January 2023, just less than 2 months after its launch. The profound impact of GEN AI on everyday life is also reflected in the 2023 Word of the Year (WOTY) lists published by some of the biggest dictionaries in the world. Merriam-Webster’s WOTY for 2023 was 'authentic'— a term that people are thinking about, writing about, aspiring to, and judging more than ever. It's also not a surprise that one of the other words outlined by the dictionary was 'deepfake', referencing the importance of GEN AI-inspired technology over the past 12 months. Among other dictionaries that publish WOTY lists, both Cambridge Dictionary and Dictionary.com chose 'hallucinate' - with new definitions of the verb describing false information produced by AI tools being presented as truth or fact. A finalist in the Oxford list was the word 'prompt', referencing the instructions that are given to AI algorithms to influence the content it generates. Finally, Collins English Dictionary announced 'AI' as their WOTY to illustrate the significance of the technology throughout 2023. GEN AI has many potential positive applications from streamlining business processes, providing creative support for various industries such as architecture, design, or entertainment, to significantly impacting healthcare or education. However, as signalled out by some of the WOTY lists, it also poses many risks. One of the biggest threats is its adoption by criminals to generate synthetic content that has the potential to deceive businesses and individuals. Unfortunately, easy-to-use, and widely available GEN AI tools have also created a low entrance point for those willing to commit illegal activities. Threat actors leverage GEN AI to produce convincing deepfakes that include audio, images, and videos that are increasingly sophisticated and practically impossible to differentiate from genuine content without the help of technology. They are also exploiting the power of Large Language Models (LLMs) by creating eloquent chatbots and elaborate phishing emails to help them steal important information or establish initial communication with their targets. GEN AI fraud trends to watch out for in 2024 As the lines between authentic and synthetic blur more than ever before, here are four fraud trends likely to be influenced most by GEN AI technology in 2024. A staggering rise in bogus accounts: (impacted by: deepfakes, synthetic PII)Account opening channels will continue to be impacted heavily by the adoption of GEN AI. As criminals try to establish presence in social media and across business channels (e.g., LinkedIn) in an effort to build trust and credibility to carry out further fraudulent attempts, this threat will expand way beyond the financial services industry. GEN AI technology continues to evolve, and with the imminent emergence of highly convincing real-time audio and video deepfakes, it will give fraudsters even better tools to attempt to bypass document verification systems, biometric and liveness checks. Additionally, they could scale their registration attempts by generating synthetic PII data such as names, addresses, emails, or national identification numbers. Persistent account takeover attempts carried out through a variety of channels: (impacted by: deepfakes, GEN AI generated phishing emails)The advancements in deepfakes present a big challenge to institutions with inferior authentication defenses. Just like with the account opening channel, fraudsters will take advantage of new developments in deepfake technology to try to spoof authentication systems with voice, images, or video deepfakes, depending on the required input form to gain access to an account. Furthermore, criminals could also try to fool customer support teams to help them regain access they claim to have lost. Finally, it's likely that the biggest threat would be impersonation attempts (e.g., criminals pretending to be representatives of financial institutions or law enforcement) carried out against individuals to try to steal access details directly from them. This could also involve the use of sophisticated GEN AI generated emails that look like they are coming from authentic sources. An influx of increasingly sophisticated Authorised Push Payment fraud attempts: (impacted by: deepfakes, GEN AI chatbots, GEN AI generated phishing emails)Committing social engineering scams has never been easier. Recent advancements in GEN AI have given threat actors a handful of new ways to deceive their victims. They can now leverage deepfake voices, images, and videos to be used in crimes such as romance scams, impersonation scams, investment scams, CEO fraud, or pig butchering scams. Unfortunately, deepfake technology can be applied to multiple situations where a form of genuine human interaction might be needed to support the authenticity of the criminals' claims. Fraudsters can also bolster their cons with GEN AI enabled chatbots to engage potential victims and gain their trust. If that isn’t enough, phishing messages have been elevated to new heights with the help of LLM tools that have helped with translations, grammar, and punctuation, making these emails look more elaborate and trustworthy than ever before. A whole new world of GEN AI Synthetic Identity: (impacted by: deepfakes, synthetic PII)This is perhaps the biggest fraud threat that could impact financial institutions for years to come. GEN AI has made the creation of synthetic identities easier and more convincing than ever before. GEN AI tools give fraudsters the ability to generate fake PII data at scale with just a few prompts. Furthermore, criminals can leverage fabricated deepfake images of people that never existed to create synthetic identities from entirely bogus content. Unfortunately, since synthetic identities take time to be discovered and are often wrongly classified as defaults, the effect of GEN AI on this type of fraud will be felt for a long time. How to prevent GEN AI related fraud As GEN AI technology continues to evolve in 2024, its adoption by fraud perpetrators to carry out illegal activities will too. Institutions should be aware of the dangers they possess and equip themselves with the right tools and processes to tackle these risks. Here are a few suggestions on how this can be achieved: Fight GEN AI with GEN AI: One of the biggest advantages of GEN AI is that while it is being trained to create synthetic data, it can also be trained to spot it successfully. One such approach is supported by Generative Adversarial Networks (GANs) that employ two neural networks competing against each other — a generator and a discriminator. The generator creates synthetic data, while the discriminator evaluates the generated data and tries to distinguish between real and fake samples. Over time, both networks fine tune themselves, and the discriminator becomes increasingly successful in recognising synthetic content. Other algorithms used to create deepfakes, such as Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and Autoencoders, can also be trained to spot anomalies in audio, images, and video, such as inconsistencies in facial movements or features, inconsistencies in lighting or background, unnatural movements or flickering, and audio discrepancies. Finally, a hybrid approach that combines multiple algorithms often presents more robust results. Advanced analytics to monitor the whole customer journey and beyond: Institutions should deploy a fraud solution that leverages data from a variety of tools that can spot irregular activity across the whole customer journey. That could be a risky activity, such as a spike in suspicious registrations or authentication attempts, unusual consumer behaviour, irregular login locations, suspicious device or browser data, or abnormal transaction activity. A best-in-class solution would give institutions the ability to monitor and analyse trends that go beyond a single transaction or account. Ideally, that means monitoring for fraud signals happening both within a financial institution’s environment and across the industry. This should allow businesses to discover signals pointing out fraudulent activity previously not seen within their systems or data points that would otherwise be considered safe, thus allowing them to develop new fraud prevention models and more comprehensive strategies. Fraud data sharing: Sharing of fraud data across multiple organisations can help identify and spot new fraud trends from occurring within an instruction's premises and stop risky transactions early. Educate consumers: While institutions can deploy multiple tools to monitor GEN AI related fraud, regular consumers don't have the same advantage and are particularly susceptible to impersonation attempts, among other deepfake or GEN AI related cons. While they can't be equipped with the right tools to recognize synthetic content, educating consumers on how to react in certain situations related to giving out valuable personal or financial information is an important step in helping them to remain con free. Learn more with our latest fraud reports from across the globe: UK Fraud Report 2023 US Fraud Report 2023 EMEA + APAC Fraud Report 2023

What are lenders prioritising when it comes to Gen AI? We take a look at five transformative use cases in lending, and organisational priorities for integrating Gen AI into customer lifecycle processes. Although Generative Artificial Intelligence (Gen AI) only launched publicly in the form of Chat GPT last November, adoption has been widespread and rapid. Even in typically risk-adverse industries like financial services, our research shows that there is widespread recognition that Gen AI could deliver a range of benefits across business functions. We identified five areas of focus for lenders based on our research. 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. The qualitative research showed that lenders are already using a type of Gen AI, Large Language Models (LLMs), in their operations, with a focus on testing across areas such as customer service and internal processes before deploying to credit operations. We look at the potential use cases, and how businesses are using Gen AI now. 1. Personalised customer experience Customers today expect a personalised lending experience that is tailored to their unique needs and preferences. GenAI can leverage customer data to generate personalised loan offers, recommendations, and repayment plans. This helps lenders improve customer satisfaction and loyalty, leading to increased customer retention and revenue growth. This is an area that is front of mind for the companies in our research – nearly half of businesses surveyed are planning to implement or expand technology capabilities to either upsell or retain customers in the next 12 months. Furthermore, 50% of companies believe that offering more tailored underwriting and pricing is a top priority in their credit operations, followed by 44% who also aim to increase personalisation in marketing, products, and services to their customers. According to the research, some organisations have formed alliances with technology providers like OpenAI and Microsoft to investigate and further explore the use of LLMs. These partnerships involve analysing customer data to identify opportunities for cross-selling. 2. Enhancing models with new data sources With new data sources emerging all the time, Gen AI is one of the technologies that will most likely accelerate the opportunity for businesses to incorporate them into models. Lenders could include sources such as social network data into their models by using LLMs. This unstructured data, including customer emotions and behaviours on social networks, would be treated as an additional variable in the models. According to the research social media data and psychometric data is already used across financial services, to varying degrees. It showed that 35% of retail companies use social media data, while 29% of FinTechs use psychometric data. Auto finance companies sit at lower end of the adoption scale, with only 12% using social media data and 15% psychometric data. 3. Operational efficiencies Gen AI can help bring operational efficiencies to customerjourneys across the entire lifecycle, offering lenders theability to automate and streamline various processes,resulting in improved productivity, cost savings, andenhanced customer experiences. One of the top challenges for businesses surveyed isimproving customer journeys during onboarding, and thiswas particularly significant for credit unions / buildingsocieties (53%). 4. Detecting and preventing fraud Gen AI can play a crucial role in fraud detection by analysing patterns and anomalies in vast datasets. By leveraging machine learning techniques, Gen AI models can proactively identify potentially fraudulent activities and mitigate risks. The ability to detect fraud in real-time improves the overall security of lending operations and helps protect lenders and borrowers from financial losses. Detecting and preventing fraud is a constant challenge for lenders. 51% of retailers and 47% of credit unions/ building societies surveyed said that reducing fraud losses is a key challenge for them. 5. Customer service Driven by advances in the machine learning and AI space, the world of customer service has benefited hugely from the adoption of virtual assistants and chatbots in recent years. This looks to continue, with businesses saying that LLMs are being tested for customer service purposes, allowing lenders to identify customer issues and automate actions. What's next for lenders? The research found that lenders are utilising various machine learning techniques like regression, decision trees, neural networks, and random forest, along with LLMs. Businesses are in the early stages of exploring how they can use LLMs in credit risk models, but it will undoubtedly involve a blend of existing and new capabilities. As with any emerging technology, it’s important to look at potential risk. The research indicated that organisations see challenges and concerns when it comes to the use of LLMs in their models. It is crucial to ensure the models are trusted, validated, and properly understood to avoid reliance on outsourced solutions and maintain control and visibility over the models’ functions. The ability to explain decisions in Gen AI to avoid bias can be difficult, and businesses will be watching the regulators to understand how best to proceed. There is no doubt, however, that Gen AI will optimise the credit customer lifecycle, creating vast opportunities for lenders. Download PDF More on Gen AI

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. More on Gen AI

The survey underpinning these insights encompasses 1,849 business respondents and 6,062 consumers from 20 countries, including Australia, Brazil, China, Chile, Colombia, Denmark, Germany, India, Indonesia, Ireland, Italy, Malaysia, The Netherlands, Norway, Peru, Singapore, South Africa, Spain, UK, and US. We’ve also included interviews with consumers from Brazil, Germany, the UK, and US.

The evolving expectations and experience of the new digital consumer The expectations of consumers are changing rapidly. People of all ages and incomes are online, seeking the right products and services to manage their financial lives digitally in a secure, speedy, and frictionless environment. A look inside: Our latest research reveals the rise of a new digitally savvy consumer—one who is increasingly aware of new payment methods, advanced recognition tools, and the use of AI, and has higher expectations of their digital experience. Read the report to find out what businesses can do to harness the digital opportunity: 1. Leveraging the AI advantage 2. Incorporating embedded finance 3. Introducing new, more secure technologies 4. Educating consumers about how you use their data 5. Exploring solutions that aggregate emerging technologies Online spending is continuing its upward trend, with 53% of consumers surveyed saying they have increased online spending and transactions in the past three months, and 50% predicting that their spending will increase in the next three months. Enabling this shift is the extent to which businesses can provide a quality digital experience. 81% of consumers said that a positive online experience, which includes interactions with multiple digital touchpoints makes them think more highly of a brand. Consumers simply do not tolerate poor-quality online experiences and will take their loyalty to businesses that can meet their expectations. Speed and security are a driving force for consumers in the payments space, which is reflected in rapid rise in mobile wallet payments. Rivalling traditional payment methods, 62% of consumers say they’ve used a mobile wallet in the last six months. Consumers are embracing these new habits across the board, with 18% saying they have used BNPL in the past six months, and 71% seeing it as secure. With the rise and increasing awareness of new payment methods like BNPL, consumers who have lacked access to traditional banking, lending and credit cards now have additional financial options, giving businesses the opportunity to prioritise financial inclusion. The rise of new and increased online activity has resulted in increased concerns about online security, with 42% of consumers more concerned than they were 12 months ago. With this awareness comes opportunity for businesses to leverage new recognition approaches. Biometrics seems to resonate with consumers, with 81% reporting that they feel most secure when encountering physical biometrics. Trust and security are becoming interdependent, with consumers expecting strong security measures from businesses. 73% of consumers say that the onus is on businesses to protect them online, and 45% identify the belief that businesses have strong security measures in place as the top reason to trust an online transaction. As consumers become ever-more educated and aware of the digital world, they want businesses to communicate with them about why they are using personal data. 63% of consumers are willing to share their data and see it as beneficial to them if they see security and convenience in return. We surveyed 6,000 consumers and 2,000 businesses from 20 countries worldwide as part of our ongoing efforts to learn more about how, why, and where consumers interact with businesses online. Read the full report Stay in the know with our latest research and insights:

Did you miss these March business headlines? We’ve compiled the top global news stories that you need to stay in-the-know on the latest hot topics and insights from our experts. Experian partners with Black Opal to bring credit options to US immigrants PYMNTS.com covers the partnership between Experian and Black Opal to boost consumer credit access to immigrants in the US. Using Crosscore and PowerCurve, Block Opal will be able to make real-time credit decisions while also managing using the platform’s tools to better manage identity verification and fraud prevention. Fraud shifting as online activity increases In this CUNA article, Brock Fritz explores Experian's Future of Fraud Index for 2022, with Experian's Chief Innovation Officer, Kathleen Peters, offering up solutions for businesses looking to mitigate the effects of more online fraud. How AI is modernizing online transactions Donna DePasquale, EVP of Global Decisioning Software, writes in Dataversity about the importance of automation and insights as objectives driving modernization through AI for businesses, and what they should focus on in order to increase customer acquisition. Online payment fraud Online payment fraud will reach 206 Billion by 2025. David Britton, Experian VP Industry Solutions Global Identity and Fraud is interviewed by David Cogan, host of the Heroes Show and founder of Eliances entrepreneur community. Stay in the know with our latest research and insights:

The ecosystem of credit lending platforms and technologies has rapidly grown in the past year. Lenders now find themselves in an increasingly competitive market with new players emerging on the scene. More companies now have access to advanced analytics and automation capabilities, and this is helping businesses improve the accuracy and inclusivity of consumer lending decisions – a giant step toward achieving their growth ambitions. Our recent research shows that one of the top priorities for businesses has been to invest in new artificial intelligence and machine learning models for smarter customer decisions. But how effective is building new AI models without considering the data? What is data-centric AI? Building AI models on fixed data has already become an outdated approach. But by coupling data with the best model, better outcomes can be achieved. The concept of data-centric AI was coined by leading thinker in the AI space, Andrew Ng. Ng believed that models in production are only as good as the point-in-time data used to build them. As businesses continue to receive new data, this data needs to feed back into the model if it’s going to continue delivering the best results. This continuous loop of enriching the model with new data can be applied across use cases. The value of data-centric AI models for acquiring new customers By using the latest available data, rather than from 6-12 months ago or longer when the model was originally developed, data-centric AI models can: • More rapidly account for changes in the economy and consumer finances • Reach under-represented populations and provide greater access to credit • Take advantage of newly available types of information from data providers The value of data-centric AI in existing frameworks More observations AI is often limited by the data that was used to create the model. By using a more fluid open-source alternative, different data sets can be inputted to get more observations based on different characteristics and findings. For example, if a business wants to acquire a new type of customer, traditional AI would require a new model with new data sets to be in order to target this new customer. With data-centric AI, businesses can use an existing model and simply expand the data, thus allowing the model to work far more efficiently and target a new consumer base. It is a shared view that businesses should not build models with just their own data, because those data sources are too limited. At the very least, businesses want to combine data with a peer sample. However, an even better way is to use hybrid data sets in order to get the most observations. Data-centric AI makes that process easy without the need to create different models to see different outcomes. Up-to-date data The world is in a state of flux—populations change, people change. This means that the data pools AI models draw on may be compromised, no longer relevant, or have new meaning over time. It’s important to keep AI data sets recent and up to date, and not assume that the models used two years ago still apply today. For AI models to operate efficiently they need current, relevant data. Having a data-centric approach and sweeping through collected observations is essential for any business relying on their AI solutions. Businesses must have processes to understand and test their data to be sure the values are still adding up to what they should be. Being disciplined about data hygiene, all the way back to the source, is a necessity. Enriched and expanded data With model-centric AI, businesses are limited by the data they start with. Data-centric AI makes it possible to expand on the current customer base, which already includes data on customer attributes, with new potential customers that might mimic characteristics of a business's current base. Expanded data can also play a role with financial inclusion and credit worthiness. Having a low credit score does not necessarily mean the consumer is a bad risk or that they shouldn't be allowed access to credit—sometimes, it could mean there is simply a lack of data. Expanding data to include varied sources and adding it to current models without changing their structure, enables businesses to provide credit for individuals who may not have originally been accepted. This new approach in AI is creating solutions that are far more inclusive than previously possible. Data has massively expanded and is constantly evolving. By using data combined with advanced analytics, such as AI, there will be more sophistication in the observations that come from the data. This will allow businesses to better decide what data they choose to rely on while ensuring accuracy. By using expanded data sources, the outcomes of models are changed, leading to more inclusive models better fit for decision making and improving performance. "Models in production are only as good as the point-in-time data used to build them." Andrew Ng Infographic: Why data-centric AI leads to more accurate and inclusive decisions Stay in the know with our latest research and insights:

Did you miss these January business headlines? We’ve compiled the top global news stories that you need to stay in-the-know on the latest hot topics and insights from our experts. Next-gen AI analytic apps in credit In this Lendit Fintech webinar about the future of AI analytics in credit, Srikanth Geedipalli, SVP of Global analytics and AI, joins a panel of experts to explain how Experian deals with delinquencies and retains customers using a proactive approach. A successful DevOps strategy is more than just technology Dr Mark D. Spiteri writes on the Forbes Technology Council about how Experian has embraced DevOps culture to not only improve internal IT processes, but also to reshape the mindset of product development teams. 7 payments trends for 2022 as innovation climbs David Bernard, SVP Global Decision Analytics, talks to Payments Dive about cross-border services, BNPL and cybersecurity tools, and how there will be no shortage of innovation and competition in the payments industry as businesses and their regulators shape new digital tools. Deepfakes – the good, the Bad, and the ugly In this Forbes article, Eric Haller, VP & General Manager, Identity, Fraud & DataLabs, talks about how the creation of deepfakes can be thought of as the latest development in the ongoing battle between business and counterfeiting. Stay in the know with our latest research and insights:

The ecosystem of credit lending platforms and technologies has rapidly grown in the past year. The top business priority emerging from the pandemic has been to prioritise investments in new artificial intelligence and machine learning models for smarter customer decisions. According to our latest report, business confidence in AI is growing: 81% up from 77% last year. Three reasons why data-centric AI models lead to more accurate and inclusive decisions More observations to better represent the population Easy o update with the most current data Enriched and expanded data sets for a complete view of the customer Stay in the know with our latest research and insights:

Did you miss these September business headlines? We’ve compiled the top global news stories that you need to stay in-the-know on the latest hot topics and insights from our experts. Lending in a Two-Lane Economy Harry Singh, Senior VP, Global Decisioning, features on this CU Management podcast, discussing ways in which Credit Unions can best serve their customers with loans and other products within what Experian's latest research refers to as the two-lane economy. The deepfake-scape: How to fight fraud in the digital age This Biometric Update article by David Britton, VP of Industry Solutions, looks at why deepfakes are a big risk to businesses and consumers, and how fighting fire with fire in the form of artificial intelligence and machine learning can be the best form of defence for organizations. Focus on Data, Advanced Analytics and Decisioning Creates a Winning Strategy for Experian Global Banking and Finance announce that Experian has been ranked number 11 in the IDC FinTech Rankings Top 100 which highlights the top 100 global providers of financial technology, with the piece referring to Experian as a “rising star.” The Rise Of Voice Cloning And DeepFakes In The Disinformation Wars Forbes's Jennifer Kite-Powell uncovers that although deepfake fraud is dominant in social media, it is quickly moving into business sectors. Kite-Powell talks to David Britton, VP of Industry Solutions, about what businesses can do to counteract deepfake fraud tactics like voice-cloning. Shri Santhanam talks AI in lending On this Fintech One to One podcast from Lendit FinTech News, Shri Santhanam, Global Head of Advanced Analytics and AI, talks about how lenders in the FinTech space should be using AI and machine learning, and what key trends he has encountered through the years, and what we might expect to see in the future. Stay in the know with our latest insights: