Tag: Advanced analytics

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 Building AI models on fixed data has already become an outdated approach.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:

*Stats from Experian Global Insights Research Read related content The evolution of data: Unlocking the potential of data to transform our world Be more open: Results of the 2021 Open Banking survey - Experian Academy Full text: The future of consumer lending in a digital economy With the advanced technologies available today, businesses can access relevant data and deliver on customer expectations in their moment of need. As more people go online and use digital channels, your business must do more to create a seamless and secure experience. Online activity has increased by 25% globally Online retail sales saw 4 years of growth in 12 months Now online, consumers have high expectations for digital experience without sacrificing security, convenience, and privacy. 64% of consumers have abandoned an online transaction in the past 12 months Consumers, regardless of age, now prefer online banking and payments over in-person transactions The future of credit and fraud risk management is integrating data and technology seamlessly to put the customer at the centre of it all. 74% of businesses are adopting AI (2021), up from 69% the year before Businesses can embrace customer-centricity at scale through: Behavioural biometrics within a layered strategy of defence to make it easier to tackle fraud and maintain a seamless customer journey Open source data so businesses of all sizes can build a view of potential customers, minimise credit risk, and bring more people into mainstream financial services Advanced analytics, AI, and machine learning for real-time underwriting, fraud detection and a truly personalised service “The market is now driven by consumer demand for digital services. Those companies that are able to tailor the digital customer journey – so it reflects the best-in-class consumer experience – are the ones that will win.” – Steve Wagner, Managing Director of Global Decision Analytics

Did you miss these December 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. How are companies responding to consumer behavior? Nasdaq Trade Talk's Jill Maladrino talks to Steve Wagner, Global Managing Director of Decision Analytics, about the increase in online activity over the course of the pandemic, how inflation can impact brand loyalty, and why businesses need to respond to consumer demand with better customer experience and fraud prevention. Q&A: Why the increased use of digital transactions is here to stay David Bernard, SVP of Strategy, Marketing and Digital, talks to Digital Journal about how businesses should be approaching the increase in digital transactions using advanced analytics and decisioning technologies to improve the digital customer experience and grow their businesses. How criminals are using synthetic identities for fraud Dark Reading's The Edge talks to David Britton, VP of Industry Solutions, about why businesses must improve their fraud detection and prevention protocols to detect synthetic identities and ensure that they are protecting their consumers' personal information. Latest retail trends: AI is on the up, consumer loyalty is heading down Digital Journal looks at Experian's latest research that uncovers how businesses are incorporating machine learning and artificial intelligence into everyday operations and investments in response to an upward trend in online activity and a downward trend in customer loyalty. Stay in the know with our latest research and insights:

What increasing expectations of the digital customer experience mean for your business and technology investment Economic recovery and waning customer loyalty are creating new opportunities 59% of businesses globally say they’re mostly or completely recovered from the pandemic 61% of customers engaging with the same companies they did a year ago, down 6% in twelve months Data, analytics and decisioning technologies help provide customers with a secure and convenient digital experience Consumers are prioritising security, privacy and convenience when engaging online 75% of consumers feel the most secure using physical biometrics Scalable software solutions give companies of all sizes the ability to better manage risk and digitally transform the customer experience 50% of businesses are exploring new data sources 7 in 10 businesses say they’re frequently discussing the use of advanced analytics and AI, to better determine consumer credit risk and collections 76% of businesses are improving or rebuilding their analytics models “Dwindling customer loyalty along with heightened customer expectations and increased competition could mean potential revenue loss or gain. Businesses must find integrated credit and fraud solutions to improve digital engagement and customer acquisition.” Steve Wagner, Global Managing Director, Decision Analytics, Experian We surveyed 12,000 consumers and 3,600 businesses across 10 countries as part of a longitudinal study that started in June 2020 Read the full report to find out where businesses are focusing their investments

In this eSpeak podcast, eWeek’s James Maguire talks to Donna DePasquale, EVP of Global Decisioning Software, about the use of technology in financial services, and how it can satisfy the ever-increasing demand for real-time intelligence. Listen to the podcast to hear Donna DePasquale discussing: Data and decisioning challenges involved with helping financial institutions reduce risk Helping lenders make better decisions about their customers by providing simplified and streamlined services. Consumers have more choice than they’ve ever had before when it comes to credit, this, along with high expectations for their online experience, is driving businesses to invest in digital transformation and automation solutions. Growing diversity among populations in terms of spending means financial services are working to provide more personalised, real-time, meaningful experiences. Consumers want secure and convenient experiences online without compromise. Evolution of data technology Businesses can now deploy new types of analytics and new types of data services in order to serve customers. Digital transformation allows automation and insights to work together improving credit risk analysis and assessment, smoothing out the customer journey throughout the lifecycle. Access to new data types and advanced analytics. AI and analytics is not a static process, it’s a dynamic process. AI and machine learning allow for constant updates and enhancements to strategy. Future of data analytics and the credit markets Financial inclusion is a very important to the future of data analytics, especially when thinking about those growing economies around the world. We believe that all consumers deserve fair and affordable access to credit, and using alternative data sources to improve credit profiles will directly impact this. Customer experience and credit risk analysis should coexist seamlessly – asking clients to do less without sacrificing the security, convenience, relevance, and privacy of consumer experiences. Stay in the know with our latest research and insights:

It’s no secret that the pandemic created a level of economic uncertainty that makes it incredibly tricky for lenders to understand their risk on a customer-by-customer basis, and therefore its impact on decision management. It’s no wonder they’re uncertain; the customers themselves are just as unsure. According to the Global Decisioning Report 2021, one out of every three consumers worldwide are still concerned about their finances even as the second anniversary of the COVID-19 outbreak approaches. While some consumers were able to easily work from home during the pandemic, others suffered job losses, cut wages, or increased expenses due to lost childcare or having to care for a loved one. As the impact of the pandemic continues to be felt – especially as government support programs begin to conclude – financial institutions will have to figure out how to navigate the uneven recovery. By leveraging advanced data and analytics, financial institutions can better understand their risk and improve their decision management. In turn, many financial institutions are creating predictive models to target their best customers and reduce exposure to unnecessary risk. However, a model is not always the end-all, be-all solution for reducing risk. Here’s why: a model requires of the right data in order to work effectively. If there isn't a data sample over a long enough time frame, the risk of creating blind spots that can leave businesses on the hook for unexpected losses can be high. Also, there will always be the need for a strategy even with a custom model. A global financial institution likely has more than enough data to create accurate, powerful custom models. However, financial institutions like local or regional credit unions or fintechs simply don't have enough customer data points to power a model. In addition, many outsourced model developers lack the specific financial industry domain expertise required to tweak their models in a way that accounts for the nuances of regulations and credit data. Finally, the pandemic continues to change the economic picture for customers by the minute, which can make a model designed for today outdated tomorrow. When a strategy makes more sense For many financial institutions, it can make more sense to focus on building out a decision management strategy instead of leveraging custom models. While a model can provide a score, it can’t tell you what to do with it. By focusing on a decision management strategy, you can leverage other information and attributes about different customer segments to inform actions and decisions. In an ideal world, of course, the choice wouldn't exist between a model and a strategy. Each has an important role to play, and each makes the work of the other more effective. However, strategy is often the smart place to start when beginning an analytics journey. The benefits of starting with strategy include: Adaptability: A strategy is much easier to change than a model. While models often have rigorous governance standards, a strategy can be adapted with relatively little compliance impact. This helps businesses adapt to changes in goals, vision, or shifts in the marketplace in a bid to attract the ideal customer. In a world that changes by the day, the ability to adjust risk tolerance on the fly is crucial. Speed: A custom model can take weeks or even months to build, test, deploy, and optimize. As a result, this can put businesses behind in analytics transformation while leaving them unnecessarily exposed to risk. On the other hand, a strategy can be developed and deployed in a relatively rapid manner, and then adapted on an ongoing basis to reflect the realities on the ground. Consistency: A strategy helps drive improvement across operations by allowing team members to ‘sing from the same songbook,’. In smaller organizations where work is still done manually by a handful of people, a strategy allows for automated processes like underwriting so businesses can scale decisioning. Strategy or model? Three questions to consider Do you need a strategy or a model? Again, in an ideal world the answer is ‘both’ due to the unique role each plays, but in the real world it depends on the institution. Here are three questions to ask in order to determine where to focus time and resources: “How different are the people I am lending to than the national average?” If the institution is lending to segments that look just like everyone else, leveraging existing third-party data sources will allow the use of generic models. In this case, the focus would be on using those generic models to power the strategy. However, for businesses that serve a niche population, a national average might skew results; in this case, it may make more sense to build a custom model. “What is my sample size?” Take a close look at the number of applications coming in each month, quarter, or year. In addition, compare it to periods dating back years to understand growth rates. This will indicate the if the data inflow required exists to power a custom model. Don’t forget to analyze how many of those applications eventually become delinquent; because some smaller financial institutions have conservative policies, they may have low delinquency rates. While this is good for the institution’s bottom line, it can make it difficult to build a model that will be able to detect future delinquencies. Therefore, even a large application sample size might not have enough variance to create an accurate custom model. “What are my long-term future goals?” This is the most difficult question to sometimes answer, as many financial institutions remain focused on navigating today’s challenges. As market conditions change, goals naturally adapt. That said, some goals might require custom models in order to effectively achieve the business vision. For example, if the plan is to enter new markets, create new partnerships, or offer new products that are different than what has been done in the past, a custom model could provide a more accurate understanding of potential risk. Our research also shows that nearly half of businesses report that they are dedicating resources to enhancing their analytics, with one-third of businesses planning on rebuilding their models from scratch. Rapid changes in consumer needs and desires means there’s less confidence in consumer risk management analytics models that are based on yesterday’s customer understanding. By focusing on a decisioning strategy, businesses can be empowered to effectively leverage analytics today to take action while creating a steppingstone for more sophisticated model-based analytics tomorrow. Stay in the know with our latest research and insights:

Why digital acceleration has created more opportunities for deepfake fraud tactics like voice cloning and what businesses can do about it Digital acceleration has placed information and services in the hands of the masses, connecting individuals on a global level like never-before, and in turn making them increasingly dependent on devices in their daily lives. The argument for technology as an equalizer in society is a strong one. Most people have a voice and a platform, producing millions of virtual interactions and recordings every day. But in this digital world of relative anonymity, it is difficult to know who is really on the other side of the connection. This uncertainty gives fraudsters an opening to threaten both businesses and consumers directly, especially in the realm of deepfakes. What is a deepfake? Deepfakes are artificially created images, video and audio designed to emulate real human characteristics. Deepfakes use a form of artificial intelligence (AI) called deep learning. A deep learning algorithm can teach itself how to solve problems using large sets of data, swapping out voices and faces where they appear in audio and video. This technology can deliver extraordinary outcomes across accessibility, criminal forensics, and entertainment, but it also allows a way in for cybercriminals that hasn’t existed until now. Deepfake fraud tactics A principal tactic among deepfake fraud is voice cloning – the practice of taking sample snippets of recorded speech from a person and then leveraging AI to understand speech patterns from those samples. Based on those learnings, the modeler can then use AI to apply the cloned voice to new contexts, generating speech that was never spoken by the actual voice owner. For businesses, deepfake tactics such as voice cloning means access to points of vulnerability in authentication processes that can put organizations at risk. Fraudsters may successfully bypass biometric systems to access areas that would otherwise be restricted. For government leaders, it can mean the proliferation of misinformation – a growing area of concern with huge repercussions. For consumers, the risk of falling victim to scams involving access to personal information or funds is particularly high when it comes to voice cloning. How to prevent deepfake fraud 1. Vigilance: Stay on top of sensitive personal information that could be targeted. Fraudsters are always at work, relentlessly seeking out opportunities to take advantage of any loophole or weak spot. Pay close attention to suspicious voice messages or calls that may sound like someone familiar yet feel slightly off. In an era of remote work, it is important to question interactions that can impact business vulnerabilities – could it be a phishing or complex social engineering scam? 2. Machine learning and advanced analytics: Deepfake fraud is an emerging threat, which leverages the development and evolution of the technology that fuels it. The flip side is that businesses can in fact use the same technology against the fraudsters, fighting fire with fire by deploying deepfake detection and analysis. 3. Layered fraud prevention strategy: Leveraging machine learning and advanced analytics to fight deepfake fraud can only be effective within a layered strategy of defense, and most importantly, at the first line of defense. Ensuring that the only people accessing the points of vulnerability are genuine means using identification checks such as verification, device ID and intelligence, behavioral analytics, and document verification simultaneously to counter how fraudsters may deploy or distribute deepfakes within the ecosystem. As with many types of fraud, staying one step ahead of the fraudsters is critical. The technology and the tactics continually evolve, which may make the countermeasures on the table right now obsolete, however the fundamentals of sound risk management, with the right layered approach, and a flexible and dynamic solution set, can mitigate these emerging threats.   Stay in the know with our latest research and insights:

How elite leaders train analytics teams to unearth and convey the highest quality data insights and better manage risk. It's surprising how much of an art the effective use and analysis of qualitative data in the business world truly is. Too often, data scientists are tasked with turning raw data into insights without ever actually being taught the true art of identifying and reporting the most meaningful insights that address the problems at hand. Instead, data teams often produce reams of summarized information without drawing any useful conclusions – falling short of discovering deeper truths hidden within. I've been fortunate to work for, with, and manage data scientists of various titles, abilities, and personalities over the years. I've found that the true "artists" in this profession can combine technical proficiency, tactical communications with an affinity for the science, and excellent detective skills. Objectivity in Data Analysis As Arthur Conan Doyle wrote in Sherlock Holmes says, "I never guess. It is a capital mistake to theorize before one has correct data. Insensibly one begins to twist facts to suit theories, instead of theories to suit facts." As data scientists, we're often sent down a singular path to analyze data to support a narrative. Data is inherently objective; analyzing with subjective intent typically leads to ineffective results when put into practice. However, with the proper guidance, probing questions, and some detective work, scientists can uncover deeper insights leading to effective outcomes in the form of actionable intelligence and forecasts. Early in my career, I was tasked by a business partner to pull data that demonstrated higher customer satisfaction scores for a customer call center. Requests like this – "just get me the data" – are (unfortunately) common. In this case, however, he was open to discussing the "why" behind his ask. As a result, this incident proved a learning opportunity for me on how to satisfy a requirement while simultaneously producing information explicitly valuable to the organization. I've often had to find workable paths through figurative minefields with mandates such as "just get me the data" or "make the numbers work." During this scenario, I diligently asked ancillary questions to build into the data modeling outside the required parameters. I intended to generate value beyond the pre-conceived conclusion I was tasked with finding data for. The resulting report yielded compelling insights, actionable intelligence, and a clear forecasting plan. In this example, it was found that clients had higher satisfaction scores for reasons other than what we initially thought and had nothing to do with the seven million dollars my business partner spent on branding, training, etc. The solution was simple: move a training location. Tactical communication skills were necessary in this scenario as I had to tell my business partner where the efficiency gains were actually coming from and where future budgets could be more effective. Doing so was the catalyst behind an alternative business strategy and focus, resulting in a much more significant impact on our customer relationships. Asking the Right Questions The true purpose of analytics is to discover, interpret, and communicate meaningful patterns in data and the connective tissue between. Most importantly, it exists to aid in effective decision-making within an organization. Under that premise, I teach my teams to be communicative, especially during planning stages and consistently ask questions of the data throughout the analytical process. It's always imperative to identify the specific addressable problems our clients are trying to solve while frequently conversing with them to understand what actions and/or decisions the analysis is meant to inform. This strategy produces more profound results and focuses on solving a problem – not endlessly cycling through various cuts of the same data. As a result, the team will be primed to evaluate results objectively and be ready to dig beyond surface-level data, capturing vital insights hidden deep within. Using the Right Tools Nobody does arithmetic by hand anymore. A data scientist's best friend should be sophisticated model development software that leverages AI and Machine Learning. The efficiency they provide enables us to focus on areas where human intelligence is best applied, such as interpreting model performance within the context of how that model will be used. Elite leaders know how to leverage the right tools to maximize speed and efficiency. Ignoring the sheer processing power of cloud computing and other advancements places your organization at a distinct competitive disadvantage in performance and accuracy. I shudder when thinking about the dark days when it would take six to nine months to develop a new model. It reminds me of watching NASA mathematicians do advance calculations with slide rules in movies like Apollo 13 and Hidden Figures. Strategy optimization is a perfect example; how do I ensure that my portfolio is holistically delivering the highest value within risk constraints? I could grow my portfolio endlessly, but that likely means taking on too much back-end risk. Instead, mathematical optimization can be used to determine the right balance between growth, return, and risk. To do this successfully requires a vast amount of processing power. Gradient boosting, a Machine Learning technique that helps build far more accurate models, is another excellent example of what's possible with modern technology. Some of the operations we perform daily were literally not possible 10-15 years ago as we did not have access to such processing power. Thus, we're able to solve problems not previously solvable. What has also changed is our ability to process volumes of data and highly complicated, multi-tiered models, with extreme speed and efficiency. Organizations don't need to take all of this on, as companies like Experian effectively provide data science services where AI/ML solutions are delivered rapidly and digitally. A well-equipped, efficient, curious, and well-trained data team whose data analysis consistently helps corporate leaders make informed decisions is true art. The answers they provide to challenging business questions is their magnum opus. Read about topics related to this article Stay in the know with our latest research and insights:

The pandemic has impacted everyone differently. With consumers emerging from the crisis with very different credit needs, we take a look at how lenders can navigate an uneven economic recovery when it comes to credit risk decisioning.  Download the full Global Decisioning Report: Navigating a new era of credit risk decisioning.

Shri Santhanam, EVP and Global Head of Analytics and AI, talks to Ganesh Padmanabhan from Stories in AI about why he hopes the changing world of lending will lead to better financial inclusion. "The whole digital revolution in lending means that financial institutions are scrambling to make the process much more seamless, reduce time for approvals, let consumers have access to different financial products, and have innovative products like buy now pay later. But underneath it all, you have to get more nuanced and more sophisticated about the methodologies that you use for lending. And this is where AI and ML come in." Expect to hear discussions about the future of finance, how to drive impact by leveraging data analytics and AI, frameworks for setting up and institutionalizing an AI center of competence for a large organization, and how to scale data science efforts through hiring, promoting from within, and setting up the right structure and processes to make it happen. "Experian for over 100 years now has leveraged the power of data. We’ve been a very powerful data company. We’ve used that data to improve the lives of consumers and improve how businesses make decisions. Fundamentally, we’ve had a set of pioneers who before Big Data tech was introduced to the world, figured out that having a data marketplace or collecting high quality data on consumer lending will be of value, and that’s been the core of our business. That dynamic is changing. We see a lot of value migrating what we call up the stack. So from purely data to actually the decisions that are made with the data, to products and services in the data."   Related content

Credit providers have long relied on data to lend insights into how their customers are faring—and help predict what's to come. The pandemic, however, introduced unexpected anomalies that have made understanding the actual credit landscape far more challenging. For example, while government assistance programs have enabled customers to stay up-to-date on their payments, they've also made it harder to discern the true financial impacts of the crisis. Our recent research gives voice to these challenges. We surveyed businesses around the world three times from July 2020 through January 2021 for our annual Global Decisioning Report. The results reveal that business confidence in credit risk analytics models has declined over the pandemic, dropping by nine percentage points for Tier 1 lenders, and 15 percentage points for Tier 3 lenders. As we look ahead, credit providers need ways to improve confidence in their analytics models so that that they can make smarter, faster decisions on behalf of their customers and businesses. This is where synthetic and alternative data are beginning to make a real difference. The rise of AI and machine learning solutions has opened the door for lenders to leverage this data. Understanding how to put it to use—and why it's imperative to do so—will help lenders navigate the end of this crisis and prepare them for any economic volatility in the future. The data differentiator  Before we dive into how lenders can best utilize alternative and synthetic data, let's quickly define what we're discussing. Credit providers have traditionally used credit bureau data to assess their portfolio risk and inform credit decisions. But as noted, in times of crisis, supplementing that data with additional context can significantly improve its effectiveness. Alternative data does just that. Alternative data refers to primarily unstructured data from non-traditional sources. For example, social media data can help paint a more complete picture of customer behavior. And location data can provide information about customer geography, such as opportunities for travel-related purchases. Synthetic data complements alternative data but is not the same. Synthetic data is new data created by altering existing data. So a lender might change the profile of its customer base and then use that dataset in analytics models to better understand what the future may hold. Both types of data work together, with alternative data providing a more complete customer view and synthetic data allowing lenders to account for additional variables and offset their risk accordingly. New data in action  Confidence in analytics models may have dropped during the crisis. But lenders aren't resting on their laurels. Instead, nearly half of businesses report that they are dedicating resources to enhance their analytics efforts. Those that include alternative and synthetic data in their improved models have the opportunity to leverage the information in multiple ways. Some of the most exciting applications of alternative and synthetic data include: Anticipating purchasing behavior New data sources, especially from social media, help lenders understand what's happening in their customers' lives and how that may translate into purchases. For example, a customer who has recently moved into a new home may be considering purchasing furniture or home décor. Or customers who are celebrating life milestones such as birthdays or graduations may be buying gifts or spending on events. Predicting credit risk In this realm, synthetic data can be beneficial. Lenders can use synthetic data to understand how credit profiles may change in specific circumstances, such as modeling a higher unemployment rate or dramatic income shifts. They can then use analytics models to determine the related impact on customer affordability. Improving fraud detection With an improved customer view, fraud prevention teams can more easily identify unusual patterns in customer behavior and spending. For instance, does a customer's current location (per location data) match their most recent transactions? Or has the number of contacts on their phone dramatically changed (it may not be their phone)? Enhancing pricing Both types of data are useful in improving pricing models across company portfolios and at a personal level. The additional context can help everyone from lenders to insurers to banks assess customer needs and provide products that meet them—at prices that make sense. What's more, machine learning automates that pricing, allowing companies to scale personalization across the organization. Improving marketing In the same vein, new sources of data can also give marketing efforts a boost. The ability to access more real-time information about customer behaviors uncovers opportunities to provide them with credit, insurance, and other lending products that may prove immediately helpful. Data can also help identify new markets entirely or highlight rising needs that may demand the development of additional products or services. The past year was an anomaly in so many ways. However, as we ease out of the crisis, financial service companies have the opportunity to strengthen their data models—and leverage new types of data to reduce their risk and provide improved decisioning no matter what the future holds.

As we enter the beginning of the end of this global crisis, the role of data, analytics, and credit risk decisioning takes on even greater significance than before. Consumers face uneven roads to recovery, with some ready to spend again and others still mired in pandemic-related financial stress. And businesses of all sizes report their operations are recovering but there’s still a way to go. A key difference we saw is that companies that adapted to serve customer needs digitally are faring much better. Our 2021 Global Decisioning eBook, Navigating a new era of credit risk decisioning, looks at how consumers are stabilizing their finances and how businesses are returning to growth. A recent survey among 9,000 consumers and 2,700 businesses across ten countries worldwide reveals the importance of lenders prioritizing digital transformation, and the role of advanced data and analytics in enhancing the customer experience. The pandemic fall-out is impacting everyone differently: 1 in 3 consumers remains concerned about their finances – paying bills and managing credit Whereas high-income households are no longer reducing their discretionary spending Navigating this varied credit landscape requires a deep understanding of customer needs on both ends of the spectrum. However, business confidence in the consumer credit risk management analytics models dropped over the past year from 71 percent to 61 percent. Smaller lenders with revenues ranging from $10M to $49M have seen the sharpest decline from 72 percent to 57 percent in the past six months. Adapting data and analytics to a rapidly changing customer base: Almost 50% of businesses surveyed said their dedicate more resources to enhance analytics One-third of businesses are planning to re-build their models from scratch Recalibrating credit models is one thing, but lenders also need to rethink their data sources to better understand current customer profiles. The data inputs generated by the pandemic have impacted credit risk models and machine learning applications in unexpected ways. For example, widespread payment holidays and government stimulus programs may be masking customers’ true financial circumstances. According to Recovery Insights, a separate study published by Experian North America: Delinquency prior to the pandemic is a strong indicator of future risk. Accounts exiting an accommodation period are 2x more likely to become delinquent than are accounts that never received an accommodation. Payment on debt during accommodation indicated a reduced risk for subsequent delinquency. Amidst the pandemic lockdown, consumers turned online to manage finances and connect with lenders – including older consumers.  And while the pandemic pushed consumers online out of necessity, now that they’re there – it’s become a preference – as overall digital gains are holding above pre-pandemic levels. Lenders have a new digital imperative to meet consumers’ evolving needs for continued digital engagement. Consumer expectations of digital experiences 55% of consumers have higher expectations of their digital experience since Covid-19 began 43% of consumers surveyed age 70+ reported digital banking throughout the pandemic 14% of consumers surveyed age 60-69 applied for a new loan or card online The importance of a digital-first approach has revealed itself and many companies have put a digital customer journey in place since Covid-19 began. The future, however, is more than providing online services. It’s about knowing your customers well enough to anticipate their credit needs and using tools to automate the process and reduce risk. Adapt or lose customers 9 in 10 businesses have a digital customer journey in place 1 in 4 consumers have taken their business elsewhere because a company didn’t adapt to their digital needs Online customer experience and credit risk management are more connected than ever before. And, businesses need technology that supports the entire customer journey, from onboarding to customer management to collections. Five digital investments businesses are prioritizing the new era of credit risk management: Implement new machine learning models for customer decisions Increase digital acquisitions and engagement Understand their customer base (affordability, value, behavior) Automate customer decisions Increase value of existing customers Access the report here to get more consumer trends and find out what the future of decisioning means for businesses looking to return to growth. Stay in the know with our latest insights: