Tag: predictive analytics

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"Grandma, it’s me, Mike.” Imagine hearing the voice of a loved one (or what sounds like it) informing you they were arrested and in need of bail money. Panicked, a desperate family member may follow instructions to withdraw a large sum of money to provide to a courier. Suspicious, they even make a video call to which they see a blurry image on the other end, but the same voice. When the fight or flight feeling settles, reality hits. Sadly, this is not the scenario of an upcoming Netflix movie. This is fraud – an example of a new grandparent scam/family emergency scam happening at scale across the U.S. While generative AI is driving efficiencies, personalization and improvements in multiple areas, it’s also a technology being adopted by fraudsters. Generative AI can be used to create highly personalized and convincing messages that are tailored to a specific victim. By analyzing publicly available social media profiles and other personal information, scammers can use generative AI to create fake accounts, emails, or phone calls that mimic the voice and mannerisms of a grandchild or family member in distress. The use of this technology can make it particularly difficult to distinguish between real and fake communication, leading to increased vulnerability and susceptibility to fraud. Furthermore, generative AI can also be used to create deepfake videos or audio recordings that show the supposed family member in distress or reinforce the scammer's story. These deepfakes can be incredibly realistic, making it even harder for victims to identify fraudulent activity. What is Generative AI? Generative artificial intelligence (GenAI) describes algorithms that can be used to create new content, including audio, code, images, text, simulations, and videos. Generative AI has the potential to revolutionize many industries by creating new and innovative content, but it also presents a significant risk for financial institutions. Cyber attackers can use generative AI to produce sophisticated malware, phishing schemes, and other fraudulent activities that can cause data breaches, financial losses, and reputational damage. This poses a challenge for financial organizations, as human error remains one of the weakest links in cybersecurity. Fraudsters capitalizing on emotions such as fear, stress, desperation, or inattention can make it difficult to protect against malicious content generated by generative AI, which could be used as a tactic to defraud financial institutions. Four types of Generative AI used for Fraud: Fraud automation at scale Fraudulent activities often involve multiple steps which can be complex and time-consuming. However, GenAI may enable fraudsters to automate each of these steps, thereby establishing a comprehensive framework for fraudulent attacks. The modus operandi of GenAI involves the generation of scripts or code that facilitates the creation of programs capable of autonomously pilfering personal data and breaching accounts. Previously, the development of such codes and programs necessitated the expertise of seasoned programmers, with each stage of the process requiring separate and fragmented development. Nevertheless, with the advent of GenAI, any fraudster can now access an all-encompassing program without the need for specialized knowledge, amplifying the inherent danger it poses. It can be used to accelerate fraudsters techniques such as credential stuffing, card testing and brute force attacks. Text content generation In the past, one could often rely on spotting typos or errors as a means of detecting such fraudulent schemes. However, the emergence of GenAI has introduced a new challenge, as it generates impeccably written scripts that possess an uncanny authenticity, rendering the identification of deceit activities considerably more difficult. But now, GenAI can produce realistic text that sounds as if it were from a familiar person, organization, or business by simply feeding GenAI prompts or content to replicate. Furthermore, the utilization of innovative Language Learning Model (LLM) tools enables scammers to engage in text-based conversations with multiple victims, skillfully manipulating them into carrying out actions that ultimately serve the perpetrators' interests. Image and video manipulation In a matter of seconds, fraudsters, regardless of their level of expertise, are now capable of producing highly authentic videos or images powered by GenAI. This innovative technology leverages deep learning techniques, using vast amounts of collected datasets to train artificial intelligence models. Once these models are trained, they possess the ability to generate visuals that closely resemble the desired target. By seamlessly blending or superimposing these generated images onto specific frames, the original content can be replaced with manipulated visuals. Furthermore, the utilization of AI text-to-image generators, powered by artificial neural networks, allows fraudsters to input prompts in the form of words. These prompts are then processed by the system, resulting in the generation of corresponding images, further enhancing the deceptive capabilities at their disposal. Human voice generation The emergence of AI-generated voices that mimic real people has created new vulnerabilities in voice verification systems. Firms that rely heavily on these systems, such as investment firms, must take extra precautions to ensure the security of their clients' assets. Criminals can also use AI chatbots to build relationships with victims and exploit their emotions to convince them to invest money or share personal information. Pig butchering scams and romance scams are examples of these types of frauds where AI chatbots can be highly effective, as they are friendly, convincing, and can easily follow a script. In particular, synthetic identity fraud has become an increasingly common tactic among cybercriminals. By creating fake personas with plausible social profiles, hackers can avoid detection while conducting financial crimes. It is essential for organizations to remain vigilant and verify the identities of any new contacts or suppliers before engaging with them. Failure to do so could result in significant monetary loss and reputational damage. Leverage AI to fight bad actors In today's digital landscape, businesses face increased fraud risks from advanced chatbots and generative technology. To combat this, businesses must use the same weapons than criminals, and train AI-based tools to detect and prevent fraudulent activities. Fraud prediction: Generative AI can analyze historical data to predict future fraudulent activities. By analyzing patterns in data and identifying potential risk factors, generative AI can help fraud examiners anticipate and prevent fraudulent behavior. Machine learning algorithms can analyze patterns in data to identify suspicious behavior and flag it for further investigation. Fraud Investigation: In addition to preventing fraud, generative AI can assist fraud examiners in investigating suspicious activities by generating scenarios and identifying potential suspects. By analyzing email communications and social media activity, generative AI can uncover hidden connections between suspects and identify potential fraudsters. To confirm the authenticity of users, financial institutions should adopt sophisticated identity verification methods that include liveness detection algorithms and document-centric identity proofing, and predictive analytics models. These measures can help prevent bots from infiltrating their systems and spreading disinformation, while also protecting against scams and cyberattacks. In conclusion, financial institutions must stay vigilant and deploy new tools and technologies to protect against the evolving threat landscape. By adopting advanced identity verification solutions, organizations can safeguard themselves and their customers from potential risks. To learn more about how Experian can help you leverage fraud prevention solutions, visit us online or request a call

Published: August 24, 2023 by Alex Lvoff, Janine Movish

The science of turning historical data into actionable insights is far from magic. And while organizations have successfully used predictive analytics for years, we're in the midst of a transformation. New tools, vast amounts of data, enhanced computing power and decreasing implementation costs are making predictive analytics increasingly accessible. And business leaders from varying industries and functions can now use the outcomes to make strategic decisions and manage risk. What is predictive analytics? Predictive analytics is a type of data analytics that uses statistical modeling and machine learning techniques to make predictions based on historical data. Organizations can use predictive analytics to predict risks, needs and outcomes. You might use predictive analytics to make an immediate decision. For example, whether or not to approve a new credit application based on a credit score — the output from a predictive credit risk model. But organizations can also use predictive analytics to make long-term decisions, such as how much inventory to order or staff to hire based on expected demand. How can predictive business analytics help a business succeed? Businesses can use predictive analytics in different parts of their organizations to answer common and critical questions. These include forecasting market trends, inventory and staffing needs, sales and risk. With a wide range of potential applications, it’s no surprise that organizations across industries and functions are using predictive analytics to inform their decisions. Here are a few examples of how predictive analytics can be helpful: Financial services: Financial institutions can use predictive analytics to assess credit risk, detect fraudulent applicants or transactions, cross-sell customers and limit losses during recovery. Healthcare: Using data from health records and medical devices, predictive models can predict patient outcomes or identify patients who need critical care. Manufacturing: An organization can use models to predict when machines need to be turned off or repaired to improve their longevity and avoid accidents. Retail: Brick-and-mortar retailers might use predictive analytics when deciding where to expand, what to cross-sell loyalty program members and how to improve pricing. Hospitality: A large hospitality group might predict future reservations to help determine how much staff they need to hire or schedule. Advanced techniques in predictive modeling for financial services Emerging technologies, particularly AI and machine learning (ML), are revolutionizing predictive modeling in the financial sector by providing more accurate, faster and more nuanced insights. Taking a closer look at financial services, consider how an organization might use predictive credit analytics and credit risk scores across the customer lifecycle. Marketing: Segment consumers to run targeted marketing campaigns and send prescreened credit offers to the people who are most likely to respond. AI models can analyze customer data to offer personalized offers and product recommendations. Underwriting: AI technologies enable real-time data analysis, which is critical for underwriting. The outputs from credit risk models can help you to quickly approve, deny or send applications for manual review. Explainable machine learning models may be able to expand automation and outperform predictive models built with older techniques by 10 to 15 percent.1 Fraud detection models can also raise red flags based on suspicious information or behaviors. Account management: Manage portfolios and improve customer retention, experience and lifetime value. The outputs can help you determine when you should adjust credit lines and interest rates or extend offers to existing customers. AI can automate complex decision-making processes by learning from historical data, reducing the need for human intervention and minimizing human error. Collections: Optimize and automate collections based on models' predictions about consumers' propensity to pay and expected recovery amounts. ML models, which are capable of processing vast amounts of unstructured data, can uncover complex patterns that traditional models might miss. Although some businesses can use unsupervised or “black box" models, regulations may limit how financial institutions can use predictive analytics to make lending decisions. Fortunately, there are ways to use advanced analytics, including AI and ML, to improve performance with fully compliant and explainable credit risk models and scores. WHITE PAPER: Getting AI-driven decisioning right in financial services Developing predictive analytics models Going from historical data to actionable analytics insights can be a long journey. And if you're making major decisions based on a model's predictions, you need to be confident that there aren’t any missteps along the way. Internal and external data scientists can oversee the process of developing, testing and implementing predictive analytics models: Define your goal: Determine the predictions you want to make or problems you want to solve given the constraints you must act within. Collect data: Identify internal and external data sources that house information that could be potentially relevant to your goal. Prepare the data: Clean the data to prepare it for analysis by removing errors or outliers and determining if more data will be helpful. Develop and validate models: Create predictive models based on your data, desired outcomes and regulatory requirements. Deciding which tools and techniques to use during model development is part of the art that goes into the science of predictive analytics. You can then validate models to confirm that they accurately predict outcomes. Deploy the models: Once a model is validated, deploy it into a live environment to start making predictions. Depending on your IT environment, business leaders may be able to easily access the outputs using a dashboard, app or website. Monitor results: Test and monitor the model to ensure it's continually meeting performance expectations. You may need to regularly retrain or redevelop models using training data that better reflects current conditions. Depending on your goals and resources, you may want to start with off-the-shelf predictive models that can offer immediate insights. But if your resources and experience allow, custom models may offer more insights. CASE STUDY: Experian worked with one of the largest retail credit card issuers to develop a custom acquisition model. The client's goal was to quickly replace their outdated custom model while complying with their model governance requirements. By using proprietary attribute sets and a patented advanced model development process, Experian built a model that offered 10 percent performance improvements across segments. Predictive modeling techniques Data scientists can use different modeling techniques when building predictive models, including: Regression analysis: A traditional approach that identifies the most important relationships between two or more variables. Decision trees: Tree-like diagrams  show potential choices and their outcomes. Gradient-boosted trees: Builds on the output from individual decision trees to train more predictive trees by identifying and correcting errors. Random forest: Uses multiple decision trees that are built in parallel on slightly different subsets of the training data. Each tree will give an output, and the forest can analyze all of these outputs to determine the most likely result. Neural networks: Designed to mimic how the brain works to find underlying relationships between data points through repeated tests and pattern recognition. Support vector machines: A type of machine learning algorithm that can classify data into different groups and make predictions based on shared characteristics. Experienced data scientists may know which techniques will work well for specific business needs. However, developing and comparing several models using different techniques can help determine the best fit. Implementation challenges and solutions in predictive analytics Integrating predictive analytics into existing systems presents several challenges that range from technical hurdles to external scrutiny. Here are some common obstacles and practical solutions: Data integration and quality: Existing systems often comprise disparate data sources, including legacy systems that do not easily interact. Extracting high-quality data from these varied sources is a challenge due to inconsistent data formats and quality. Implementing robust data management practices, such as data warehousing and data governance frameworks, ensure data quality and consistency. The use  of APIs can facilitate seamless data integration. Scalability: Predictive business analytics models that perform well in a controlled test environment may not scale effectively across the entire organization. They can suffer from performance issues when deployed on a larger scale due to increased data volumes and transaction rates. Invest in scalable infrastructure, such as cloud-based platforms that can dynamically adjust resources based on demand. Regulatory compliance: Financial institutions are heavily regulated, and any analytics tool must comply with existing laws — such as the Fair Credit Reporting Act in the U.S. — which govern data privacy and model transparency. Including explainable AI capabilities helps to ensure transparency and compliance in your predictive models. Compliance protocols should be regularly reviewed to align with both internal audits and external regulations. Expertise: Predictive analytics requires specialized knowledge in data science, machine learning and analytics. Develop in-house expertise through training and development programs or consider partnerships with analytics firms to bridge the gap. By addressing these challenges with thoughtful strategies, organizations can effectively integrate predictive analytics into their systems to enhance decision-making and gain a competitive advantage. From prediction to prescription While prediction analytics focuses on predicting what may happen, prescription analytics focuses on what you should do next. When combined, you can use the results to optimize decisions throughout your organization. But it all starts with good data and prediction models. Learn more about Experian's predictive modeling solutions. 1Experian (2020). Machine Learning Decisions in Milliseconds *This article includes content created by an AI language model and is intended to provide general information.

Published: April 27, 2023 by Julie Lee

The automotive marketing world has benefited from cookie-based information to help identify and reach consumers in the market for a vehicle. Now that cookies will be going away, marketers need to find alternate ways to find in-market consumers. Let’s explore. What are cookies? Created to personalize internet browsing experiences, cookies are small pieces of code placed within a user’s browser whenever someone visits a specific website. Cookies typically track the website’s name and a generated unique user ID. They can capture page clicks, viewed web pages, clicks within a website, and Personally Identifiable Information (PII) such as an address, name, and device IDs. How are cookies used in marketing? Cookie data helps automotive marketers enhance the user experience by better understanding consumer behaviors to deliver targeted, relevant messaging that moves the consumer through the buying funnel. For example, think about when a consumer researches RVs/travel trailers to see if their current vehicle can handle towing a camper or when they search for the best way to hook up a camper. A dealer could use this cookie data to send advertisements for trucks with a towing package that could potentially tow a trailer. Cookie support is ending, so now what? In 2020, Google announced it would end support for cookies in the Chrome browser by early 2022. This timeline was established to allow Google to address the needs of users, publishers, and advertisers to respond and look for workarounds. The revised timeline puts Google’s cookie retirement in 2023. Marketers have been using cookie data in advertising for years, so what are the options when cookie data goes away? Cookie alternatives Automotive marketers can tackle a cookie-less world by using other sources of consumer data insights. For instance, a third-party data aggregator, like Experian, has access to numerous sources, platforms, and websites. Beyond that, we have access to a vast range of specific consumer data insights, including vehicle ownership, registrations, vehicle history data, and lending data. We take all that information and help marketers segment audiences and predict what consumers will do next. (That’s more than the average cookie!) Sample audience segment information: Consumers in market Loan status In positive equity Driving a specific year/make/model 1000+ lifestyle events such as new baby, marriage, new home Geography, demographics, psychographics To take it to the next level, we can use predictive analytics to go beyond what cookie data could provide by predicting who is ready to purchase a vehicle. In our example above, a marketer used cookie data to find buyers who had shown interest in a tow package, but that’s where it ended. By combining audience segmentation with a predictive model, marketers can target and identify consumers in-market and most likely ready to purchase a vehicle with a tow package. In this way, the data-driven insights from a third-party data provider specializing in automotive insights can replace the cookie-driven approach and take it a significant step beyond. Other ways to reach consumers in a cookie-less world Automotive marketers can also use data-driven insights to further explore specific channels where consumers spend their time. Social media, for example, is an effective channel to reach consumers. Marketers can go beyond standard Facebook audiences by utilizing Experian audience segmentation and predictive analytics to highly target consumers on Facebook. So, if you can predict when a consumer will be in-market, and you know in what channel they spend most of their time, you can target them with specific messages about your dealership and your vehicles. With cookies becoming a tool of the past, knowing who is likely to be in-market, what message resonates with them, and the best channel to use allows marketers to move beyond cookie-based strategies effectively. So, let the cookie crumble! For a deeper dive into cookies, watch this recorded webinar from the 2021 Digital Marketing Strategies conference: As the Cookies Crumble, How Will Automotive Digital Marketing Respond? Presented by Experian's Amy Hughes, Sr. Director of Dealer Intelligence. Learn more about Experian’s Automotive Intelligence Engine and how audience segmentation and predictive analytics can drive more in-market buyers to your dealership.

Published: March 10, 2022 by Kelly Lawson

In today’s uncertain economic environment, the question of how to reduce portfolio volatility while still meeting consumers’ needs is on every lender’s mind.  With more than 100 million consumers already restricted by traditional scoring methods used today, lenders need to look beyond traditional credit information to make more informed decisions. By leveraging alternative credit data, you can continue to support your borrowers and expand your lending universe. In our most recent podcast, Experian’s Shawn Rife, Director of Risk Scoring and Alpa Lally, Vice President of Data Business, discuss how to enhance your portfolio analysis after an economic downturn, respond to the changing lending marketplace and drive greater access to credit for financially distressed consumers. Topics discussed, include: Making strategic, data-driven decisions across the credit lifecycle Better managing and responding to portfolio risk Predicting consumer behavior in times of extreme uncertainty Listen in on the discussion to learn more. Experian · Effective Lending in the Age of COVID-19

Published: August 3, 2020 by Laura Burrows

In today’s ever-changing and hypercompetitive environment, the customer experience has taken center-stage – highlighting new expectations in the ways businesses interact with their customers. But studies show financial institutions are falling short. In fact, a recent study revealed that 94% of banking firms can’t deliver on the “personalization promise.” It’s not difficult to see why. Consumer preferences have changed, with many now preferring digital interactions. This has made it difficult for financial institutions to engage with consumers on a personal level. Nevertheless, customers expect seamless, consistent, and personalized experiences – that’s where the power of advanced analytics comes into play. It’s no secret that using advanced analytics can enable businesses to turn rich data into insights that lead to confident business decisions and strategy development. But these business tools can actually help financial institutions deliver on that promise of personalization. According to an Experian study, 90% of organizations say that embracing advanced analytics is critical to their ability to provide an excellent customer experience. By using data and analytics to anticipate and respond to customer behavior, companies can develop new and creative ways to cater to their audiences – revolutionizing the customer experience as a whole. It All Starts With Data Data is the foundation for a successful digital transformation – the lack of clean and cohesive datasets can hinder the ability to implement advanced analytic capabilities. However,  89% of organizations face challenges on how to effectively manage and consolidate their data, according to Experian’s Global Data Management Research Benchmark Report of 2019. Because consumers prefer digital interactions, companies have been able to gather a vast amount of customer data. Technology that uses advanced analytic capabilities (like machine learning and artificial intelligence) are capable of uncovering patterns in this data that may not otherwise be apparent, therefore opening doors to new avenues for companies to generate revenue. To start, companies need a strategy to access all customer data from all channels in a cohesive ecosystem – including data from their own data warehouses and a variety of different data sources. Depending on their needs, the data elements can come from a third party data provider such as: a credit bureau, alternative data, marketing data, data gathered during each customer contact, survey data and more. Once compiled, companies can achieve a more holistic and single view of their customer. With this single view, companies will be able to deliver more relevant and tailored experiences that are in-line with rising customer expectations. From Personalized Experiences to Predicting the Future The most progressive financial institutions have found that using analytics and machine learning to conquer the wide variety of customer data has made it easier to master the customer experience. With advanced analytics, these companies gain deeper insights into their customers and deliver highly relevant and beneficial offers based on the holistic views of their customers. When data is provided, technology with advanced analytic capabilities can transform this information into intelligent outputs, allowing companies to optimize and automate business processes with the customer in mind. Data, analytics and automation are the keys to delivering better customer experiences. Analytics is the process of converting data into actionable information so firms can understand their customers and take decisive action. By leveraging this business intelligence, companies can quickly adapt to consumer demand. Predictive models and forecasts, increasingly powered by machine learning, help lenders and other businesses understand risks and predict future trends and consumer responses. Prescriptive analytics help offer the right products to the right customer at the right time and price. By mastering all of these, businesses can be wherever their customers are. The Experian Advantage With insights into over 270 million customers and a wealth of traditional credit and alternative data, we’re able to drive prescriptive solutions to solve your most complex market and portfolio problems across the customer lifecycle – while reinventing and maintaining an excellent customer experience. If your company is ready for an advanced analytical transformation, Experian can help get you there. Learn More

Published: December 3, 2019 by Kelly Nguyen

The fact that the last recession started right as smartphones were introduced to the world gives some perspective into how technology has changed over the past decade. Organizations need to leverage the same technological advancements, such as artificial intelligence and machine learning, to improve their collections strategies. These advanced analytics platforms and technologies can be used to gauge customer preferences, as well as automate the collections process. When faced with higher volumes of delinquent loans, some organizations rapidly hire inexperienced staff. With new analytical advancements, organizations can reduce overhead and maintain compliance through the collections process. Additionally, advanced analytics and technology can help manage customers throughout the customer life cycle. Let’s explore further: Why use advanced analytics in collections? Collections strategies demand diverse approaches, which is where analytics-based strategies and collections models come into play. As each customer and situation differs, machine learning techniques and constraint-based optimization can open doors for your organization. By rethinking collections outreach beyond static classifications (such as the stage of account delinquency) and instead prioritizing accounts most likely to respond to each collections treatment, you can create an improved collections experience. How does collections analytics empower your customers? Customer engagement, carefully considered, perhaps comprises the most critical aspect of a collections program—especially given historical perceptions of the collections process. Experian recently analyzed the impact of traditional collections methods and found that three percent of card portfolios closed their accounts after paying their balances in full. And 75 percent of those closures occurred shortly after the account became current. Under traditional methods, a bank may collect outstanding debt but will probably miss out on long-term customer loyalty and future revenue opportunities. Only effective technology, modeling and analytics can move us from a linear collections approach towards a more customer-focused treatment while controlling costs and meeting other business objectives. Advanced analytics and machine learning represent the most important advances in collections. Furthermore, powerful digital innovations such as better criteria for customer segmentation and more effective contact strategies can transform collections operations, while improving performance and raising customer service standards at a lower cost. Empowering consumers in a digital, safe and consumer-centric environment affects the complete collections agenda—beginning with prevention and management of bad debt and extending through internal and external account resolution. When should I get started? It’s never too early to assess and modernize technology within collections—as well as customer engagement strategies—to produce an efficient, innovative game plan. Smarter decisions lead to higher recovery rates, automation and self-service tools reduce costs and a more comprehensive customer view enhances relationships. An investment today can minimize the negative impacts of the delinquency challenges posed by a potential recession. Collections transformation has already begun, with organizations assembling data and developing algorithms to improve their existing collections processes. In advance of the next recession, two options present themselves: to scramble in a reactive manner or approach collections proactively. Which do you choose? Get started

Published: August 13, 2019 by Laura Burrows

What if you had an opportunity to boost your credit score with a snap of your fingers? With the announcement of Experian BoostTM, this will soon be the new reality. As part of an increasingly customizable and instant consumer reality in the marketplace, Experian is innovating in the space of credit to allow consumers to contribute information to their credit profiles via access to their online bank accounts. For decades, Experian has been a leader in educating consumers on credit: what goes into a credit score, how to raise it and how to maintain it. Now, as part of our mission to be the consumer’s bureau, Experian is ushering in a new age of consumer empowerment with Boost. Through an already established and full-fledged suite of consumer products, Experian Boost is the next generation offering a free online platform that places the control in the consumers’ hands to influence their credit scores. The platform will feature a sign-in verification, during which consumers grant read-only permission for Experian Boost to connect to their online bank accounts to identify utility and telecommunications payments. After they verify their data and confirm that they want the account information added to their credit file, consumers will receive an instant updated FICO® Score. The history behind credit information spans several centuries from a group of London tailors swapping information on customers to keeping credit files on index cards being read out to subscribers over the telephone. Even with the evolution of the credit industry being very much in the digital age today, Experian Boost is a significant step forward for a credit bureau. This new capability educates the consumer on what types of payment behavior impacts their credit score while also empowering them to add information to change it. This is a big win-win for consumers and lenders alike. As Experian is taking the next big step as a traditional credit bureau, adding these data sources is a new and innovative way to help consumers gain access to the quality credit they deserve as well as promoting fair and responsible lending to the industry. Early analysis of Experian’s Boost impact on the U.S. consumer credit scores showed promising results. Here’s a snapshot of some of those findings: These statistics provide an encouraging vision into the future for all consumers, especially for those who have a limited credit history. The benefit to lenders in adding these new data points will be a more complete view on the consumer to make more informed lending decisions. Only positive payment histories will be collected through the platform and consumers can elect to remove the new data at any time. Experian Boost will be available to all credit active adults in early 2019, but consumers can visit www.experian.com/boost now to register for early access. By signing up for a free Experian membership, consumers will receive a free credit report immediately, and will be one of the first to experience the new platform. Experian Boost will apply to most leading consumer credit scores used by lenders. To learn more about the platform visit www.experian.com/boost.

Published: December 19, 2018 by Guest Contributor

Experian estimates card-to-card consumer balance transfer activity to be between $35 and $40 billion a year, representing a sizeable opportunity for proactive lenders seeking to grow their revolving product line. This opportunity, however, is a threat for reactive lenders that only measure portfolio attrition instead of working to retain current customers. While billions of dollars are transferred every year, this activity represents only a small percentage of the total card population. And given the expense of direct marketing, lenders seeking to capitalize on and protect their portfolio from balance transfer activity must leverage data insights to make more informed decisions. Predicting a consumer’s future propensity to engage in card-to-card balance transfers starts with trended data. A credit score is a snapshot in time, but doesn’t reveal deep insights about a consumer’s past balance transfer activity. Lenders that rely only on current utilization will group large populations of balance revolvers into one bucket – and many of these individuals will have no intention of transferring to another product in the near future. Still, balance transfer activity can be identified and predicted by utilizing trended data. By analyzing the spend and payment data over time to see when one (or multiple) trade’s payment approximately matches another trade’s spend, we have the logic that suggests there has been a card-to-card transfer. What most people don’t realize is that trended data is difficult to work with. With 24 months of history on five fields, a single trade includes 120 data points. That’s 720 data points for a consumer with six trades on file and 72,000,000 for a file with 100,000 records, not to mention the other data fields in the file. It’s easy to see why even the most sophisticated organizations become paralyzed working with trended data. While teams of analysts get buried in the data, projects drag, costs swell, and eventually the world changes as rates climb and fall. By the time the analysis is complete, it must be recalibrated. But there is a solution. Experian has developed powerful predictions tools that combine past balance transfer history, historical transfer amounts, current trades carried and utilized, payments, and spend. Combined, these data fields can help identify consumers who are most likely to transfer a balance in the future. With Experian’s Balance Transfer Index the highest scoring 10 percent of consumers capture nearly 70 percent of total balance transfer dollars. Imagine the impact on ROI of reducing 90 percent of the marketing cost of your next balance transfer campaign and still reaching 70 percent of the balance transfer activity. Balance transfer activity represents a meaningful dollar opportunity for growth, but is concentrated in a small percentage of the population making predictive analytics key to success. Trended data is essential for identifying those opportunities, but financial institutions must assess their capabilities when it comes to managing the massive data attached. The good news is that regardless of financial institution size, solutions now exist to capture the analytics and provide meaningful and actionable insights to lenders of all sizes.

Published: August 1, 2016 by Kyle Matthies

 All customers are not created equally – at least when it comes to one’s ability to pay. Incomes differ, financial circumstances vary and economic challenges surface. Lost job. Totaled car. Unplanned medical bills. Life happens. Research conducted by a recent Bankrate study revealed  just 38 percent of Americans said they could cover an unexpected emergency room visit or a $500 car repair with available cash in a checking or savings account. It’s a scary situation for individuals, and also a source of stress for the lender expecting payment. So what are the natural moments for a lender to assess “ability to pay?” Moment No. 1: When prepping for a prescreen campaign and at origination. Many lenders leverage an income estimation model, designed to give an indication of the customer’s capacity to take on additional debt by providing an estimation of their annual income. Within the model, multiple attributes are used to calculate the income, including: Number of accounts Account balances Utilization Average number of months since trade opened Combined, all of these insights determine a customer’s current obligations, as well as an estimation of their current income, to see if they can realistically take on more credit. The right models and criteria on the front-end – whether used when a consumer applies for new credit or when a lender is executing a prescreen campaign to acquire new customers – minimizes the risk for default. It’s a no-brainer. Moment No. 2: When a customer is already on your books. As the Bankrate study mentioned, sudden life events can send some customers’ lives into a financial tailspin. On the other hand, financial circumstances can change for the better too. Aggressively paying down a HELOC, doubling down on a mortgage, or wiping out a bankcard balance could signal an opportunity to extend more credit, while the reverse could be the first signs of payment stress. Attaching triggers to accounts can give lenders indications on what to do with either scenario, helping to grow a portfolio and protect it. Moment No. 3: When an account goes south. While a lender hates to think any of its accounts will plummet into collections, sometimes, it’s inevitable. Even prime customers fall behind, and suddenly financial institutions are faced with looking at collections strategies. Where should they place their bets? You can’t treat all delinquent customer equally and work the accounts the same way. Collection resources can be wasted on customers who are difficult or impossible to recover, so it’s best to turn to predictive analytics and a collections scoring strategy to prioritize efforts. Again, who has the greatest ability to pay? Then place your manpower on those individuals where you can recover the most dollars. --- Assessing one’s ability to pay is a cornerstone to the financial services business. The quest is to find the sweet spot with a combination of application data, behavioral data and credit risk scoring analytics.  

Published: July 12, 2016 by Kerry Rivera

Every portfolio has a set of delinquent customers who do not make their payments on time. Truth. Every lender wants to collect on those payments. Truth. But will you really ever be able to recover all of those delinquent funds? Sadly, no. Still, financial institutions often treat all delinquent customers equally, working the account the same and assuming eventually they’ll get their funds. The sentiment to recover is good, but a lot of collection resources are wasted on customers who are difficult or impossible to recover. The good news? There is a better way. Predictive analytics can help optimize the allocation of collection resources by identifying the most effective accounts to prioritize to your best collectors, do not contact and proceed to legal actions to significantly increase the recovery of dollars, and at the same time reduce collection costs. I had the opportunity to recently present at the annual Debt Buyer Association’s International Conference and chat with my peers about this very topic. We asked the room, “How many of you are using scoring to determine how to work your collection accounts?” The response was 50/50, revealing many of these well-intentioned collectors are working themselves too hard, and likely not getting the desired returns. Before you dive into your collections work, you need to respond to two questions: Which accounts am I going to work first? How am I going to work those accounts? This is where scoring enters the scene. A scoring model is a statistical algorithm that assigns a numerical expression based on known information to predict an unknown future outcome. You can then use segmentation to group individuals with others that show the same behavior characteristics and rank order groups for collection strategies. In short, you allow the score to dictate the collection efforts and slope your expenses based on the propensity and expected amount of the consumer to pay. This will inform you on: What type, if any, skip trace tactic you should use? If you should purchase additional data? What intensity you should work the account? With scoring, you will see different performances on different debts. If you have 100 accounts you are collecting on, you’ll then want to find the accounts where you will have the greatest likelihood to collect, and collect the most dollars. I like to say, “You can’t get blood from a stone.” Well the same holds true for certain accounts in your collections pile. Try all you like, but you’ll never recoup those dollars, or the dollars you do recoup will be minimal. With a scoring strategy, you can establish your “hit list” and find the most attractive accounts to collect on, and also match your most profitable accounts with your best collectors. My message to anyone managing a collections portfolio can be summed up in three key messages. You need to use scoring in your business to optimize resources and increase profits. The better data that goes into your model will net you better performance results. Get a compliance infrastructure in place so you can ensure you are collecting the right way and stay out of trouble. The beauty of scores is they tell you what to do. It will help you best match resources to the most profitable accounts, and work smarter, not harder. That’s the power of scoring.

Published: February 22, 2016 by Paul Desaulniers

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